tugrul˜u.˜daim nima˜a. behkami orhun˜m.˜kök … · 2020. 5. 5. · nima˜a. behkami...

Innovation Technology and Knowledge Management

Tugrul U DaimNima A BehkamiNuri BasogluOrhun M KoumlkLiliya Hogaboam

Healthcare Technology Innovation AdoptionElectronic Health Records and Other Emerging Health Information Technology Innovations


Series Editor Elias G Carayannis George Washington University Washington DC USA

More information about this series at httpwwwspringercomseries8124

Tugrul U Daim bull Nima A Behkami Nuri Basoglu bull Orhun M Koumlk Liliya Hogaboam

Healthcare Technology Innovation Adoption Electronic Health Records and Other Emerging Health Information Technology Innovations

ISSN 2197-5698 ISSN 2197-5701 (electronic) Innovation Technology and Knowledge Management ISBN 978-3-319-17974-2 ISBN 978-3-319-17975-9 (eBook) DOI 101007978-3-319-17975-9

Library of Congress Control Number 2015942128

Springer Cham Heidelberg New York Dordrecht London copy Springer International Publishing Switzerland 2016 This work is subject to copyright All rights are reserved by the Publisher whether the whole or part of the material is concerned specifi cally the rights of translation reprinting reuse of illustrations recitation broadcasting reproduction on microfi lms or in any other physical way and transmission or information storage and retrieval electronic adaptation computer software or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names registered names trademarks service marks etc in this publication does not imply even in the absence of a specifi c statement that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty express or implied with respect to the material contained herein or for any errors or omissions that may have been made

Printed on acid-free paper

Springer International Publishing AG Switzerland is part of Springer Science+Business Media (wwwspringercom)

Tugrul U Daim Department of Engineering

and Technology Management Portland State University Portland OR USA

Nuri Basoglu Department of Industrial Design İzmir Institute of Technology Urla Izmir Turkey

Liliya Hogaboam Department of Engineering


Nima A Behkami Merck Research Laboratories Boston MA USA

Orhun M Koumlk Ernst and Young Advisory Istanbul Turkey

v

Series Foreword

The Springer book series Innovation Technology and Knowledge Management was launched in March 2008 as a forum and intellectual scholarly ldquopodiumrdquo for globallocal transdisciplinary transsectoral publicndashprivate and leadingldquobleedingrdquo edge ideas theories and perspectives on these topics

The book series is accompanied by the Springer Journal of the Knowledge Economy which was launched in 2009 with the same editorial leadership

The series showcases provocative views that diverge from the current ldquoconven-tional wisdomrdquo that are properly grounded in theory and practice and that consider the concepts of robust competitiveness 1 sustainable entrepreneurship 2 and demo-cratic capitalism 3 central to its philosophy and objectives More specifi cally the aim of this series is to highlight emerging research and practice at the dynamic intersection of these fi elds where individuals organizations industries regions and nations are harnessing creativity and invention to achieve and sustain growth

1 We defi ne sustainable entrepreneurship as the creation of viable profi table and scalable fi rms Such fi rms engender the formation of self-replicating and mutually enhancing innovation networks and knowledge clusters (innovation ecosystems) leading toward robust competitiveness (EG Carayannis International Journal of Innovation and Regional Development 1(3) 235ndash254 2009) 2 We understand robust competitiveness to be a state of economic being and becoming that avails systematic and defensible ldquounfair advantagesrdquo to the entities that are part of the economy Such competitiveness is built on mutually complementary and reinforcing low- medium- and high- technology and public and private sector entities (government agencies private fi rms universities and nongovernmental organizations) (EG Carayannis International Journal of Innovation and Regional Development 1(3) 235ndash254 2009) 3 The concepts of robust competitiveness and sustainable entrepreneurship are pillars of a regime that we call ldquo democratic capitalism rdquo (as opposed to ldquopopular or casino capitalismrdquo) in which real opportunities for education and economic prosperity are available to all especiallymdashbut not onlymdashyounger people These are the direct derivatives of a collection of topdown policies as well as bottom-up initiatives (including strong research and development policies and funding but going beyond these to include the development of innovation networks and knowledge clusters across regions and sectors) (EG Carayannis and A Kaloudis Japan Economic Currents p 6ndash10 January 2009)

vi

Books that are part of the series explore the impact of innovation at the ldquomacrordquo (economies markets) ldquomesordquo (industries fi rms) and ldquomicrordquo levels (teams indi-viduals) drawing from such related disciplines as fi nance organizational psychol-ogy research and development science policy information systems and strategy with the underlying theme that for innovation to be useful it must involve the shar-ing and application of knowledge

Some of the key anchoring concepts of the series are outlined in the fi gure below and the defi nitions that follow (all defi nitions are from EG Carayannis and DFJ Campbell International Journal of Technology Management 46 3ndash4 2009)

GlobalSystemicmacro level

Democraticcapitalism

Structural andorganizationalmeso level

Innovationnetworks

Entrepreneurialuniversity Globallocal

Individualmicro level

Local

Creativemilieus

Academicfirm

Democracyofknowledge

Mode 3 Quadruplehelix

Knowledgeclusters

Sustainableentrepreneurship

Entrepreneuremployeematrix

Conceptual profi le of the series Innovation Technology and Knowledge Management

bull The ldquoMode 3rdquo Systems Approach for Knowledge Creation Diffusion and Use ldquoMode 3rdquo is a multilateral multinodal multimodal and multilevel systems approach to the conceptualization design and management of real and virtual ldquoknowledge-stockrdquo and ldquoknowledge-fl owrdquo modalities that catalyze accelerate and support the creation diffusion sharing absorption and use of cospecialized knowledge assets ldquoMode 3rdquo is based on a system-theoretic perspective of socio-economic political technological and cultural trends and conditions that shape the coevolution of knowledge with the ldquoknowledge-based and knowledge-driven globallocal economy and societyrdquo

bull Quadruple Helix Quadruple helix in this context means to add to the triple helix of government university and industry a ldquofourth helixrdquo that we identify as the ldquomedia-based and culture-based publicrdquo This fourth helix associates with ldquomediardquo ldquocreative industriesrdquo ldquoculturerdquo ldquovaluesrdquo ldquolife stylesrdquo ldquoartrdquo and per-haps also the notion of the ldquocreative classrdquo

Series Foreword

vii

bull Innovation Networks Innovation networks are real and virtual infrastructures and infratechnologies that serve to nurture creativity trigger invention and cata-lyze innovation in a public andor private domain context (for instance govern-mentndashuniversityndashindustry publicndashprivate research and technology development coopetitive partnerships)

bull Knowledge Clusters Knowledge clusters are agglomerations of cospecialized mutually complementary and reinforcing knowledge assets in the form of ldquoknowledge stocksrdquo and ldquoknowledge fl owsrdquo that exhibit self-organizing learning- driven dynamically adaptive competences and trends in the context of an open systems perspective

bull Twenty-First Century Innovation Ecosystem A twenty-fi rst century innovation ecosystem is a multilevel multimodal multinodal and multiagent system of sys-tems The constituent systems consist of innovation metanetworks (networks of innovation networks and knowledge clusters) and knowledge metaclusters (clus-ters of innovation networks and knowledge clusters) as building blocks and orga-nized in a self-referential or chaotic fractal knowledge and innovation architecture 4 which in turn constitute agglomerations of human social intel-lectual and fi nancial capital stocks and fl ows as well as cultural and technologi-cal artifacts and modalities continually coevolving cospecializing and cooperating These innovation networks and knowledge clusters also form reform and dissolve within diverse institutional political technological and socioeconomic domains including government university industry and non-governmental organizations and involving information and communication tech-nologies biotechnologies advanced materials nanotechnologies and next-generation energy technologies

Who is this book series published for The book series addresses a diversity of audiences in different settings

1 Academic communities Academic communities worldwide represent a core group of readers This follows from the theoreticalconceptual interest of the book series to infl uence academic discourses in the fi elds of knowledge also carried by the claim of a certain saturation of academia with the current concepts and the postulate of a window of opportunity for new or at least additional con-cepts Thus it represents a key challenge for the series to exercise a certain impact on discourses in academia In principle all academic communities that are interested in knowledge (knowledge and innovation) could be tackled by the book series The interdisciplinary (transdisciplinary) nature of the book series underscores that the scope of the book series is not limited a priori to a specifi c basket of disciplines From a radical viewpoint one could create the hypothesis that there is no discipline where knowledge is of no importance

2 Decision makers mdash private academic entrepreneurs and public ( governmental subgovernmental ) actors Two different groups of decision makers are being addressed simultaneously (1) private entrepreneurs (fi rms commercial fi rms

4 EG Carayannis Strategic Management of Technological Learning CRC Press 2000

Series Foreword

viii

academic fi rms) and academic entrepreneurs (universities) interested in opti-mizing knowledge management and in developing heterogeneously composed knowledge-based research networks and (2) public (governmental subgovern-mental) actors that are interested in optimizing and further developing their poli-cies and policy strategies that target knowledge and innovation One purpose of public knowledge and innovation policy is to enhance the performance and com-petitiveness of advanced economies

3 Decision makers in general Decision makers are systematically being supplied with crucial information for how to optimize knowledge-referring and knowledge- enhancing decision-making The nature of this ldquocrucial informationrdquo is conceptual as well as empirical (case-study-based) Empirical information highlights practical examples and points toward practical solutions (perhaps remedies) conceptual information offers the advantage of further driving and further-carrying tools of understanding Different groups of addressed decision makers could be decision makers in private fi rms and multinational corporations responsible for the knowledge portfolio of companies knowledge and knowl-edge management consultants globalization experts focusing on the interna-tionalization of research and development science and technology and innovation experts in universitybusiness research networks and political scien-tists economists and business professionals

4 Interested global readership Finally the Springer book series addresses a whole global readership composed of members who are generally interested in knowl-edge and innovation The global readership could partially coincide with the communities as described above (ldquoacademic communitiesrdquo ldquodecision makersrdquo) but could also refer to other constituencies and groups

Elias G Carayannis

Series Foreword

ix

Pref ace

Healthcare costs have been increasing dramatically over the last years This volume explores the adoption of health technology innovations designed to streamline the service delivery and thus reduce costs and increase quality

The fi rst part reviews theories and applications for the diffusion of healthcare technology innovations The second and third parts focus on electronic health records (EHR) which is the leading technology innovation in the healthcare sector The second part develops evaluation models and the third part analyzes an adoption case These models and the case provide a set of factors which need further attention by those responsible for implementing such technologies

Portland OR USA Tugrul U Daim Boston MA USA Nima A Behkami Izmir Turkey Nuri Basoglu Istanbul Turkey Orhun M Koumlk Portland OR USA Liliya Hogaboam

xi

Part I A Dynamic Capabilities Theory-Based Innovation Diffusion Model for Spread of Health Information Technology in the USA Nima A Behkami and Tugrul U Daim

1 Introduction to the Adoption of Health Information Technologies 3 Nima A Behkami and Tugrul U Daim 11 The Healthcare Crisis in the United States 3 12 Government Efforts and HIT Meaningful-Use Initiative 4

121 State of Diffusion Research General and Health IT 5 References 7

2 Background Literature on the Adoption of Health Information Technologies 9 Nima A Behkami and Tugrul U Daim 21 Overview of the Healthcare Delivery System 9 22 A Methodological Note 10 23 The Critical Stakeholders and Actors 10

231 Care Providers 11 232 Government 12 233 Patients and Their Family and Care Givers 13 234 Payers 13 235 HITInnovation Suppliers 14

24 Attributes of the Stakeholders 15 25 Important Factors Effecting Diffusion and Adoption for HIT 15

251 Barriers and Infl uences 17 252 Tools Methods and Theories 19 253 Policy Making 20 254 Hospital Characteristics and the Ecosystem 21 255 Adopter Attitudes Perceptions and Characteristics 22 256 Strategic Management and Competitive Advantage 23

Contents

xii

257 Innovation Champions and Their Aids 23 258 Workfl ow and Knowledge Management 24 259 Timing and Sustainability 24 2510 Modeling and Forecasting 25 2511 Infusion 25 2512 Social Structure and Communication

Channels 25 26 The Need for Multiple Perspectives in Research 26 27 Linstonersquos Multiple Perspectives Method 26 28 The ldquo4 + 1 Viewrdquo Model for Software Architectures 28 29 Categorization of Important Factors in HIT Adoption

Using Multi-perspectives 28 References 30

3 Methods and Models 37 Nima A Behkami and Tugrul U Daim 31 Proposed Model Overview and Justifi cation 37 32 Modeling Approach 39 33 Diffusion Theory 40

331 An Innovation 41 332 Recent Diffusion of Innovation Issues 42 333 Limitations of Innovation Research 44

34 Other Relevant Diffusion and Adoption Theories 45 341 The Theory of Reasoned Action 46 342 The Technology Acceptance Model 46 343 The Theory of Planned Behavior 48 344 The Unifi ed Theory of Acceptance

and Use of Technology 48 345 Matching Person and Technology Model 49 346 Technology-Organization-Environment

Framework (TOE) 49 347 Lazy User Model 50

35 Resource-Based Theory Invisible Assets Competencies and Capabilities 50 351 Foundations of Resource-Based Theory 51 352 Seminal Work in Resource-Based Theory 52 353 Invisible Assets and Competencies Parallel Streams

of ldquoResource-Based Workrdquo 53 354 A Complete List of Terms Used to Refer to Factors

of Production in Literature 54 355 Typology and Classifi cation of Factors of Production 55

36 Modeling Component Descriptions 55 361 Model 56 362 Diagram 56 363 View 56

Contents

xiii

364 Domain 56 365 Modeling Language 56 366 Tool 57 367 Simulation 57

37 Modeling Technique Trade-Off Analysis for Proposed HIT Diffusion Study 57 371 Soft System Methodology 60 372 Structured System Analysis and Design Method 61 373 Business Process Modeling 61 374 System Dynamics (SD) 61 375 System Context Diagram and Data Flow Diagrams

and Flow Charts 62 376 Unifi ed Modeling Language 64 377 SysML 66

38 Conclusions for Modeling Methodologies to Use 66 39 Qualitative Research Grounded Theory and UML 67

391 An Overview of Qualitative Research 67 392 Grounded Theory and Case Study Method Defi nitions 68 393 Using Grounded Theory and Case Study Together 70 394 Grounded Theory in Information Systems (IS)

and Systems Thinking Research 71 395 Criticisms of Grounded Theory 72 396 Current State of UML as a Research Tool and Criticisms 73 397 To UML or Not to UML 73 398 An Actual Example of Using Grounded Theory

in Conjunction with UML 73 References 76

4 Field Test 83 Nima A Behkami and Tugrul U Daim 41 Introduction and Objective 83 42 Background Care Management Plus 84

421 Signifi cance of the National Healthcare Problem 84 422 Preliminary CMP Studies at OHSU 85

43 Research Design 86 431 Overview 86 432 Objectives 86 433 Methodology and Data Collection 87 434 Analysis 90 435 Results and Discussion 91 436 Simulation A System Dynamics Model

for HIT Adoption 100 References 110

Contents

xiv

5 Conclusions 113 Tugrul U Daim and Nima A Behkami 51 Overview and Theoretical Contributions 113 52 Recommended Proposition for Future Research 123 References 123

Part II Evaluating Electronic Health Record Technology Models and Approaches Liliya Hogaboam and Tugrul U Daim

6 Review of Factors Impacting Decisions Regarding Electronic Records 127 Liliya Hogaboam and Tugrul U Daim 61 The Adoption of EHR with Focus on Barriers and Enablers 127 62 The Selection of EHR with Focus on Different Alternatives 133 63 The Use of EHR with Focus on Impacts 137 References 144

7 Decision Models Regarding Electronic Health Records 151 Liliya Hogaboam and Tugrul U Daim 71 The Adoption of EHR with Focus on Barriers and Enables 151

711 Theory of Reasoned Action 151 712 Technology Acceptance Model 152 713 Theory of Planned Behavior 154

72 The Selection of EHR with Focus on Different Alternatives 159 721 Criteria 160

73 The Use of EHR with Focus on Impacts 172 References 178

Part III Adoption Factors of Electronic Health Record Systems Orhun M Koumlk Nuri Basoglu and Tugrul U Daim

8 Adoption Factors of Electronic Health Record Systems 189 Orhun Mustafa Koumlk Nuri Basoglu and Tugrul U Daim 81 Introduction 18982 Literature Review 191

821 Electronic Health Records 191822 Technology Adoption Models 192823 Health Information System Adoption 195

83 Framework 19984 Methodology 206

841 Qualitative Study 206842 Expert Focus Group Study 207843 Pilot Study 207844 Quantitative Field Survey 208

Contents

xv

85 Findings 209851 Qualitative Study Findings 209852 Expert Focus Group Findings 213853 Pilot Study Findings 214854 Quantitative Field Survey Study Findings 217

86 Conclusion 230861 Limitations 231862 Implications 231

87 Appendices 232871 1 Interview Questions 232872 2 Expert Focus Group Questionnaire 233873 3 Factor Analysis Results for Pilot 236 874 4 Factor Analysis Results 238875 5 Regression Results 242

References 245

Contents

Part I A Dynamic Capabilities Theory-Based

Innovation Diffusion Model for Spread of Health Information Technology in the USA

Nima A Behkami and Tugrul U Daim

Abstract Real adoption (aka successful adoption) of an innovation occurs when an adopter has become aware of the innovation the conditions for using it make sense and the adopter has developed the capabilities to truly and meaningfully implement and use the innovation While making critical contributions existing diffusion the-ory research have not examined capabilities and conditions as part of the adoption framework this proposal helps bridge this gap This has been done by developing a new conceptual model based on Rogersrsquo classical diffusion theory with new exten-sions for capabilities The effort included selecting and integrating the appropriate methodology for data collection (case study) analysis (multi-perspectives) model development (diffusion theory dynamic capabilities) model analysis and documen-tation (Unifi ed Modeling Language) and simulation (system dynamics) In this research the new extensions to diffusion theory are studied in the context of health information technology (HIT) innovation adoption and diffusion in the USA According to the US Department of Health and Human Services (HHS) defi -nition HIT allows comprehensive management of medical information and its secure exchange between healthcare consumers and providers The promise of HIT adoption lies in reducing the cost of care delivery while increasing the quality of patient care therefore its accelerated rate of diffusion is of top priority for the gov-ernment and society

Chapter 1 introduces the crisis in the US healthcare system defi nition of HIT and the motivations for studying and advocating acceleration of HIT diffusion sup-ported especially by the government of the USA Chapter 2 describes an overview of the health delivery system and the critical stakeholders involved The stakehold-ers and their attributes are described in detail This chapter also identifi es factors effecting HIT diffusion and reviews research literature for example for factors such as barriers infl uences adopter characteristics and more The other main point dis-cussed in Chap 1 is that in order to make analysis comprehensive there is a need to look at the research area from a multi-perspective point of view The two popular methodologies of ldquo Linstonersquos Multi-perspectives rdquo and the ldquo 4+1 View Model rdquo for software architectures are examined Finally in Chap 1 important factors identifi ed

2

earlier in the chapter are categorized using Linstonersquos perspectives to show appropriateness of using multi-perspective for analysis

Chapter 3 describes the proposed model and the justifi cations for using the theories and methodologies used to support the research First a detailed description of the new proposed extensions to diffusion theory is presented that include dynamic capabilities and conditions The proposed is supported and reasoned for using fi ve main sections in the chapter that include describing diffusion theory in detail com-paring and evaluating other potential adoption theories exploring resource-based theory and capability research modeling technique trade-off analysis and quality research methods including usage of grounded theory with UML

Chapter 4 is the description of the fi eld study conducted to demonstrate the fea-sibility of research proposal The fi eld study was conducted for examining the adop-tion process for a care management product built and dissemination through Oregon Health and Science University named CMP (Care Management Plus) CMP is a HIT-enabled care model targeted for older adults and patients with multiple chronic conditions CMP components include software clinical business processes and training For this research secondary data from site (clinic) readiness survey and in- person expert interviews were used to collect data Through case study and the-matic analysis methods the data was extracted and analyzed An analysis model was built using data collected that demonstrated the structural and behavioral aspects of the system using UML and a classifi cation of capabilities Later in the chapter to demonstrate the usefulness of system dynamics a simple Bass diffusion model for spread of innovations through advertising was used to estimate dissemination of CMP using data from contact management at OHSU

Chapter 5 concludes the report and the feasibility study with the discovery that through examination of HIT adoption data indeed there is a need for extension of diffusion theory to explain organizational adoption more accurately Dynamics capabilities are an appropriate candidate for integration into diffusion theory Coupling the types of case study andor grounded theory methods with using UML makes valuable strides in studying organization and societal processes And fi nally that system dynamics method can successfully be used as a partner for scenario analysis and forecasting for a wide range of purposes This chapter concludes the report by stating propositions for future research

A Dynamic Capabilities Theory-Based Innovation Diffusion Model for Spreadhellip

3copy Springer International Publishing Switzerland 2016 TU Daim et al Healthcare Technology Innovation Adoption Innovation Technology and Knowledge Management DOI 101007978-3-319-17975-9_1

Chapter 1 Introduction to the Adoption of Health Information Technologies


11 The Healthcare Crisis in the United States

Due to changing population demographics and their state of health the healthcare system in the United States is facing monumental challenges For example patients suffering from chronic illnesses account for approximately 75 of the nationrsquos healthcare-related expenditures A patient on Medicare with fi ve or more illnesses will visit 13 different outpatient physicians and fi ll 50 prescriptions per year (Friedman Jiang Elixhauser amp Segal 2006 ) As the number of a patientrsquos condi-tions increases the risk of hospitalizations grows exponentially (Wolff Starfi eld amp Anderson 2002 ) While the transitions between providers and settings increase so does the risk of harm from inadequate information transfer and reconciliation of treatment plans A third of these costs may be due to inappropriate variation and failure to coordinate and manage care (Wolff et al 2002 ) As costs continue to rise the delivery of care must change to meet these costs

This has brought about a renewed interest from various government public and private entities for proposing solutions to the healthcare crisis (Technology health care amp management in the hospital of the future 2003 ) which is helping fuel dif-fusion research in healthcare Technology advances and the new ways of bundling technologies to provide new healthcare services is also contributing to interest in Health Information Technology (HIT) research (E-Health Care Information Systems An Introduction for Students and Professionals 2005 ) The promise of applying technology to healthcare lies in increasing hospital effi ciency and accountability and decreasing cost while increasing quality of patient care

N A Behkami Merck Research Laboratories Boston MA USA

T U Daim () Portland State University Portland OR USA e-mail tugruludaimpdxedu

4

( HealthIT hhs gov ) Therefore itrsquos imperative to study how technology in particu-lar HIT is being adopted and eventually defused in the healthcare sector to help achieve the nationrsquos goals Rogers in his seminal work has highlighted his concern for almost overnight drop and near disappearance of diffusion studies in such fi elds as sociology and has called for renewed efforts in diffusion research (Rogers 2003 ) Others have identifi ed diffusion as the single most critical issue facing our modern technological society (Green Ottoson Garciacutea amp Hiatt 2009 )

According to the US Department of Health and Human Services defi nition Health Information Technology allows comprehensive management of medical information and its secure exchange between health care consumers and providers ( HealthIT hhs gov ) Information Communication Technology (ICT) and Health Information Technology (HIT) are two terms that are often used interchangeably and generally encompass the same defi nition It is hoped that use of HIT will lead to reduced costs and improved quality of care (Heinrich 2004 ) Various policy bod-ies including Presidents Obamarsquos administration ( Organizing for America ) and other independent reports have called for various major healthcare improvements in the United States by the year 2025 ( The Commonwealth Fund ) In describing these aspirations almost always a call for accelerating the rate of HIT adoption and diffu-sion is stated as one of the top fi ve levers for achieving these improvement goals ( Organizing for America ) Hence it is of critical importance to study and understand upstream and downstream dynamics of environments that will enable successful diffusion of HIT innovations

12 Government Efforts and HIT Meaningful-Use Initiative

In order to introduce signifi cant and measurable improvements in the populations health in the United States various government and private entities seek to trans-form the healthcare delivery system by enabling providers with real-time access to medical information and tools to help increase quality and safety of care ( US Department of Health and Human Services ) Performance improvement pri-orities have focused on patient engagement reduction of racial disparities improved safety increased effi ciency coordination of care and improved popula-tion health ( US Department of Health and Human Services ) Using these priori-ties the Health Information Technology (HIT) Policy Committee a Federal Advisory Committee (FACA) to the US Department of Health and Human Services (HHS) has initiated the ldquomeaningful userdquo intuitive for adoption of Electronic Health Records (EHR)

Fueled by the $19 billion investment available through the American Recovery and Reinvestment Act of 2009 (Recovery Act) efforts are in full swing to accelerate the national adoption and implementation of health information technology (HIT) ( Assistant Secretary for Public Affairs ) The Recovery act authorizes the Centers for Medicare amp Medicaid Services (CMS) to provide payments to eligible physicians

NA Behkami and TU Daim

5

and hospitals who succeed in becoming ldquomeaningful usersrdquo of an electronic health record (EHR) Incentive payments begin in 2011 and phase out by 2015 nonadopt-ing providers will be subject to fi nancial penalties under Medicare ( US Department of Health and Human Services ) Medicare is a social insurance program adminis-tered by the United States government providing health insurance to people aged 65 and over or individuals with disabilities Similarly Medicaid provides insurance for low-income families ( US Department of Health amp Human Services Centers for Medicare amp Medicaid Services )

CMS will work closely with the Offi ce of the National Coordinator and other parts of HHS to continue defi ning incentive programs for meaningful use The Healthcare Information and Management Systems Society (HIMSS) recommend that a mature defi nition for ldquomeaningful use of certifi ed EHR technologyrdquo includes at least the following four attributes (Merrill 2009 )

1 A functional EHR certifi ed by the Certifi cation Commission for Healthcare Information Technology (CCHIT)

2 Electronic exchange of standardized patient data with clinical and administrative stakeholders using the Healthcare Information Technology Standards Panelrsquos (HITSP) interoperability specifi cations and Integrating the Healthcare Enterprisersquos (IHE) frameworks

3 Clinical decision support providing clinicians with clinical knowledge and intelligently- fi ltered patient information to enhance patient care and

4 Capabilities to support process and care measurement that drive improvements in patient safety quality outcomes and cost reductions

While existence of such programs as the meaningful-use initiative is a motiva-tion to consider using an EHR historically adoption has been slow and troublesome (Ash amp Goslin 1997 ) One often cited obstacle is the high cost of implementing Electronic Health Records Since usually incentives for adoption often go to the insurer recouping the cost is diffi cult for providers (Middleton Hammond Brennan amp Cooper 2005 Cherry 2006 Menachemi 2006 ) Other challenges existing in the United States healthcare system include variations in practices and proportion of income realized from adoption (Daim Tarman amp Basoglu 2008 Angst 2007 )

121 State of Diffusion Research General and Health IT

Health Information Technology (HIT) innovations are considered to have great potential to help resolve important issues in healthcare The potential benefi ts include enhanced accessibility to healthcare reduced cost of care and increased quality of care (COECAO 1996 ) However despite such potential many HIT innovations are either not accepted or not successfully implemented Some of the reasons cited include poor technology performance organizational issues and legal barriers (Cho Mathiassen amp Gallivan 2008 ) In general there is agreement amongst

1 Introduction to the Adoption of Health Information Technologies

6

researchers that we donrsquot fully understand what it takes for successful innovations to diffuse into the larger population of healthcare organizations

Diffusion of Innovation (DOI) theory has gained wide popularity in the Information Technology (IT) fi eld for example one study found over 70 IT articles published in IT outlets between 1984 and 1994 that relied on DOI theory (Teng Grover amp Guttler 2002 ) Framing the introduction of new Information Technology (IT) as an organizational innovation information systems (IS) researchers have studied the adoption and diffusion of modern software practices spreadsheet soft-ware customer-based inter-organizational systems database management systems electronic data interchange and IT in general (Teng et al 2002 ) These studies have been conducted at several levels (1) at the level of intra-fi rm diffusion ie diffu-sion of innovation within an organization (2) inter-fi rm diffusion at the industry level (3) overall diffusion of an innovation throughout the economy

The main models used for diffusion of innovation were established by 1970 The main modeling developments in the period 1970 onwards have been in modifying the existing models by adding greater fl exibility to the underlying model in various ways The main categories of these modifi cations are listed below (Meade amp Islam 2006 )

bull The introduction of marketing variables in the parameterization of the models bull Generalizing models to consider innovations at different stages of diffusions in

different countries bull Generalizing the models to consider the diffusion of successive generations of

technology

In most of these contributions the emphasis has been on the explanation of past behavior rather than on forecasting future behavior Examining the freshness of contributions the average age of the marketing forecasting and ORmanagement science references is 15 years the average age of the businesseconomics reference is 19 years (Meade amp Islam 2006 ) Scholars of IT diffusion have been quick to apply the widespread DOI theory to IT but few have carefully analyzed whether it is justifi able to extend the DOI vehicle to explain the diffusion of IT innovations too Similar critical voices have been raised recently against a too simplistic and fi xed view of IT (Robinson amp Lakhani 1975 )

Figure 11 shows the research publications trend in HIT and Diffusion studies (Behkami 2009a 2009b ) which shows a steep increase in interest over the last few years While adopter attitudes adoption barriers and hospital characteristics have been studied in depth other components of DOI theory are under-studied No research had attempted to explain diffusion of innovation through dynamic capabili-ties yet There also have been less than a handful of papers forecasting diffusion with system dynamics methodology Figure 12 summarizes the frequency of themes that emerged from a study that analyzed publications related to HIT Diffusion 80 of the 108 articles examined were published between the years 2004 and 2009 (Behkami 2009a )


7

References

Angst C (2007) Information technology and its transformational effect on the health care indus-try Dissertation Abstracts International Section A Humanities and Social Sciences

Ash J amp Goslin L (1997) Factors affecting information technology transfer and innovation dif-fusion in health care Innovation in Technology ManagementmdashThe Key to Global Leadership PICMETrsquo97 Portland International Conference on Management and Technology (pp 751ndash754)

Assistant Secretary for Public Affairs Process begins to defi ne ldquomeaningful userdquo of electronic health records

400

300

200

100

0

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

500articles not in PubMed

articles from PubMed (mostly Biomedical Informatics)

600

700

Fig 11 Cumulative trend of HIT diffusion research publications over the last three decades

0 5 10 15 20 25 30

Social Structure amp Communication Channels

Modeling amp Forecasting

Infusion

Workflow amp Knowledge Management

Timing amp Sustainability

Innovation Champions amp their Aids

Strategic Management amp Competitive Advantage

Adopter Attitudes Perceptions amp Characteristics

Hospital Characteristics amp the Ecosystem

Policy Making

Tools Methods amp Theories

Factors Barriers amp Influences

Fig 12 Number of published articles that address themes generated from review


8

Behkami N (2009a) Literature review Diffusion amp organizational adoption of healthcare related information technologies amp innovations

Behkami N (2009b) Methodological analysis of Health Information Technology (HIT) diffusion research to identify gaps and emerging topics in literature

COECAO (1996) Telemedicine and IO Medicine Telemedicine A guide to assessing tele-communications for health care Washington National Academies Press

Cherry B (2006) Determining facilitators and barriers to adoption of electronic health records in long-term care facilities UMI Dissertation Services ProQuest Information and Learning Ann Arbor MI

Cho S Mathiassen L amp Gallivan M (2008) From adoption to diffusion of a Telehealth innova-tion Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences (p 245) Los Alamitos CA IEEE Computer Society

Daim T U Tarman R T amp Basoglu N (2008) Exploring barriers to innovation diffusion in health care service organizations An issue for effective integration of service architecture and information technologies Hawaii International Conference on System Sciences (p 100) Los Alamitos CA IEEE Computer Society

E-Health care information systems An introduction for students and professionals San Francisco CA Jossey-Bass 2005

Friedman B Jiang H Elixhauser A amp Segal A (2006) Hospital inpatient costs for adults with multiple chronic conditions Medical Care Research and Review 63 327ndash346

Green L W Ottoson J M Garciacutea C amp Hiatt R A (2009) Diffusion theory and knowledge dissemination utilization and integration in public health Annual Review of Public Health 30 151ndash174

HealthIThhsgov Home Heinrich J (2004) HHSrsquos efforts to promote health information technology and legal barriers to

its adoption Meade N amp Islam T (2006) Modelling and forecasting the diffusion of innovationmdashA 25-year

review International Journal of Forecasting 22 519ndash545 Menachemi N (2006) Barriers to ambulatory EHR Who are lsquoimminent adoptersrsquo and how do

they differ from other physicians Informatics in Primary Care 14 101ndash108 Merrill M (2009) HIMSS publishes lsquomeaningful usersquo defi nitions Healthcare IT News Middleton B Hammond W E Brennan P F amp Cooper G F (2005) Accelerating US EHR

adoption How to get there from here Recommendations based on the 2004 ACMI retreat Journal of the American Medical Informatics Association 12

Organizing for America|BarackObamacom|Health Care Robinson B amp Lakhani C (1975) Dynamic price models for new-product planning Management

Science 21 1113ndash1122 Rogers E (2003) Diffusion of innovations (5th ed) New York Free Press Technology health care and management in the hospital of the future Praeger Publishers 2003 Teng J Grover V amp Guttler W (2002) Information technology innovations General diffusion

patterns and its relationships to innovation characteristics IEEE Transactions on Engineering Management 49 13ndash27

The Commonwealth FundmdashHealth policy health reform and performance improvement US Department of Health amp Human Services Centers for Medicare amp Medicaid Services US Department of Health amp Human Services HealthIThhsgov Health IT Policy Committee Wolff J Starfi eld B amp Anderson P G (2002) Expenditures and complications of multiple

chronic conditions in the elderly Archives of Internal Medicine 162 (20) 2269ndash2276



Chapter 2 Background Literature on the Adoption of Health Information Technologies


21 Overview of the Healthcare Delivery System

The Healthcare Delivery System is defi ned as the comprehensive collection of actors stakeholders and the relationships amongst them which when in action deliver care to the patients create economic value for the participants serve govern-ment interests and service societal needs When thinking about the healthcare deliv-ery system itrsquos benefi cial to think in terms of a value chain Lacking this integrated view in research leads to a one dimensional assessment or fails to consider views of all the stakeholders in illustrating the problem space (Chaudhry et al 2006 ) Figure 21 is an illustration of the Healthcare Delivery System in context of usage adoption and diffusion of HIT centered on the patient provider and payer The fol-lowing sections will describe in detail the signifi cance impact and infl uence of each of the components as it partitions to delivery of healthcare and diffusion of Health Information Technology



10

22 A Methodological Note

In order to provide a complete description of the healthcare delivery system the model is built and analyzed through three components Objects Relationships and Views The behavior of the system results when these elements collaborate towards a system goal This approach to analysis and decomposition is necessary for effec-tive systems thinking (Sterman amp Sterman 2000 ) To refl ect the static structure and dynamic behavior of these collaborating objects various models can be created and a range of notations can be used to describe and communicate the models such as the Unifi ed Modeling Language (UML) But in this section for simplicity a boxes-and- arrows notation has been used followed by more formal modeling languages representations in the following sections of this document In effect what has been attempted here is to produce a conceptual model of the system in a casual manner

23 The Critical Stakeholders and Actors

The critical stakeholders in the Healthcare delivery system in the United States include the providers the government the payers the patients and the suppliers In the following sections each of these categories of stakeholders is described in more detail

Fig 21 The healthcare delivery system


11

231 Care Providers

The term Provider is used to refer to the source of care that provides treatment to patients It is important to differentiate between the two instantiations of the Provider one as an Individual and another as an Organization The individual Provider is for example the Physician Nurse or someone with similar medical training that provides often one-on-one care to the patient The organization type of Provider is the clinic or hospital which is the business unit housing the physician or nurse whom provide the care

2311 Physicians Nurses and Medical Assistants

Physicians are individuals who through training experience and certifi cation are allowed to provide care to patients with a variety of illnesses A Physician can be a general practitioner such a primary care physician or a specialist Typically physi-cians are employed by a hospital or clinic Nurses similar to Physicians have been through healthcare education and often under physician supervision (and at times independently) are expected to provide care to patients Medical Assistants (MA) typically poses job-specifi c training mainly to assist physicians and nurses with routine and less education dependent activities of providing care around the clinic

During daily operations physicians nurses and MAs are typically consumers of various forms of technology-based tools and they have been subjects of various research studies (Dorr Wilcox Donnelly Burns amp Clayton 2005 Dorr et al 2006 Eden 2002 Eley Soar Buikstra Fallon amp Hegney 2009 Ford McAlearney Phillips Menachemi amp Rudolph 2008 Jha et al 2007 May et al 2001 Simpson 2007 Wilcox et al 2007 ) Research has shown that each of these types of individ-ual provides based on attributes of their work place andor their own personal char-acteristics experience various levels of technology use Their use of technology can range from simply using electronic mail or calendars to sophisticated usages such as design patient selection algorithms from EHR data

Studying this type of stakeholder is critical since they are the daily users of tech-nology and can have a profound effect on adoption of HIT Innovations They can also often act as the champion or decision makers when it comes to adopting an innovation in their clinics or hospitals As shown in Fig 21 the providers provide care to patients are employed in the clinic provide feedback to the IT vendors they use products from adopt amp use HIT innovations and collaborate with other provid-ers for providing care

2312 The Hospital or Clinic

The hospital or clinic is where patients would receive care and they are type of a provider This type of provider can range from a single physician clinic in a rural community to a large multi-system hospital in a large city Research has shown that

2 Background Literature on the Adoption of Health Information Technologies

12

these two types of providers operate drastically different from one another and when it comes to adoption of HIT they have different needs barriers and facilitators(David 1993 Fonkych 2006 Hikmet Bhattacherjee Menachemi Kayhan amp Brooks 2008 May et al 2001 Menachemi 2007 Menachemi Brooks amp Simpson 2007 Menachemi Burke amp Brooks 2004 ) In general hospitals can have various attributes that distinguishes how they participate in the healthcare dev-ilry ecosystem for example affi liation tax status number of beds technology usage culture location and more

It is important to study this type of Provider separate from the individual Provider such as a physician since their priorities are organizational where physicians are individual contributors For example a physician may feel that using an EHR at any price is justifi ed while the priorities and budget conditions of the hospital may not allow for that (Katsma Spil Light amp Wassenaar 2007 Lobach Detmer amp Supplement 2007 ) As shown in Fig 21 the hospital employeersquos physicians pays the HIT vendor for products and adopts innovations

232 Government

The role of government in the health delivery system of the United States is enor-mous (Aalbers van der Heijden Potters van Soest amp Vollebergh 2009 Bower 2005 Cherry 2006 ) Government plays this role in two ways (1) payer (meaning providing insurance through Medicaid and Medicare ( U S Department of Health Human Services Centers for Medicare Medicaid Services ) for the low income and elderly) (2) policy setter and enforcer (Rosenfeld Bernasek amp Mendelson 2005 ) As a payer the government expenditure for providing insurance through Medicare alone reached $440 billion in 2007( Centers for Medicare Medicaid Services National Health Expenditure Data ) Such volume of business makes the government have an active interest in cost reduction through adoption of HIT ( HealthIT hhs gov ) As a policy setter especially under the current Obama administration through the American Recover Act (HR 1 American recovery and reinvestment act of 2009 ) the government of the United States has taken the driver seat to implement Healthcare reform Government hopes that much of this improved in care and reduction in cost will be realized through meaningful use of HIT ( Assistant Secretary for Public Affairs ) and faster and wider spread of technology adoption

Research that have reviewed the role of government have found that it can posi-tively infl uence and sometimes accelerate more effective HIT adoption (Fonkych 2006 ) It is important to note that in the United State with a decentralized health system the government infl uences the ecosystem both at the federal level and at the regionalstate levels Hence when modeling the system it is critical to consider the multiple perspectives As shown in Fig 21 the government pays providers infl u-ences adoption decisions of providers infl uences the physicians in general invests in support agencies and encourages nationwide standards


13

233 Patients and Their Family and Care Givers

The patient is one of the most critical actors in the healthcare delivery system Patients once ill seek care through providers In 2006 Americans made a total of 902 million healthcare visits and 49 were with primary care physicians (Ambulatory medical care utilization estimates for 2006 ) Family or other care givers are one of the main support networks for the patient Research fi nds that patients with family or a network are more likely to recover As active participants in the care process patients and their familycaregivers can be a large infl uencer for HIT adoption by their providers or even use HIT themselves ( Ash 1997 Dorr et al 2005 Hersh 2004 Leonard 2004 May et al 2001 Robeznieks 2005a ) The patient family also uses HIT by using Personal Health Records (PHR) (Tang Ash Bates Overhage amp Sands 2006 ) As shown in Fig 21 this stakeholder pays pro-viders for service seeks care from physicians can provide feedback to HIT ven-dors cares for patients and use HIT innovations

234 Payers

The payers are the stakeholders who pay for the care that the patients receive They fall in the three categories of the government private insurance and the patients themselves In 2006 43 million Americans were enrolled in Medicare and 53 mil-lion enrolled in Medicaid ( Centers for Medicare Medicaid Services National Health Expenditure Data ) Medicare is an insurance program administered by the United States government providing health insurance to people aged 65 and over or indi-viduals with disabilities Similarly Medicaid provides insurance for low income families ( U S Department of Health Human Services Centers for Medicare Medicaid Services )

By having Private health coverage people can protect themselves from fi nical cost and guaranteed to have access to health care when needed (Claxton 2002 ) In order to make private healthcare affordable to individual citizens payers pool the risk of healthcare cost across large number of people This affords individuals (usu-ally through their employers) to pay a premium that is equal to the average cost of medical care for the group of people It is this spreading of the risk that makes healthcare affordable to most people in the society

Public sources of healthcare coverage include Medicare Medicaid federal and state employee health plans the military and the Veterans Administration Private health coverage is primarily through employee sponsored benefi t plans Private Citizen can also obtain individual health insurance from the free market in 2002 about 12 million nonelderly people purchased health insurance on their own (Claxton 2002 ) Examples of health insurance coverage include commercial health insurers Blue Cross and Blue Shield plans Health Maintenance Organizations (HMOs) Self-Funded Employee Health Benefi t Plans


14

With such numbers and revenue it is not surprising that Payers exercise a lot of power and leverage in the healthcare delivery system In fact the change agents in care delivery are often the demands of the payers instead those of the patients (Healthcare payers and providers Vital signs for software development 2004 ) Effectively payers are able to manipulate providers through such mechanisms as co-payments and negotiated rates for procedures It is this infl uence from payers that is pushing hospitals to invest in Health IT For example in order to deliver care more effi ciently integrating their various isolated repositories of patient data is a priority for the payers Providers fear that this push for investment in HIT can erode their already thin revenues However it is believed that if the providers are able show effective use of IT through meaningful usage Payers would be willing to compensate for infrastructure investment through future contract negations that would be more favorable and provide more revenue for the providers (Healthcare payers and providers Vital signs for software development 2004 )

235 HITInnovation Suppliers

In context of the proposed research Suppliers are either the entities that build sup-port or service the HIT innovation that are used by the providers and the patients and sometimes paid for by the payers for the purpose of delivering patient care For example the General Electric Corporation is the vendor that builds one of the most popular EHR on the market and in this case is considered a Supplier in the ecosystem Another type of Supplier is government organizations that support HIT use for pro-viders such as a Regional Health Information Organization (RHIO) discussed below

2351 HIT Vendors

HIT vendors develop and offer technical services for a variety of HIT applications such as Health Records e-prescribing and others Vendors typically specialize in serving certain size physician practices Their products are often licensed by physi-cian or user They charge maintenance and support fees and usually charge for prod-uct upgrades They offer some limited service policies and guarantees

In case of products such as Electronic Health Records (EHR) a vendorrsquos product may be certifi ed for interoperability through the Certifi cation Commission for Health Information Technology (CCHIT) (Certifi edreg 2011 ) The vendors often charge for their products to interface with other products or sources of information at the adopting hospital In some case third-party modules or components are bun-dled with a product and the customer may need to pay for them separately Implementation and training services add to the adoption cost Since usually adop-tion requires a large investment from the provider a healthy relationship is desired


15

with the vendors As shown in Fig 21 vendors receive feedback from providers and patients and try to stay competitive in the market place

2352 Regional Health Information Organizations

According to the defi nition from National Alliance for Health Information Technology a Regional Health Information Organization (RHIO) or also referred to as Health Information Exchange (HIE) is ldquoA health information organization [HIO] that brings together health care stakeholders within a defi ned geographic area and governs health information exchange [HIE] among them for the purpose of improv-ing health and care in that communityrdquo ( NAHIT releases HIT defi nitions News Healthcare Informatics ) RHIOs are the fundamental building blocks of the pro-posed National Health Information Network (NHIN) initiative presented by the Offi ce of the National Coordinator for Health Information Technology (ONCHIT) It is understood that to build an interoperable national health record network a strat-egy that initiates from the local and state levels is critical

HIE will focus on the areas of technology interoperability standards utilization and business information systems The goal of HIE is to make possible access to clinical data in an effective and timely manner Another goal of the HIEs will be to make available secondary data through implementation of infrastructure to be used for purposes of public health and consumer health research

24 Attributes of the Stakeholders

The Stakeholders described in the previous sections each have multiple attributes For example an attribute of the Hospital as a stakeholder maybe its affi liation is it affi liated with an academic university or is it purely for profi t organization These attributes determine how a stakeholder participates and infl uences the healthcare delivery ecosystem Table 21 summarizes the critical attributes associated with each healthcare system stakeholder extracted from research literature

25 Important Factors Effecting Diffusion and Adoption for HIT

While stakeholders and their attributes determine some of the characteristics of the healthcare delivery system there are other factors that also infl uence the ecosystem The categories of these factors include Barriers amp Infl uences theories amp methodologies policy making ecosystem characteristics adopter attitudes market competition inno-vation champions clinic workfl ow timing modeling infusion and social structures


16

Table 21 Stakeholders and attributes

Stakeholder Attribute(s)

Providersphysiciansnurses bull Attitudes toward technology bull Education bull Age bull Comfort with computers bull Leadership style bull Personality bull Workload and productivity bull Stage in career bull Previous experience with adoption bull Specialization bull Role in team bull Continuing education

ProvidersHospital bull Payer mix bull IT concentration bull Patient demographics bull Geography bull Affi liation (academic or other) bull IT operations bull Budget availability bull Type of care provided bull Size bull Affl uence of customer base bull Decision making processes bull Tax status bull Partnerships bull Previous adoption experience bull Org structure style

Government bull Standards bull Regulation bull Education bull Government assistance bull Reimbursement bull Financial incentives

Patient and family bull Quality of care bull Biographic data bull Size of support network bull Education bull Experience with technology bull Extent of illness bull Family and marital status bull Age bull Attitudes towards technology

Payers bull Patient demographics bull Type (public private) bull Executive team bull Mix of patients

(continued)


17

251 Barriers and Infl uences

Evaluating facilitators and barriers to adoption of electronic health records in long- term care facilities reviled the following barriers costs training implementation processes and compatibility with existing systems (Cherry 2006 ) Physicians EHR adoption patterns show those practicing in large groups in hospitals or medical centers and in the western region of the United States were more likely to use electronic health records (DesRoches et al 2008 ) Less likely are those hospitals that are smaller more rural non-system affi liated and in areas of low environmen-tal uncertainty (Kazley amp Ozcan 2007 ) Another study fi nds support for a positive relationship between IT concentration and likelihood of adoption (Angst 2007 ) Academic affi liation and larger IT operating capital and staff budgets are associ-ated with more highly automated clinical information systems (Amarasingham et al 2008 ) Hospital EMR adoption is signifi cantly associated with environmental uncertainty type of system affi liation size and urban-ness The effects of competi-tion munifi cence ownership teaching status public payer mix and operating mar-gin are not statistically signifi cant (Kazley amp Ozcan 2007 )

Shared electronic records are not plug-in technologies They are complex inno-vations that must be accepted by individual patients and staff and also embedded in organizational and inter-organizational routines (Greenhalgh et al 2008 ) Physicians located in counties with higher physician concentration were found to be more likely to adopt EHRs Health maintenance organization penetration rate and poverty level were not found to be signifi cantly related to EHR adoption However practice size years in practice Medicare payer mix and measures of technology readiness were found to independently infl uence physician adoption (Abdolrasulnia et al 2008 ) Organizational variables of ldquodecision makingrdquo and ldquoplanningrdquo have signifi cant impacts and successfully encouraging usage of the CPR entails attention and resources devoted to managing the organizational aspects of implementation ( Ash 1997 )

Table 21 (continued)


SuppliersHIT vendors bull Portfolio bull Expertise bull Cost Structure bull Marketing bull Partnerships bull Reputation bull Brand positioning

SuppliersHealth information exchange bull Standards bull Regulation bull Geography bull Cost structure


18

Hospitals that place a high priority on patient safety can more easily justify the cost of Computerized Physician Order Entry (CPOE) Outside the hospital fi nan-cial incentives and public pressures encourage CPOE adoption Dissemination of data standards would accelerate the maturation of vendors and lower CPOE costs (Poon et al 2004 ) Adoption of functionalities with fi nancial benefi ts far exceeds adoption of those with safety and quality benefi ts (Poon et al 2006 ) The ideal COPE would be a system that is both customizable and integrated with other parts of the information system is implemented with maximum involvement of users and high levels of support and is surrounded by an atmosphere of trust and collabora-tion (Ash Lyman Carpenter amp Fournier 2001 )

Lack of clarity about the value of telehealth implementations is one reason cited for slow adoption of telemedicine (Cusack et al 2008 ) Others have looked at potential factors affecting telehealth adoption (Gagnon et al 2004 ) and end user online literature searching the computer-based patient record and electronic mail systems in academic health sciences centers in the United States ( Ash 1997 ) Successful diffusion of online end user literature searching is dependent on the visibility of the systems communication among rewards to and peers of possible users who promote use (champions) ( Ash 1997 ) Adoption factors on RFID deployment in healthcare applications have also been researched (Kuo amp Chen 2008 )

Technology and Administrative innovation adoption factors that have been iden-tifi ed include the job tenure cosmopolitanism educational background and organi-zational involvement of leaders (Kimberly amp Evanisko 1981 ) Hospitals that adopted a greater number of IT applications were signifi cantly more likely to have desirable quality outcomes on seven Inpatient Quality Indicator measures (Menachemi Saunders Chukmaitov Matthews amp Brooks 2007 ) Factors found to be positively correlated with PSIT (patient safety-related IT) use included physi-cians active involvement in clinical IT planning the placement of strategic impor-tance on IT by the organization CIO involvement in patient safety planning and the perception of an adequate selection of products from vendors (Menachemi Burke amp Brooks 2004 )

Patientrsquos fears about having their medical records available online is hindering not helping the push for electronic medical records Specifi c concerns include com-puter breaches and employers having access to the records(Robeznieks 2005b ) Public sector support is essential in fi ve main aspects of child health information technology namely data standards pediatric functions in health information systems privacy policies research and implementation funding and incentives for technology adoption(Conway White amp Clancy 2009 )

Financial barriers and a large number of HIT vendors offering different solu-tions present signifi cant risks to rural health care providers wanting to invest in HIT (Bahensky Jaana amp Ward 2008 ) The relative costs of the interventions or technologies compared to existing costs of care and likely levels of utilization are critical factors in selection (Davies Drummond amp Papanikolaou 2001 ) Reasons for the slow adoption of healthcare information technology include a misalign-ment of incentives limited purchasing power among providers and variability in


19

the viability of EHR products and companies and limited demonstrated value of EHRs in practice (Middleton Hammond Brennan amp Cooper 2005 ) Community Health Centers (CHC) serving the most poor and uninsured patients are less likely to have a functional EHR CHCs cited lack of capital as the top barrier to adoption (Shields et al 2007 ) Increasing cost pressures associated with managed-care environments are driving hospitalsrsquo adoption of clinical and administrative IT systems as a means for cost reduction (Menachemi Hikmet Bhattacherjee Chukmaitov amp Brooks 2007 )

252 Tools Methods and Theories

A hospitalrsquos clinical information system requires a specifi c environment in which to fl ourish Clinical Information Technology Assessment Tool (CITAT) which mea-sures a hospitalrsquos level of automation based on physician interactions with the infor-mation system has been used to explain such environment (Amarasingham et al 2008 ) Multi-perspectives and Hazard Modeling Analysis have been used to study impact of fi rm characteristics on diffusion of Electronic Medical Records (Angst 2007 ) Elaboration Likelihood Model and Individual Persuasion model to study presence of privacy concerns in adoption of Electronic Medical Records (Angst 2007 ) Physician Order Entry (POE) adoption has been studied qualitatively using observations focus groups and interviews (Ash et al 2001 )

Other research has built conceptual models to lay out the relationships among factors affecting IT diffusion in health care organizations (Daim Tarman amp Basoglu 2008 ) Yet others have adapted diffusion of innovation (DOI) framework to the study of information systems innovations in healthcare organizations (Wainwright amp Waring 2007 ) and build a causal model to describe the development path of telemedicine internationally (Higa 1997 ) There have been attempts to extend the model of hospital innovation in order to incorporate new forms of inno-vation and new actors in the innovation process in accordance with the Schumpeterian tradition of openness (Djellal amp Gallouj 2007 ) Health innovation has been described as complex bundles of new medical technologies and clinical services emerging from a highly distributed competence base (Consoli amp Mina 2009 )

User acceptance of a Picture Archiving and Communication System has been studied through unifi ed theory of acceptance and use of technology (UTAUT) in a radiological setting (Duyck et al 2006 ) Technology Acceptance Model (TAM) and Trocchia and Jandarsquos interaction themes enabled exploring factors impacting the engagement of consumers aged 65 and older with higher forms of IT primarily PCs and the Internet (Hough amp Kobylanski 2009 ) One Electronic Medical Record (EMR) study examined the organizational and environmental correlates using a Resource Dependence Theoretical Perspective (Kazley amp Ozcan 2007 ) Since Healthcare today is mainly knowledge-based and the diffusion of medical knowl-edge is imperative for proper treatment of patients a study of the industry explored


20

barriers to knowledge fl ow using a Cultural Historical Activity Theory framework (Deng amp Poole 2003 Lin Tan amp Chang 2008 )

Diffusion of innovation framework has also been used to discuss factors affect-ing adoption of telemedicine (Menachemi Burke amp Ayers 2004 Park amp Chen 2007 ) Smartphone userrsquos perceptions in a healthcare setting have been studied based on technology acceptance model (TAM) and innovation attributes (Park amp Chen 2007 ) A study of Information Technology Utilization in Mental Health Services utilization adopted two theoretical framework models from Teng and Calhounrsquos computing and communication dimensions of information technology and Hammer and Mangurianrsquos conceptual framework for applications of communi-cations technology (Saouli 2004 )

To identify factors that affect hospitals in adopting e-signature the Technology-Organization- Environment (TEO) have been adopted (Chang Hwang Hung Lin amp Yen 2007 ) An examination of factors that infl uence the healthcare profession-alsrsquo intent to adopt practice guideline innovation combined diffusion of innovation theory and the theory of planned behavior (TPB) (Granoff 2002 ) To identify the concerns of managers and supervisors for adopting a managerial innovation the Concerns-Based Adoption Model and the Stages of Concern (SoC) were utilized (Agney 1997 )

253 Policy Making

There is a gap in our knowledge on how regulatory policies and other national health systems attributes combine to impact on the utilization of innovation and health system goals and objectives A study found that strong regulation adversely affects access to innovation reduces incentives for research-based fi rms to develop innovative products and leads to short- and long-term welfare losses Concluding that policy decision makers need to adopt a holistic approach to policy making and consider potential impact of regulations on the uptake and diffusion of innovations innovation systems and health system goals (Atun Gurol-Urganci amp Sheridan 2007 ) Recommendations have been made to stimulate adoption of EHR including fi nancial incentives promotion of EHR standards enabling policy and educational marketing and supporting activities for both the provider community and health-care consumers (Blumenthal 2009 Middleton et al 2005 ) Proposed manners on how the government should assist are a reoccurring topic (Bower 2005 )

Economic issues for health policy and policy issues for economic appraisal have concluded that a wide range of mechanisms exist to infl uence the diffusion and use of health technologies and that economic appraisal is potentially applicable to a number of them (Drummond 1994 ) Other conclusions calls for greater Centers for Medicare and Medicaid Service (CMS) involvement and reimbursement models that would reward higher quality and effi ciency achieved (Fonkych 2006 ) Medicare should pay physicians for the costs of adopting IT and assume that future savings to Medicare will justify the investment The Medicare Payment Advisory Commission


21

(MedPAC) recommended establishing a budget-neutral pay-for-performance pro-gram to reward physicians for the outcomes of use instead of simply helping them purchase a system (Hackbarth amp Milgate 2005 Menachemi Matthews Ford amp Brooks 2007 )

As the largest single US purchaser of health care services Medicare has the power to promote physician adoption of HIT The Centers for Medicare and Medicaid Services should clarify its technology objectives engage the physician community shape the development of standards and technology certifi cation crite-ria and adopt concrete payment systems to promote adoption of meaningful tech-nology that furthers the interests of Medicare benefi ciaries (Powner 2006 Rosenfeld et al 2005 )

Imminent adopters perceived EHR barriers very differently from their other colleges For example imminent adopters were signifi cantly less likely to consider upfront cost of hardwaresoftware or that an inadequate return on investment was a major barrier to EHR Policy and decision makers interested in promoting the adop-tion of EHR among physicians should focus on the needs and barriers of those most likely to adopt HER (Menachemi 2006 ) Ensuring comparable health IT capacity among providers that disproportionately serve disadvantaged patients will have increasing relevance for disparities thus monitoring adoption among such provid-ers should be a priority (Shields et al 2007 ) In the health information security arena results suggest that signifi cant non-adoption of mandated security measures continues to occur across the health-care industry (Lorence amp Churchill 2005 )

254 Hospital Characteristics and the Ecosystem

Academic affi liation and larger IT operating capital and staff budgets are associ-ated with more highly automated clinical information systems (Amarasingham et al 2008 ) Despite several initiatives by the federal government to spur this devel-opment HIT implementation has been limited particularly in the rural market (Bahensky et al 2008 ) Study of a small clinic found that the EHR implementation did not change the amount of time spent by physicians with patients On the other hand the work of clinical and offi ce staff changed signifi cantly and included decreases in time spent distributing charts transcription and other clerical tasks (Carayon Smith Hundt Kuruchittham amp Li 2009 )

Health IT adoption for medication safety indicate wide variation in health IT adoption by type of technology and geographic location Hospital size ownership teaching status system membership payer mix and accreditation status are associ-ated with health IT adoption although these relationships differ by type of technol-ogy Hospitals in states with patient safety initiatives have greater adoption rates (Furukawa Raghu Spaulding amp Vinze 2008 ) Another study examined geographic location (urban versus rural) system membership (stand-alone versus system- affi liated) and tax status (for-profi t versus non-profi t) and found that location is systematically related to HIT adoption (Hikmet Bhattacherjee Menachemi


22

Kayhan amp Brooks 2008 ) Others studies have also considered hospital characteris-tics (Jha Doolan Grandt Scott amp Bates 2008 Koch amp Kim 1998 )

Although top information technology priorities are similar for all rural hospitals examined differences exist between system-affi liated and stand-alone hospitals in adoption of specifi c information technology applications and with barriers to infor-mation technology adoption (Menachemi Burke Clawson amp Brooks 2005 ) Hospitals adopted an average of 113 (452 ) clinical IT applications 157 (748 ) administrative IT applications and 5 (50 ) strategic IT applications (Menachemi Chukmaitov Saunders amp Brooks 2008 )

There are concerns that psychiatry may lag behind other medical fi elds in adopt-ing information technology (IT) Psychiatristsrsquo lesser reliance on laboratory and imaging studies may explain differences in data exchange with hospitals and labs concerns about patient privacy are shared among all medical providers (Mojtabai 2007 ) Some innovations in health information technology for adult populations can be transferred to or adapted for children but there also are unique needs in the pedi-atric population (Conway et al 2009 )

255 Adopter Attitudes Perceptions and Characteristics

Studies have been conducted on perceptions and attitudes of healthcare profession-als towards telemedicine technology (Al-Qirim 2007a ) A diffusion study of a community-based learning venue demonstrated that about half of this senior popu-lation was interested in using the Internet as a tool to fi nd credible health informa-tion (Cortner 2006 ) Societal trends are transforming older adults into lead adopters of a new 247 lifestyle of being monitored managed and at times motivated to maintain their health and wellness A study of older adults perception of Smart Home Technologies uncovered support of technological advance along with a vari-ety of concerns that included usability reliability trust privacy stigma accessibil-ity and affordability (Coughlin DrsquoAmbrosio Reimer amp Pratt 2007 ) Factors impacting the engagement of healthcare consumers aged 65 and older with higher forms of IT primarily PCs and the Internet have been examined (Hough amp Kobylanski 2009 )

Principal uses for the Information Technology by the nurses are for access to patientsrsquo records and for internal communication However not all aspects of computer introduction to nursing are positive (Eley et al 2009 ) Physicians who cared for large minority populations had comparable rates of EHR use identifi ed similar barriers and reported similar benefi ts (Jha et al 2007 ) Patients have a role in designing Health Information Systems (Leonard 2004 ) and consideration of patient values and preferences in making clinical decisions is essential to deliver the highest quality of care (Melnyk amp Fineout-Overholt 2006 ) Patient characteristics of hospi-tals are related to the adoption of health IT has been under studied Once study pro-posed that children when hospitalized are more likely to seek care in technologically


23

and clinically advanced facilities However it is unclear whether the IT adopted is calibrated for optimal pediatric use (Menachemi Brooks amp Simpson 2007 )

256 Strategic Management and Competitive Advantage

The diffusion of health care technology is infl uenced by both the total market share of care organizations as well as the level of competition among them Results show that a hospital is less likely to adopt the technology if Healthcare Maintenance Organization (HMO) market penetration increases but more likely to adopt if HMO competition increases (Bokhari 2009 ) Increasing cost pressures associated with managed-care environments are driving hospitalsrsquo adoption of clinical and adminis-trative IT systems as such adoption is expected to improve hospital effi ciency and lower costs (Menachemi Hikmet et al 2007 )

Deployment of health information technology (IT) is necessary but not suffi -cient for transforming US health care The strategic impact of information tech-nology convergence on healthcare delivery and support organizations have been studied (Blumberg amp Snyder 2001 ) Four focus areas for application of strategic management have been identifi ed adoption governance privacy and security and interoperability (Kolodner Cohn amp Friedman 2008 ) While another found little that strategic behavior or hospital competition affects IS adoption (McCullough 2008 )

A study looking at strategic behavior of EHR adopters found that the relevance of EHR merely focuses on the availability of information at any time and any place This implementation of relevance does not meet end-usersrsquo expectations and is insuffi cient to accomplish the aspired improvements In addition the used participa-tion approaches do not facilitate diffusion of EHR in hospitals (Katsma Spil Ligt amp Wassenaar 2007 )

257 Innovation Champions and Their Aids

There is a need for the tight coupling between the roles of both the administrative and the clinical managers in healthcare organizations in order to champion adoption and diffusion and to overcome many of the barriers that could hinder telemedicine success (Al-Qirim 2007b ) Survey of chief information offi cers (CIOs) the indi-viduals who manage HIT adoption effort suggests that the CIO position and their responsibilities varies signifi cantly according to the profi t status of the hospital (Burke Menachemi amp Brooks 2006 )

Acting as aids to change-agents in healthcare settings Clinical engineers can identify new medical equipment review their institutionrsquos technological posi-tion develop equipment-selection criteria supervise installations and monitor


24

post- procurement performance to meet their hospitalrsquos programrsquos objectives The clinical engineerrsquos skills and expertise are needed to facilitate the adoption of an innovation (David 1993 ) However Information technology implementation is a political process and in the increasingly cost-controlled high-tech healthcare environment a successful nursing system implementation demands a nurse leader with both political savvy and technological competency (Simpson 2000 ) One study found that prior user testimony had a positive effect on new adaptors (Eden 2002 )

258 Workfl ow and Knowledge Management

Successful adoption of health IT requires an understanding of how clinical tasks and workfl ows will be affected yet this has not been well described Understanding the clinical context is a necessary precursor to successful deployment of health IT (Leu et al 2008 ) Healthcare today is mainly knowledge-based and the diffu-sion of medical knowledge is imperative for proper treatment of patients (Lin et al 2008 ) For example researchers must determine how to take full advantage of the potential to create and disseminate new knowledge that is possible as a result of the data that are captured by EHR and accumulated as a result of EHR diffusion (Lobach amp Detmer 2007 ) Findings suggest that some small practices are able to overcome the substantial learning barriers presented by EMRs but that others will require support to develop suffi cient learning capacity (Reardon amp Davidson 2007 )

259 Timing and Sustainability

Determining the right time for adoption and the appropriate methods for calculating the return on investment are not trivial (Kaufman Joshi amp OrsquoDonnell 2009 ) Among the practices without an EHR 13 plan to implement one within the next 12 months 24 within the next 1ndash2 years 11 within the next 3ndash5 years and 52 reported having no plans to implement an EHR in the foreseeable future (Simpson 2000 ) The relationship between the timing of adoption of a technologi-cal innovation and hospital characteristics have been explored (Poulsen et al 2001 )

Key factors that infl uence sustainability in the diffusion of the Hospital Elder Life Program (HELP) are Staff experiences sustaining the program recognizing the need for sustained clinical leadership and funding as well as the inevitable modifi cations required to sustain innovative programs can promote more-realist (Bradley Webster Baker Schlesinger amp Inouye 2005 )


25

2510 Modeling and Forecasting

The future diffusion rate of CPOE systems in US hospitals is empirically predicted and three future CPOE adoption scenarios-ldquoOptimisticrdquo ldquoBest estimaterdquo and ldquoConservativerdquo developed Two of the CPOE adoption scenarios have diffusion S-curve that indicates a technology will achieve signifi cant market penetration Under current conditions CPOE adoption in urban hospitals will not reach 80 penetration until 2029 (Ford et al 2008 ) Using a Bass Diffusion Model EHR adoption has been predicted Under current conditions EHR adoption will reach its maximum market share in 2024 in the small practice setting The promise of improved care quality and cost control has prompted a call for universal EHR adoption by 2014 The EHR products now available are unlikely to achieve full diffusion in a critical market segment within the time frame being targeted by policy makers (Ford Menachemi amp Phillips 2006 ) Others have attempted to model healthcare technology adoption patterns (Carrier Huguenor Sener Wu amp Patek 2008 )

2511 Infusion

Innovation attributes are important predictors for both the spread of usage (internal diffusion) and depth of usage (infusion) of electronic mail in a healthcare setting (Ash amp Goslin 1997 ) In a study two dependent variables internal diffusion (spread of diffusion) and infusion (depth of diffusion) were measured Little correlation between them was found indicating they measured different things (Ash 1999 ) Study of organizational factors which infl uence the diffusion of end user online lit-erature searching the computer-based patient record and electronic mail systems in academic health sciences centers found that Organizational attributes are important predictors for diffusion of information technology innovations Individual variables differ in their effect on each innovation The set of attributes seems less able to pre-dict infusion ( Ash 1997 )

2512 Social Structure and Communication Channels

Resisting and promoting new technologies in clinical practice face a fundamental problem of the extent to which the telecommunications system threatened deeply embedded professional constructs about the nature and practice of care giving rela-tionships (May et al 2001 ) Researchers have also attempted to understand how and why patient and consumer organizations use Health Technology Assessment


26

(HTA) fi ndings within their organizations and what factors infl uence how and when they communicate their fi ndings to members or other organizations (Fattal amp Lehoux 2008 )

26 The Need for Multiple Perspectives in Research

In his book ldquoUsing Multiple Perspective to improve performancerdquo Linstone states that the approach of looking at the problem from multiple perspectives will enable ldquo viewing complex systems and decision about them from different perspectives each providing insights not attainable with the others rdquo (Linstone 1999 ) Due to the ever growing complexity of systems many researchers and practitioners have advo-cated the need for viewing building and analyzing systems (especially those used by humans and the society) from multiple views Two methods that are pertinent to the HIT diffusion research being proposed here are Linstonersquos Multiple Perspectives Methodology and the ldquo4 + 1 viewrdquo model originated by Philippe Kruchten ( 1995 ) and popularized in Software Engineering and Software Architecture Domains The next two sections discuss these to methodologies in detail

27 Linstonersquos Multiple Perspectives Method

There are three perspectives that are part of Linstonersquos Multiple Perspectives meth-odology Technical (T) Organizational (O) and Personal (P) (Linstone 1999 )

In the T perspective the technology and its environment are viewed as a system The T perspective is a rational approach to viewing the problem and it represents a quantitative approach to viewing the world in terms of for example alternatives trade-offs optimization data and models (Linstone 1999 )

The O perspective is concerned with less technical matters and more what affects organizations can have The O perspective also describes the culture that has helped form and connects the organization or a society For example an example of an item from this view could be fear of staff in a company about making errors in their work The O perspective can help by identifying pressures on the technology insights into societal abilities to absorb a technology and increase abilities to facili-tate organizationrsquos support for technology

According to Linestone the P perspective can be the hardest view to defi ne and should include any matters relating to individuals that are not included in other views In general the P perspective helps us better understand the O perspective Individuals matter and they can sometimes bring changes to organization with less effort than the whole institution would the P perspective identifi es their character-istic and behavior Perspectives are dynamic and change over time they also can confl ict or support each other Table 22 shows a summary of characteristics for each Linestone perspective (Linstone 1999 )


27

Tabl

e 2

2 Su

mm

ary

of L

inst

onersquo

s m

ulti-

pers

pect

ives

cha

ract

eris

tics

(Lin

ston

e 1

999 )

Tech

nica

l (T

) O

rgan

izat

iona

l (O

) Pe

rson

al (

P)

Wor

ldvi

ew

Scie

nce-

tech

nolo

gy

Uni

que

grou

p or

inst

itutio

nal v

iew

In

divi

dual

the

sel

f O

bjec

tive

Prob

lem

sol

ving

pro

duct

A

ctio

n p

roce

ss s

tabi

lity

Pow

er i

nfl u

ence

pre

stig

e Sy

stem

foc

us

Art

ifi ci

al c

onst

ruct

So

cial

G

enet

ic p

sych

olog

ical

M

ode

of in

quir

y O

bser

vatio

n a

naly

sis

dat

a an

d m

odel

s C

onse

nsua

l ad

vers

ary

bar

gain

ing

and

com

prom

ise

Intu

ition

lea

rnin

g e

xper

ienc

e

Eth

ical

bas

is

Log

ical

rat

iona

lity

Just

ice

fai

rnes

s M

oral

ity p

erso

nal e

thic

s Pl

anni

ng h

oriz

on

Far

(low

dis

coun

ting)

In

term

edia

te (

mod

erat

e di

scou

ntin

g)

Shor

t for

mos

t (hi

gh d

isco

untin

g)

Oth

er d

escr

ipto

rs

Cau

se a

nd e

ffec

t O

ptim

izat

ion

Qua

ntifi

catio

n tr

ade-

offs

cos

t-be

nefi t

ana

lysi

s Pr

obab

ilitie

s a

vera

ges

sta

tistic

s

expe

cted

val

ue

Prob

lem

sim

plifi

ed a

nd id

ealiz

ed

redu

ctio

nism

N

eed

valid

atio

n r

eplic

abili

ty

Con

cept

ualiz

atio

n s

yste

ms

theo

ries

U

ncer

tain

ties

note

d

Age

nda

(pro

blem

of

the

mom

ent)

Sa

tisfy

ing

Incr

emen

tal c

hang

e R

elia

nce

on e

xper

ts i

nter

nal t

rain

ing

of

prac

titio

ners

Pr

oble

m d

eleg

ated

fac

tore

d is

sues

and

cr

isis

man

agem

ent

Nee

d st

anda

rd o

pera

ting

proc

edur

es

reut

iliza

tion

Rea

sona

blen

ess

Unc

erta

inty

use

d fo

r or

gani

zatio

nal

self

-pre

serv

atio

n

Cha

lleng

e an

d re

spon

se l

eade

rs a

nd

follo

wer

s A

bilit

y to

cop

e w

ith o

nly

a fe

w a

ltern

ativ

es

Fear

of

chan

ge

Nee

d fo

r be

liefs

illu

sion

s m

ispe

rcep

tion

of

prob

abili

ties

Hie

rarc

hy o

f in

divi

dual

nee

ds (

surv

ival

hellip)

Nee

d to

fi lte

r ou

t inc

onsi

sten

t im

ages

C

reat

ivity

vis

ion

by th

e fe

w i

mpr

ovis

atio

n N

eed

for

cert

aint

y

Cri

teri

a fo

r ldquoa

ccep

tabl

e ri

skrdquo

Log

ical

sou

ndne

ss o

penn

ess

to

eval

uatio

n d

ecis

ion

anal

ysis

In

stitu

tiona

l com

patib

ility

pol

itica

l ac

cept

abili

ty p

ract

ical

ity

Con

duci

vene

ss to

lear

ning

foc

us o

n ldquom

e-no

wrdquo

Com

mun

icat

ions

Te

chni

cal r

epor

t br

iefi n

g In

side

r la

ngua

ge o

utsi

ders

rsquo as

sum

ptio

ns

ofte

n m

ispe

rcei

ved

Pers

onal

ity a

nd c

hari

sma

desi

rabl

e


28

When using the perspectives to build a real-world model or make a decision so called the ldquo Ultimate decision rdquo by Linstone all inputs from various perspectives should to be integrated The process of integration is never simply adding the infor-mation up from various perspectives The perspectives have to fi t each other some-times reinforcing each other or canceling each other out (Linstone 1999 Linstone Mitroff amp Hoos )

28 The ldquo4 + 1 Viewrdquo Model for Software Architectures

Numerous sources emphasis the importance of modeling business processes and the relevant ecosystems however there seems to be a lack of guidance on how to best capture these architectures Documenting a model is an important sub-disciple of software engineering Architecture allows us to concentrate on the components and relationship at a relevant yet manageable level Dividing a complex problem into parts allows groups to participate in solving a problem In general documenting systems serves three important purposes as a means of education by using it to introduce people to the system a tool for communication between stakeholders and provides appropriate information for analysis

A view represents elements and relationships amongst them within a system When documenting a model a view highlights dimensions of the system architecture while hiding other details Various authors have recommended specifi c views that should be employed when documenting software architectures including Zachman Framework ( The Zachman Framework ) Reference Model for Open Distributed Processing (RM-ODP) ( Reference model of open distributed processing Wiki ) Department of Defense Architecture Framework (DoDAF) ( DoDAF Architecture Framework Version 2 0 ) Federal Enterprise Architecture ( Federal Enterprise Architecture ) and Nominal Set of Views (ANSIIEEE 1471 ) In particular ldquo4 + 1rdquo approach to architecture by Philippe Kruchten of the Rational Corporation (Kruchten 1995 ) has been infl uential used in system building it uses four views (Logical Process Development and Physical) with a fi fth view (Scenarios) that ties the other four together While these are benefi cial views they may not be useful in every system and the ultimate purpose is to separate concerns and document the model for a variety of stakeholders (Bachmann

et al 2001 )

29 Categorization of Important Factors in HIT Adoption Using Multi-perspectives

Recall that Linstonersquos multi-perspectives methodology uses the Technical Perspective (T) Organizational Perspective (O) and the Personal Perspective (P) In Sect 25 infl uencing factors within the healthcare delivery ecosystem were iden-tifi ed In this section using an iterative thematic analysis method the important


29

factors have been group into T-O-P perspectives showing how the various factors relating to HIT Diffusion can fi t into views and the proposed research

Consistent with Linstone methodology if a factor was related to technology and its focus was an artifi cial construct it was placed under the T column If the factor was from an institutional view and its system focus was social it was placed under O column If the factor was related to an individual or self with a psychological focus it was placed in the P column Table 23 shows the combinations of stakehold-ers and perspectives being considered in this research Table 24 lists each factor in

Table 23 Userperspective matrix

Perspectives

Technical perspectives (T)

Organizational perspective (O)

Personal perspective (P)

Stakeholders Patient X X X Provider X X X Payer X X X Government X X X

Table 24 Classifi cation of HIT diffusion factors by Linstone T-O-P perspectives

Technical perspective (T) Organizational perspective (O) Personal perspective (P)

Increase quality of care Reduce cost Patient family Increase accessibility of care Increase productivity Adoption decision Quality metrics Environment Patient satisfaction HIT innovations Value chain Provider attitude towards Adoption rate Patient coordination Adoption Adoption timeline Adoption decision Provider education Diffusion Adoption attitudes Social structure Meaningful HIT use Adoption barriers and challenges Support network Reimbursement Facilitators Comfort with using

technology Payer model IT decision makers Communication channels Payer mix Financial decision maker Staff roles Demographics Affi liation Staff Education Lock in cost Tax status Support cost Minority population status Standards Social structure Social system Communication channels Social structure Information activities Communication channels Diffusion activities Size

Public opinion IT operations Budget availability


30

its relevant T-O-P perspective column at this time they are combined for all the stakeholders in the future factors can be separated by stakeholder

References

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Abdolrasulnia M Menachemi N Shewchuk R M Ginter P M Duncan W J amp Brooks R G (2008) Market effects on electronic health record adoption by physicians Health Care Management Review 33 243

Agney M (1997) Managersrsquo and supervisorsrsquo stages of concern regarding adoption of Total Quality ManagementContinuous Quality Improvement as an organizational innovation in a medical center hospital Dissertation Abstracts International Section A Humanities and Social Sciences

Al-Qirim N (2007a) Realizing telemedicine advantages at the national level Cases from the United Arab Emirates Telemedicine and e-Health 13 545ndash556

Al-Qirim N (2007b) Championing telemedicine adoption and utilization in healthcare organiza-tions in New Zealand International Journal of Medical Informatics 76 42ndash54

Amarasingham R Diener-West M Plantinga L Cunningham A C Gaskin D J amp Powe N R (2008) Hospital characteristics associated with highly automated and usable clinical information systems in Texas United States BMC Medical Informatics and Decision Making 8 39

Ambulatory medical care utilization estimates for 2006 (Center for Disease Control and Prevention)


ANSIIEEE Standard 1471ISOIEC 42010 (Recommended Practice for Architectural Description of Software-Intensive Systems)

Ash J (1997) Organizational factors that infl uence information technology diffusion in academic health sciences centers Journal of the American Medical Informatics Association 4 102ndash109

Ash J S (1997) Factors affecting the diffusion of the computer-based patient record Proceedings of the AMIA Annual Fall Symposium 682ndash686

Ash J S (1999) Factors affecting the diffusion of online end user literature searching Bulletin of the Medical Library Association 87 58

Ash J amp Goslin L (1997) Factors affecting information technology transfer and innovation dif-fusion in health care Innovation in technology managementmdashthe key to global leadership PICMET rsquo97 Portland International Conference on Management and Technology (pp 751ndash754)

Ash J S Lyman J Carpenter J amp Fournier L (2001) A diffusion of innovations model of physician order entry Proceedings of the AMIA Symposium 22


Atun R A Gurol-Urganci I amp Sheridan D (2007) Uptake and diffusion of pharmaceutical innovations in health systems Innovation in the Biopharmaceutical Industry 85

Bachmann F Bass L Clements P Garlan D Ivers J Little R et al (2001) Documenting software architectures Organization of documentation package Pittsburgh PA Software Engineering Institute


31

Bahensky J A Jaana M amp Ward M M (2008) Health care information technology in rural America Electronic medical record adoption status in meeting the national agenda The Journal of Rural Health 24 101ndash105

Blumberg M R amp Snyder R L (2001) The strategic impact of information technology conver-gence on healthcare delivery and support organizations Biomedical Instrumentation and Technology 35 177ndash187

Blumenthal D (2009) Stimulating the adoption of health information technology New England Journal of Medicine 360 1477

Bokhari F A (2009) Managed care competition and the adoption of hospital technology The case of cardiac catheterization International Journal of Industrial Organization 27 223ndash237

Bower A G (2005) The diffusion and value of healthcare information technology Santa Monica CA Rand Corporation

Bradley E H Webster T R Baker D Schlesinger M amp Inouye S K (2005) After adoption Sustaining the innovation A case study of disseminating the hospital elder life program Journal of the American Geriatrics Society 53 1455ndash1461

Burke D Menachemi N amp Brooks R (2006) Health care CIOs Assessing their fi t in the orga-nizational hierarchy and their infl uence on information technology capability The Health Care Manager 25 167

Carayon P Smith P Hundt A S Kuruchittham V amp Li Q (2009) Implementation of an electronic health records system in a small clinic The viewpoint of clinic staff Behaviour and Information Technology 28 5ndash20

Carrier J M Huguenor T W Sener O Wu T J amp Patek S D (2008) Modeling the adoption patterns of new healthcare technology with respect to continuous glucose monitoring IEEE Systems and Information Engineering Design Symposium 2008 SIEDS 2008 (pp 249ndash254)

Centers for Medicare amp Medicaid Services National Health Expenditure Data CCHIT Certifi ed reg 2011 products|CCHIT Chang I Hwang H Hung M Lin M amp Yen D C (2007) Factors affecting the adoption of

electronic signature Executivesrsquo perspective of hospital information department Decision Support Systems 44 350ndash359

Chaudhry B Wang J Wu S Maglione M Mojica W Roth E et al (2006) Systematic review Impact of health information technology on quality effi ciency and costs of medical care Annals of Internal Medicine 144 742ndash752


Claxton G (2002) How private insurance works A primer The Kaiser Family Foundation Consoli D amp Mina A (2009) An evolutionary perspective on health innovation systems Journal

of Evolutionary Economics 19 297ndash319 Conway P H White P J amp Clancy C (2009) The public role in promoting child health infor-

mation technology Pediatrics 123 S125 Cortner D M (2006) Stages of Internet adoption in preventive health An exploratory diffusion

study of a community-based learning venue for 50+ year-old adults Ann Arbor 1001 Coughlin J DrsquoAmbrosio L A Reimer B amp Pratt M R (2007) Older adult perceptions of

smart home technologies Implications for research policy amp market innovations in healthcare Proceedings of IEEE Engineering in Medicine and Biology Society 2007 1810ndash1815

Cusack C M Pan E Hook J M Vincent A Kaelber D C amp Middleton B (2008) The value proposition in the widespread use of telehealth Journal of Telemedicine and Telecare 14 167

Daim T U Tarman R T amp Basoglu N (2008) Exploring barriers to innovation diffusion in health care service organizations An issue for effective integration of service architecture and information technologies In Hawaii International Conference on System Sciences (p 100) Los Alamitos CA IEEE Computer Society


32

David Y (1993) Technology evaluation in a US hospital The role of clinical engineering Medical and Biological Engineering and Computing 31 HTA28ndashHTA32

Davies L Drummond M amp Papanikolaou P (2001) Prioritizing investments in health technol-ogy assessment International Journal of Technology Assessment in Health Care 16 73ndash91

Deng L amp Poole M S (2003) Learning through telemedicine networks In Proceedings of the 36th Annual Hawaii International Conference on System Sciences ( HICSSrsquo03 )mdash Track 6mdashVolume 6 (p 1741) IEEE Computer Society

DesRoches C M Campbell E G Rao S R Donelan K Ferris T G Jha A et al (2008) Electronic health records in ambulatory caremdasha national survey of physicians The New England Journal of Medicine 359 50

Djellal F amp Gallouj F (2007) Innovation in hospitals A survey of the literature The European Journal of Health Economics 8 181ndash193

DoDAF Architecture Framework Version 20 Dorr D Wilcox A Burns L Brunker C Narus S amp Clayton P (2006) Implementing a

multidisease chronic care model in primary care using people and technology Disease Management 9 (1) 1ndash15

Dorr D A Wilcox A Donnelly S M Burns L amp Clayton P D (2005) Impact of generalist care managers on patients with diabetes Health Services Research 40 1400ndash1421

Drummond M (1994) Evaluation of health technology Economic issues for health policy and policy issues for economic appraisal Social Science and Medicine (1982) 38 1593

Duyck P Pynoo B Devolder P Voet T Adang L amp Vercruysse J (2006) User acceptance of a picture archiving and communication systemmdashApplying the unifi ed theory of acceptance and use of technology in a radiological setting Nuklearmedizin 45 139ndash143

Eden K B (2002) Selecting information technology for physiciansrsquo practices A cross-sectional study BMC Medical Informatics and Decision Making 2 4

Eley R Soar J Buikstra E Fallon T amp Hegney D (2009) Attitudes of Australian nurses to information technology in the workplace A national survey Computers Informatics Nursing 27 114

Fattal J amp Lehoux P (2008) Health technology assessment use and dissemination by patient and consumer groups Why and how International Journal of Technology Assessment in Health Care 24 473ndash480

Federal Enterprise Architecture Fonkych K (2006) Accelerating adoption of clinical IT among the healthcare providers in United

States Strategies and policies The Pardee Rand Graduate School Ford E W McAlearney A S Phillips M T Menachemi N amp Rudolph B (2008) Predicting

computerized physician order entry system adoption in US hospitals Can the federal mandate be met International Journal of Medical Informatics 77 539ndash545

Ford E W Menachemi N amp Phillips M T (2006) Predicting the adoption of electronic health records by physicians When will health care be paperless Journal of the American Medical Informatics Association 13 106ndash112

Furukawa M F Raghu T S Spaulding T J amp Vinze A (2008) Adoption of health informa-tion technology for medication safety in US hospitals 2006 Health Affairs 27 865

Gagnon M Lamothe L Fortin J Cloutier A Godin G Gagne C et al (2004) The impact of organizational characteristics on telehealth adoption by hospitals In System Sciences 2004 Proceedings of the 37th Annual Hawaii International Conference on 2004 (p 10)

Granoff M J (2002) An examination of factors that infl uence the healthcare professionalsrsquo intent to adopt practice guideline innovation Dissertation Abstracts International Section B The Sciences and Engineering

Greenhalgh T Stramer K Bratan T Byrne E Mohammad Y amp Russell J (2008) Introduction of shared electronic records Multi-site case study using diffusion of innovation theory British Medical Journal 337 a1786

HR 1 American recovery and reinvestment act of 2009 (GovTrackus)


33

Hackbarth G amp Milgate K (2005) Using quality incentives to drive physician adoption of health information technology Health Affairs 24 1147ndash1149

Healthcare payers and providers Vital signs for software development 2004 HealthIThhsgov Health IT adoption Hersh W (2004) Health care information technology Progress and barriers Journal of the

American Medical Association 292 2273ndash2274 Higa K Shin B amp Au G (1997) Suggesting a diffusion model of telemedicinemdashFocus on

Hong Kongrsquos case In Hawaii International Conference on System Sciences (p 156) Los Alamitos CA IEEE Computer Society

Hikmet N Bhattacherjee A Menachemi N Kayhan V O amp Brooks R G (2008) The role of organizational factors in the adoption of healthcare information technology in Florida hos-pitals Health Care Management Science 11 1ndash9

Hough M amp Kobylanski A (2009) Increasing elder consumer interactions with information technology Journal of Consumer Marketing 26 39ndash48

Jha A K Bates D W Jenter C A Orav E J Zheng J amp Simon S R (2007) Do minority- serving physicians have comparable rates of use of electronic health records AMIA Symposium 993

Jha A K Doolan D Grandt D Scott T amp Bates D W (2008) The use of health information technology in seven nations

Katsma C P Spil T A M Light E amp Wassenaar A (2007) Implementation and use of an electronic health record Measuring relevance and participation in four hospitals

Katsma C P Spil T A Ligt E amp Wassenaar A (2007) Implementation and use of an elec-tronic health record Measuring relevance and participation in four hospitals International Journal of Healthcare Technology and Management 8 625ndash643

Kaufman M Joshi S amp OrsquoDonnell E (2009) Itrsquos all about the timing While implementing technologies throughout your hospitalrsquos supply chain has been identifi ed as an avenue of improvement determining the right time for adoption and the appropriate methods for calculat-ing the return on investment are not quite that easy Supply Chain

Kazley A S amp Ozcan Y A (2007) Organizational and environmental determinants of hospital EMR adoption A national study Journal of Medical Systems 31 375ndash384

Kimberly J R amp Evanisko M J (1981) Organizational innovation The infl uence of individual organizational and contextual factors on hospital adoption of technological and administrative innovations The Academy of Management Journal 24 689ndash713

Koch J amp Kim C (1998) Business objectives hospital characteristics and the uses of advanced information technology In Proceedings Pacifi c Medical Technology Symposium-PACMEDTek Transcending Time Distance and Structural Barriers (Cat No98EX211) Honolulu HI (pp 68ndash78)

Kolodner R M Cohn S P amp Friedman C P (2008) Health information technology Strategic initiatives real progress Health Affairs 27 w391

Kruchten P (1995) Architectural blueprintsmdashThe ldquo4+ 1rdquo view model of software architecture IEEE Software 12 42ndash50

Kuo C amp Chen H (2008) The critical issues about deploying RFID in healthcare industry by service perspective In Hawaii International Conference on System Sciences (p 111) Los Alamitos CA IEEE Computer Society

Leonard K J (2004) The role of patients in designing health information systems The case of applying simulation techniques to design an electronic patient record (EPR) interface Health Care Management Science 7 275ndash284

Leu M G Cheung M Webster T R Curry L Bradley E H Fifi eld J et al (2008) Centers speak up The clinical context for health information technology in the ambulatory care setting Journal of General Internal Medicine 23 372ndash378

Lin C Tan B amp Chang S (2008) An exploratory model of knowledge fl ow barriers within healthcare organizations Information and Management 45 331ndash339


34

Linstone H A (1999) Decision making for technology executives Using multiple perspectives to improved performance BostonLondon Artech House

Linstone H A Mitroff I I amp Hoos I R R The challenge of the 21st century State University of New York Press

Lobach D F amp Detmer D E (2007) Research challenges for electronic health records American Journal of Preventive Medicine 32 104ndash111

Lobach D F Detmer D E amp Supplement (2007) Research challenges for electronic health records

Lorence D P amp Churchill R (2005) Incremental adoption of information security in health-care organizations Implications for document management IEEE Transactions on Information Technology in Biomedicine 9 169ndash173

May C Gask L Atkinson T Ellis N Mair F amp Esmail A (2001) Resisting and promoting new technologies in clinical practice The case of telepsychiatry Social Science and Medicine (1982) 52 1889ndash1901

McCullough J S (2008) The adoption of hospital information systems Health Economics 17 649ndash664

Melnyk B M amp Fineout-Overholt E (2006) Consumer preferences and values as an integral key to evidence-based practice Nursing Administration Quarterly 30 123

Menachemi N (2006) Barriers to ambulatory EHR Who are lsquoimminent adoptersrsquo and how do they differ from other physicians Informatics in Primary Care 14 101ndash108

Menachemi N (2007) Hospital adoption of information technologies and improved patient safety A study of 98 hospitals in Florida

Menachemi N Brooks R G amp Simpson L (2007) The relationship between pediatric volume and information technology adoption in hospitals Quality Management in Health Care 16 146ndash152

Menachemi N Burke D Clawson A amp Brooks R G (2005) Information technologies in Floridarsquos rural hospitals Does system affi liation matter The Journal of Rural Health 21 263ndash268

Menachemi N Burke D E amp Ayers D J (2004) Factors affecting the adoption of telemedi-cinemdashA multiple adopter perspective Journal of Medical Systems 28 617ndash632

Menachemi N Burke D amp Brooks R G (2004) Adoption factors associated with patient safety-related information technology Journal for Healthcare Quality 26 39ndash44

Menachemi N Chukmaitov A Saunders C amp Brooks R G (2008) Hospital quality of care Does information technology matter The relationship between information technology adop-tion and quality of care Health Care Management Review 33 51

Menachemi N Hikmet N Bhattacherjee A Chukmaitov A amp Brooks R G (2007) The effect of payer mix on the adoption of information technologies by hospitals Health Care Management Review 32 102

Menachemi N Matthews M C Ford E W amp Brooks R G (2007) The infl uence of payer mix on electronic health record adoption by physicians Health Care Management Review 32 111

Menachemi N Saunders C Chukmaitov A Matthews M C amp Brooks R G (2007) Hospital adoption of information technologies and improved patient safety A study of 98 hospitals in Florida Journal of Healthcare ManagementAmerican College of Healthcare Executives 52 398

Middleton B Hammond W E Brennan P F amp Cooper G F (2005) Accelerating US EHR adoption How to get there from here Recommendations based on the 2004 ACMI retreat Journal of the American Medical Informatics Association 12

Mojtabai R (2007) Datapoints Use of information technology by psychiatrists and other medical providers Psychiatric Services 58 1261

NAHIT releases HIT defi nitions|News|Healthcare Informatics Park Y amp Chen J V (2007) Acceptance and adoption of the innovative use of smartphone

Industrial Management and Data Systems 107 1349


35

Poon E G Blumenthal D Jaggi T Honour M M Bates D W amp Kaushal R (2004) Overcoming barriers to adopting and implementing computerized physician order entry sys-tems in US hospitals Health Affairs 23 184ndash190

Poon E G Jha A K Christino M Honour M M Fernandopulle R Middleton B et al (2006) Assessing the level of healthcare information technology adoption in the United States A snapshot BMC Medical Informatics and Decision Making 6 1

Poulsen P B Vondeling H Dirksen C D Adamsen S Go P M amp Ament A J (2001) Timing of adoption of laparoscopic cholecystectomy in Denmark and in The Netherlands A comparative study Health Policy 55 85ndash95

Powner D A (2006) Health information technology HHS is continuing efforts to defi ne a national strategy Testimony before the Subcommittee on Federal Workforce and Agency Organization Committee on Government Reform House of Representatives Government Accountability Offi ce (Vol 15 pp 7ndash8)

Reardon J L amp Davidson E (2007) An organizational learning perspective on the assimilation of electronic medical records among small physician practices European Journal of Information Systems 16 681ndash694

Reference model of open distributed processing Wiki Robeznieks A (2005a) Privacy fear factor arises (Cover story) Modern Healthcare 35 6ndash16 Robeznieks A (2005b) Privacy fear factor arises The public sees benefi ts to be had from health-

care IT but concerns about misuse of data emerge in survey Modern Healthcare 35 6 Rosenfeld S Bernasek C amp Mendelson D (2005) Medicarersquos next voyage Encouraging phy-

sicians to adopt health information technology Health Affairs 24 1138ndash1146 Saouli M A (2004) Information technology utilization in mental health services Thesis

(DPA)mdashUniversity of La Verne 2004 Shields A E Shin P Leu M G Levy D E Betancourt R M Hawkins D et al (2007)

Adoption of health information technology in community health centers Results of a national survey Health Affairs 26 1373

Simpson S (2000) Intra-institutional rivalry and policy entrepreneurship in the European union The politics of information and communications technology convergence New Media and Society 2 445

Simpson R L (2007) The politics of information technology Nursing Administration Quarterly 31 354ndash358

Sterman J amp Sterman J D (2000) Business dynamics Systems thinking and modeling for a complex world with CD-ROM Irwin McGraw-Hill

Tang P C Ash J S Bates D W Overhage J M amp Sands D Z (2006) Personal health records Defi nitions benefi ts and strategies for overcoming barriers to adoption Journal of the American Medical Informatics Association 13 121ndash126

The Zachman Frameworktrade The offi cial concise defi nition US Department of Health amp Human Services Centers for Medicare amp Medicaid Services Wainwright W D amp Waring S T (2007) The application and adaptation of a diffusion of inno-

vation framework for information systems research in NHS general medical practice Journal of Information Technology 22 44ndash58

Wilcox A B Dorr D A Burns L Jones S Poll J amp Bunker C (2007) Physician perspec-tives of nurse care management located in primary care clinics Care Management Journals 8 58ndash63



Chapter 3 Methods and Models




31 Proposed Model Overview and Justifi cation

Most classical and modern adoption literature attempts to defi ne awareness of an innovation (aka knowledge) as the main factor effecting diffusion Meaning once awareness occurs followed by a persuasion stage the innovation stands a chance for diffusion This explanation is often incomplete and at best more appropriate for consumer behavior than applicable to organizational (ie hospital) adoption of innovations Therefore a new perspective on diffusion of organizational innovations as product of three parts is needed and this proposal is a step toward such explana-tion awareness plus condition plus capabilities Figure 31 shows questions relevant to each of these three factors and how individual adoptions will accumulate to become diffusion of an innovation Figure 32 compares the data and decision fl ow in existing diffusion models with the one in newly proposed extensions

Figure 33 summarizes the proposed extensions to Rogersrsquo diffusion theory using dynamic capabilities The top part of the diagram shows the stages in the classical Rogersrsquo diffusion theory where adopters move through the stages of knowledge persuasion decision implementation and confi rmation The bottom part of the dia-gram shows the proposed extensions for condition (existence of it) and capability (acquiring and actually using it) Figure 34 shows the state chart for the new diffu-sion view using the proposed extensions Figure 35 shows how using a capability- based view rather than a knowledge-based (awareness) can show precisely how an adopter can be pushed out on the technology adoption life cycle (depending on when the organization is ready to adopt)

38

(Does the organization knowabout this HIT Innovation) Awareness

+

+

+

+

+

+

Awareness

Awareness

Condition Adoption 1

Adoption N

DiffusionAdoption Condition

Condition

Capabilities

Capabilities

Capabilities

(Does the organization have theCompetencies need to adoptthe Innovation)

(Does Adopting the innovationfinanciallyother make sense)

Fig 31 Capability-based diffusion

Fig 32 Flow of diffusion in existing research vs proposed


39

32 Modeling Approach

In researching the HIT diffusion phenomena using system thinking this proposed research has two overarching goals One is ldquoto understandrdquo and the other is ldquoto improverdquo To understand means and refers to all the activities related to

Fig 33 New extensions to Rogersrsquo DOI theory

Adopter Knowledge Persuasion

Condition Capabilities

Decision Implementaton

DiffusionAdopters

Confirmation

Fig 34 Diffusion state chart with new extensions

Fig 35 Time element of capabilities in diffusion

3 Methods and Models

40

investigating and later describing the problem space To improve means and refers to all the activities to use the description and use it to improve the existing condi-tion or problem Naturally various research traditions tools techniques and theo-ries can be used to assist in achieving these two goals (Forrester 1994 ) Figure 36 shows the phases of research model building using system thinking that are appro-priate for the proposed HIT diffusion study ldquoTo understandrdquo includes prototyping modeling documenting and communicating research models and fi ndings ldquoTo improverdquo includes using documentation and communication simulation and changing through new policy or theories Inside each of the boxes in Fig 36 the artifacts used for that activity are listed For example technology management constructs scientifi c theories and research methods are tools for m odeling In the following sections various methods and tools for modeling simulation theoriz-ing and research methods that were investigated as candidate for this research are described and discussed

33 Diffusion Theory

ldquoDiffusion is the process in which an innovation is communicated through certain channels over time among the members of a social systemrdquo (Rogers 2003) This special type of communication is concerned with new ideas It is through this pro-cess that stakeholders create and share information together in order to reach a shared understanding Some researchers use the term ldquodisseminationrdquo for diffusion that is directed and planned In his classic work (Rogers 2003) Rogers identifi es four main elements in the diffusion process that are virtually present in all diffusion research (1) an innovation (2) communication channels (3) over time and (4) social systems The following sections provide an overview of each of these process elements

Fig 36 Phases of research model building using system thinking


41

331 An Innovation

An innovation is a new idea or product perceived useful by an individual or an organization Newness is not measured by the time passed since inception of the idea it is rather the point of time that the individual becomes aware of the perceived benefi ts of the innovation The innovation can have a physical form such as the television or a personal computer Or it can also be entirely composed of informa-tion such as a political view a business idea or a software innovation A method-ological diffi culty exists in that it is not easy to track and evaluate information-based innovations (Rogers 2003)

Innovations encounter different adoption rates For example administrating lemon juice to Navy soldiers in order to prevent illness during long voyages take over a 100 years to be adopted by the Western Navies By contrast youtubecom has reached astronomic number of daily users since its inception in 2005 Understanding Rogersrsquo ldquoperceived attributes of innovationsrdquo helps explain this variance in adoption rates

3311 Relative Advantage

Advantage is defi ned in terms of a benefi t gained Therefore relative advantage in this case is the amount of benefi t realized using the new innovation rather than apply-ing the existing and older solutions This relative advantage can be in the form of economic gain or non-tangible gains such as improved perception safety or peace of mind Relative advantage has a positive effect on an innovation rate of adoption The higher the perceived value of an innovation the faster its adoption rate

3312 Compatibility

Compatibility is referred to as how good of a fi t the new innovation is with the cur-rent structure of values past experiences and needs of candidate adopters An idea that is ill fi t for an organization will face slower adoption rate or may never be adopted For an unfi t innovation to be adopted by an organization it requires the culture and value structure of the adopters to change

3313 Complexity

The extent that an innovation is challenging to use or understand is the complexity attribute of the innovation Innovations that can easily be understood by the majority of population donrsquot require specialized skill and knowledge For example a nontech-nical project manager may have diffi culty understanding the need for adopting a cer-tain technology that would provide the company a competitive advantage Ideas that are simpler and require little or no amount of learning achieve faster adoption rates


42

3314 Trialability

New innovations that can be tried within a restricted scope prior to adoption are said to be trialable The easier it is to try out a new idea the higher the chance of its adop-tion by potential participants The concept of trial has become immensely popular with software innovation Many software vendors allow a close to full product dem-onstration of their products over an extended period of time (usually 30 days) The feeling of uncertainty inherent in adopters can be reduced by a trial of a new innova-tion The new learning can lead to a more rapid adoption

3315 Observability

Observability is the extent that results of an adoption of a new innovation are notice-able by other people The more noticeable innovations are adopted more quickly Observability information is mostly communicated through peer-to-peer networks

332 Recent Diffusion of Innovation Issues

Based on a literature review for criticisms and limitations of diffusion theory some of the more recent issues are listed and described in this section

Diffusion research is spreading from industrial settings to public policy setting as well DOI research was started in industrial and service settings and ever since it has been concentrated in areas of study such as agriculture manufacturing and electronics Success in those fi elds has prompted applying DOI research in areas such as public service and policy innovation for example healthcare and education (Nutley amp Davies 2000 )

Diffusion of innovation is not as linear process as most researches suggest Traditional research has described the DOI process as one that fl ows through the fol-lowing steps research creation dissemination and fi nally utilization These steps describe a more or less linear process Studies have shown that in fact often innovations donrsquot spread throughout the population in such a manner and instead experience vari-ous iterations and loops among the stages (Cousins amp Simon 1996 ) Therefore to have a better understanding of the DOI process the entire picture needs to be evaluated

Interests in diffusion research still remains high Wolfe conducted a literature review on diffusion of innovation from 1989 to 1994 and identifi ed 6240 articles on this topic (Wolfe 1994 ) A similar search was performed by Nutley from 1990 to 2002 that identifi ed 14600 articles (Nutley amp Davies 2000 ) This twofold increase highlights the increasing research interest in this area Increase may be contributed to public policy health and energy and consumer diffusion research


43

Research has not characterized organization innovativeness Structure of inno-vative organizations has been subject of many studies Their ability and attitude toward adopting innovation have been measured in various ways (Damanpour 1988 1991 ) However we yet donrsquot have a characterization of an organization that is more innovative vs one that is slower to adopt innovation (Nutley amp Davies 2000 )

The path diffusion of innovation fl ows is unpredictable Path of diffusion is the stages an innovation passes through from inception to utilization Van de Ven argues that qualitative DOI studies have highlighted that it may be better not to discuss dif-fusion in terms of a predictable or unpredictable path (Vandeven amp Rogers 1988 ) similar to Cousins and Simon argument that diffusion process is not linear To think of the complex process of diffusion in terms of a predictable process may corner us into trying to fi t research into this otherwise incorrect notion of predictability

Innovation type classifi cation To better understand and evaluate the effective-ness of diffusion of innovation itrsquos important to be able to classify types of innova-tions Types can have similarities but also each type may uncover peculiarities that are important to be noted Damanpour and Evens have proposed two simple classi-fi cations fi rst technical vs administrative innovations and second product vs pro-cess innovations (Damanpour amp Evan 1984 ) Wolfe has provided more resolution to innovation types with 17 innovation attributes (Wolfe 1994 ) More recently Osborne has classifi ed social policy innovations (Osborne 1998 )

Innovation adopter decisions are more based on fad and fashion than rationality

A rational decision is one that is made with the desirable outcomes in mind A logi-cal process is followed and is free of peer network pressure and current fashion Research has shown that similar to consumer markets innovation adopters are heav-ily infl uenced by fad and fashion when deciding to adopt (Abrahamson 1991 1996 ) The need for peer acceptance is a large driver of adoption behavior (ONeill Pouder amp Buchholtz 1998 ) To have a correct understanding itrsquos critical to keep this variable in mind when studying and evaluating innovation diffusion

Adoption decision reversal Much of the research has focused on the adoption decision process itself The phenomena of adoption reversal have mostly been neglected Even after making an adoption decision adopters look for continuous reinforcements within their network if they are exposed to negative press they attempt to reverse their adoption decision (Rogers 2003)

Staged diffusion models The most sited model of diffusion is Rogersrsquo fi ve- stage illustration (Rogers 2003) Rogersrsquo model includes the following stages in order knowledge persuasion decision implementation and confi rmation Other authors have proposed variation to Rogersrsquo model to include routinization and infusion (Cooper amp Zmud 1990 ) Routinization occurs when adoption is no longer consid-ered innovative this is normally seen in late adopters Infusion occurs when innova-tion has been adopted by an organization and it has spread strongly within that organization


44

Additional innovation characteristics In his classic work Rogers identifi ed the following innovation attributes relative advantage comparability compatibility trialability and observiblity (Rogers 2003) Building on his work other attributes have been suggested such as adoptability centrality and additional work load (Wolfe 1994 )

Linear-stage model inadequate (innovation journey) Linear models that have so far been defi ned for innovation diffusion are limited Linear models assume tech-nology fl ows from one step to the other in a waterfall manner Based on case studies such as the Minnesota Innovation Research Program (MIRP) the process is more and more being visualized as a journey termed the ldquoinnovation journeyrdquo (Vandeven amp Rogers 1988 ) The new fi ndings show that DOI is non-sequential chaotic and impulsive The new learning highlights that there are no simple solutions but orga-nizations can learn from their past adoption experiences to improve future projects While there are no simple representations of the process and no ldquoquick fi xesrdquo to ensure that it is successful participants who learn from their past experience can increase the odds of their success (Nutley amp Davies 2000 )

Institutional pressure is a large factor in adoption decisions Abrahamson et al introduced administrative innovations as a new type The authors explained how groups adopt or reject administrative innovations They argue that rather than evi-dence institutional pressures coming from certain fads and fashion infl uence the adopter (Abrahamson 1991 1996 Abrahamson amp Fombrun 1994 Abrahamson amp Rosenkopf 1993 )

Decentralized systems are most appropriate (for not highly technical adop-tions) In the newest revision of his book Rogers argues that decentralized systems are best diffused when a high level of new technical learning expertise is not needed and the users are very mixed in expertise and skills (Rogers 2003)

333 Limitations of Innovation Research

According to Nutley (Nutley amp Davies 2000 ) to date Wolfe identifi es the following limitations in innovation research (Wolfe 1994 )

bull Lack of specifi city concerning the innovation stage upon which investigations focus

bull Insuffi cient consideration given to innovation characteristics and how these change over time

bull Research being limited to single-type studies bull Researchers limiting their scope of inquiry by working within single theoretical

perspectives


45

34 Other Relevant Diffusion and Adoption Theories

A macro-level (market-levelecosystem-level) theory such as diffusion theory is better suited for describing activities of multiple fi rms in a space that can have policy implications (Erdil amp Emerson 2008 Otto amp Simon 2009 ) However for example theories such as the technology acceptance model (TAM) are at the indi-vidual (micro) level which is better suited for analyzing the atomic individual deci-sion (can later be built into a market-level theory such as diffusion models) Therefore for the proposed HIT study diffusion theory is the best fi t Table 31 lists other relevant theories relating to adoption and diffusion that were considered before deciding on using diffusion theory for this research The following sections describe each theory in detail and discuss its strength and weakness as relevant to this research effort (Fig 37 )

Table 31 List of relevant diffusion and adoption theories

Name

Main dependent construct

Main independent construct Originating area

Level of analysis

Technology acceptance model (TAM)

Behavioral intention to use system usage

Perceived usefulness perceived ease of use

Information systems

Individual

Theory of reasoned action (TRA)

Behavioral intention behavior

Attitude toward behavior subjective norm

Social psychology

Individual

Theory of planned behavior (TPB)


Attitude toward behavior subjective norm perceived behavioral control

Social psychology

Individual

Unifi ed theory of acceptance and use of technology (UTAUT)

Behavioral intention usage behavior

Performance expectancy effort expectancy social infl uence facilitating conditions gender age experience voluntariness of use

Information systems

Individual

Technology-organization- environment framework (TOEF)

Likelihood of adoption intention to adopt extent of adoption

Technological context Organizational context Environmental context

Organizational psychology

Firmorganization

Matching Person and technology model (MPTM)

Behavior Attitude Social sciences Individual

Lazy user model (LUM)

Behavior Attitude Engineering Individual


46

341 The Theory of Reasoned Action

According to the theory of reasoned action (TRA) an individualrsquos behavior is guided by an individualrsquos attitude along with the subjective norms (Ajzen amp Fishbein 1973 Fishbein 1967 Fishbein amp Ajzen 1975 ) as illustrated in Fig 38 An individualrsquos positive or negative attitude toward conducting a behavior is defi ned as the attitude toward act or behavior Assessing an individualrsquos belief regarding results of acting and desirability of that result determine the attitude Subjective norm is described as whether the individualrsquos environment and other people in it feel itrsquos positive or nega-tive for a behavior to be performed The strength of subjective norm factor on actual behavior of the individual is affected by the level of strength the individual wished to conform to opinions of the others

The TRA model has two important limitations (Eagly amp Chaiken 1993 ) First there can be confusion between attitude and subjective norm since attitudes can often be driven or be products of subjective norms or vice versa The other limita-tion of the model is that it does not consider constraints imposed on individual behavior In other words it assumes free will to behave independent of constraints such as time environment and laws

342 The Technology Acceptance Model

The TAM model is an adaptation of the TRA for the information technology (IT) domain How users reach the point to adopt a technology and use it is explained by TAM TAM hypothesizes that perceived usefulness and perceived ease of use are

Attitude TowardAct or Behavior

BehavioralIntention

Behavior

Subjective Norm

Fig 38 Theory of reasoned action (TRA)

Fig 37 Market level vs fi rm level


47

the determinants for an individualrsquos intention to use a system or not as shown in the top part of Fig 39 (Davis 1985 1989 Davis Bagozzi amp Warshaw 1989 ) Perceived usefulness is defi ned as the degree that an individual believes using a technology would improve hisher performance Perceived ease of use is defi ned as the level an individual believes using a technology would bring himher effi ciently by saving them effort for otherwise needed work Perceived usefulness can also be directly impacted by perceived ease of use

In order to simplify the TAM model researchers have removed the attitude constrict from the original TRA (Venkatesh et al 2003 ) In the literature various efforts have been made to extend TAM which these efforts generally fall into one of the following three categories adding infl uential parameters from other related models adding brand new parameters to the model not found in other models and fi nally examining various infl uences on perceived usefulness and perceived ease of use (Wixom amp Todd 2005 ) The relationship between usefulness ease of use and system usage have been explored since the original work on TAM (Adams Nelson amp Todd 1992 Davis et al 1989 Hendrickson Massey amp Cronan 1993 Segars amp Grover 1993 Subramanian 1994 Szajna 1994 ) Similar to the limitations of TRA TAM also assumes that intention to act is formed free of limitations and constraints such as time environment and capability In addi-tion triviality and lack of practical value have been recently highlighted as limita-tions of TAM (Chuttur 2009 ) The original TAM has been extended to now include social infl uence and instrumental processes in TAM2 (Viswanath Morris Davis amp Davis 2003 )

A Possible Dynamic Capabilitiesextension to TAM

Classic TAM Model

PeroeivedUsefulness

Peroeived Ease of Use

BehavioralIntention to Use

Capabilities toUse Exists

Actual SystemUse

PeroeivedUsefulness



Source Davis et al (1989) Venkatesh et al (2003)

Actual SystemUse

Fig 39 Theory of technology acceptance model (TAM)


48

As explained earlier for the proposed study the methodology of choice is diffu-sion theory since it provides a macro-level view However dynamic capabilities can also be integrated with the TAM model For example as shown in the bottom part of Fig 39 a new ldquocapabilities to use existrdquo construct can be added to the classic TAM which would infl uence the existing ldquobehavioral intentions to userdquo or ldquoactual system userdquo constructs One of the main diffi culties in this integration is that unlike diffu-sion theory TAM does not provide a way to describe a time element

343 The Theory of Planned Behavior

The theory of planned behavior (TPB) model states that an individualrsquos behavior is powered by behavioral intentions which are infl uenced by attitude subjective norm and perceptions of ease of use as in Fig 310 (Ajzen 1985 1991 ) The originating fi eld for this theory is psychology and it was proposed as an extension to TRA Similar to the components of TRA model an individualrsquos positive or negative attitude toward performing a behavior is defi ned as the attitude toward act or behavior Subjective norm is described as whether the individualrsquos environment and other people in it feel itrsquos positive or negative for a behavior to be performed Behavioral control is described as an individualrsquos perception of how diffi cult it is to perform an act or behavior

344 The Unifi ed Theory of Acceptance and Use of Technology

The unifi ed theory of acceptance and use of technology (UTAUT) was developed to explain the individualrsquos intentions in using an information system and its resulting behavior as in Fig 311 UTAUT was developed based on the combination of com-ponents identifi ed by previous models including theory of reasoned action TAM motivational model theory of planned behavior a combined theory of planned behaviortechnology acceptance model model of PC utilization innovation


Subjective NormBehavioralIntention Behavior

Source Ajzen (1991)

PerceivedBehavioral

Control

Fig 310 Theory of planned behavior (TPB)


49

diffusion theory and social cognitive theory Its hypostasis that the four constructs of performance expectancy effort expectancy social infl uence and facilitating con-ditions can explain usage intention and resulting behavior (Viswanath et al 2003 ) Gender age experience and voluntariness of use were identifi ed as other important parameters in explaining usage and behavior (Viswanath et al 2003 )

345 Matching Person and Technology Model

Matching person and technology model (MPTM) is a way to organize infl uences on the successful adoption and use of technologies in systems in settings such as the workplace home and healthcare settings Research has shown that a well- intentioned technology may not arrive at its full potential if the important personal-ity preference psychosocial characteristics or necessary environmental support critical are not considered An MPTM assessment can help match individuals with the most appropriate technologies for their intended use (Scherer 2002 )

346 Technology-Organization-Environment Framework (TOE)

TOEF framework identifi es technological organizational and environmental contexts as the components of the processes by which fi rms adopt and use technological inno-vations (Tornatzky amp Fleischer 1990 ) The scope of technological context includes both external and internal artifacts relevant to the fi rm Both physical equipments and processes are part of the technological context Organizational context includes the

UseBehavior

BehavioralIntention

Voluntarinessof Use

ExperienceAgeGender

PerformanceExpectancy

EffortExpectancy

SocialInfluence

FancilitatingConditions

Fig 311 The unifi ed theory of acceptance and use of technology


50

characteristics of the fi rm fi rm size degree of centralization managerial structure and the likes The environment context can include the size and structure of the market ecosystem including competition regulations and more

347 Lazy User Model

Similar to the TAM lazy user model (LUM) attempts to describe the process that individuals use to select a solution for satisfying a need from a series of alternatives (Collan amp Teacutetard 2007 ) LUM hypothesizes that from a set of available solutions the user always attempts to select the one with the least amount of effort

The model starts by assuming that the user has a need that is defi nable and satisfi able Then the set of possible solutions are defi ned by the user need Each solution in the set has its own characteristics which meet the user need in varying degrees The user state further determines the available solutions For example to check an address for a restaurant an individual can use the Internet or a tele-phone But if this individual is driving and is without an Internet connection heshe can either call the phone directory to get the restaurant phone number or phone a friend for directions Therefore as in this example the user state is deter-mined by the users and their situation characteristics at any given time

The LUM model assumes that after the user need and user state have defi ned the set of possible solutions the user will select a solution Worth mentioning that if the set is empty the user does not have a way to satisfy the need The LUM hypothesizes that the use will select a solution from the limited set based on lowest level of effort Effort is defi ned as aggregate of monetary cost + time needed + physical andor mental efforts necessary to satisfy the user need (Tetard amp Collan 1899 )

35 Resource-Based Theory Invisible Assets Competencies and Capabilities

As described in the earlier sections of this document dynamic capabilities are one of the main constructs that are being proposed for extending diffusion theory for HIT adoption What is specifi cally referred to as dynamic capabilities is also generally discussed by researchers through other explanations such as competencies factors of production assets and more The roots of almost all of these variations can be traced back to resource-based theory (RBT) Before deciding on dynamic capabili-ties it was important to review and compare all the variations of so-called factors of production Almost any of the variations would be useable for the proposal since itrsquos merely intended to demonstrate the existence of organizational ability (capabil-ity) However since adoption of HIT would require obtaining new abilities or recon-fi guring existing abilities this is most consistent with the dynamic qualifi cation of dynamic capabilities


51

Strategic management researchers attempt to understand differences in fi rm per-formance by asking the question ldquoWhy do some fi rms persistently outperform othersrdquo(Barney amp Clark 2007 ) Understanding this point has traditionally been looked at from a strategic management point of view in the context of creating com-petitive advantage or diversifying the corporate portfolio But interesting enough studying the differences in this performance can also help us understand diffusion of innovation In this context one of the major goals of research industry society and especially government is the accelerated diffusion of information in healthcare technology So knowing how why and which fi rms outperform others would allow the stakeholders involved to make better policy and plan more precisely It is in this context that this research proposes using dynamic capabilities to model diffusion of HIT In order to better understand its importance it is useful to look at the history of this research how it developed and what alternative candidates to dynamic capa-bilities there are This is done in the following sections by reviewing the foundations of RBT seminal work in the area variations classifi cations and limitations

351 Foundations of Resource-Based Theory

Firmsrsquo outperforming other fi rms has been explained using two explanations in the literature (Barney amp Clark 2007 ) The fi rst is attributed to Porter (Porter 1981 Porter Michael 1979 ) and is based on structure-conduct-performance (SCP) theory from industrial organization economics (Bain 1956 ) This perspective argues that a fi rmrsquos market power to increase prices above a competitive level creates the superior performance (Porter 1981 ) The second explains superior performance through the differential ability of those fi rms to more rapidly and cost effectively react to cus-tomer needs (Demsetz 1973 ) This perspective suggests that it is resource intensive for fi rms to copy more effi cient fi rms hence this causes the superior performance to persist between the haves and the have-nots (Rumelt amp Lamb 1984 )

In RBD Barney acknowledges that these two explanations are not contradictory and each applies in some settings While also acknowledging the roll of market power in explaining sustained superior performance Barney chooses to ignore it and instead focus on ldquoeffi ciency theories of sustained superior fi rm performancerdquo (Barney amp Clark 2007 )

Four sources contribute to theoretical underpinnings of RBD (Barney amp Clark 2007 ) (a) distinctive competencies research (b) Ricardorsquos analysis of land rents (c) Penrose 1959 (Penrose 1959 ) and (d) studies of antitrust implications of economics Of the four parts only distinctive competencies and Penrosersquos work are related to this proposed research and will be explained in more detail in the following subsections

3511 Distinctive Competencies

A fi rmrsquos distinctive competencies are the characteristics of the fi rm that enable it to implement a strategy more effi ciently than other fi rms (Hitt amp Ireland 1985a 1986 Hrebiniak amp Snow 1982 Learned Christensen Andrews amp Guth 1969 ) One of


52

the early distinctive competencies that researchers identifi ed was ldquogeneral manage-ment capabilityrdquo The thinking was that fi rms that employ high-quality general man-agers often outperform fi rms with ldquolow-qualityrdquo general managers However it is now understood that this perspective is severely limited in explaining performance difference among fi rms First the qualities and attributes that constitute a high- quality general manager are ambiguous and diffi cult to identify (a platter of research literature has shown that general managers with a wide array of styles can be effec-tive) Second while general management capabilities are important itrsquos not the only competence critical in the superior performance of a fi rm For example a fi rm with high-quality general managers may lack the other resources ultimately necessary to gain competitive advantage (Barney amp Clark 2007 )

3512 Penrose 1959

In the work The Theory of the Growth in 1959 Penrose attempted to understand the processes that lead to fi rm growth and its limitations (Penrose 1959 ) Penrose advocated that fi rms should be conceptualized as follows fi rst an administrative framework that coordinates activities of the fi rm and second as a bundle of produc-tive resources Penrose identifi ed that the fi rmrsquos growth was limited by opportuni-ties and the coordination of the fi rm resources In addition to analyzing the ability of fi rms to grow Penrose made two important contributions to RBD (Barney amp Clark 2007 ) First Penrose observed that the bundle of resources controlled can be different from fi rm to fi rm in the same market Second and most relevant to this research proposal Penrose used a liberal defi nition for what might be considered a productive resource including managerial teams top management groups and entrepreneurial skills

352 Seminal Work in Resource-Based Theory

Four seminal papers constituted the early work on RBT these included Wernerfelt (1984) Rumelt (1984) Barney (1986) and Dierickx (1989) (Barney 1986 Dierickx amp Cool 1989 Rumelt amp Lamb 1984 Wernerfelt 1984 ) These papers made it pos-sible to analyze fi rmrsquos superior performance using resources as a unit of analysis They also explained the attributes resource must have in order to be source of sus-tained superior performance

Using the set of resources a fi rm holds and based on the fi rmrsquos product market position Wernerfelt developed a theory for explaining competitive advantage (Wernerfelt 1984 ) that is complementary to Porters (Porter 1985 ) Wernerfelt labeled this idea resource-based ldquoviewrdquo since it looked at the fi rmrsquos competitive advantage from the perspective of the resources controlled by the fi rm This method argues that the collection of resources a fi rm controls determines the collections of product market positions that the fi rm takes


53

Around the same time as Wernerfelt Rumelt published a second infl uential paper that tried to explain why fi rms exist based on being able to more effi ciently generate economic rents than other types of economic organizations (Rumelt amp Lamb 1984 ) An important contribution of Rumelt to RBD was that he described fi rms as a bun-dle of productive resources

In a third paper similar to Wernerfelt Barney recommended a superior perfor-mance theory based on attributes of the resources a fi rm controls (Barney 1986 Wernerfelt 1984 ) However Barney additionally argued that a theory based on product market positions of the fi rms can be very different than the pervious and therefore a shift from resource-based view to the new RBD (Barney amp Clark 2007 ) In a fourth paper Dierickx and Cool supported Barneyrsquos argument by explaining how it is that the resources already controlled by fi rm can produce economic rents for it (Dierickx amp Cool 1989 )

353 Invisible Assets and Competencies Parallel Streams of ldquoResource-Based Workrdquo

While RBD was shaping into its own other research streams were developing theories about competitive advantage that have implications to this proposed research since they were also looking at competencies and capabilities The most infl uential were the theory of invisible assets by Itami and Roehl ( 1987 ) and competence-based theo-ries of corporate diversifi cation (Hamel amp Prahalad 1990 Prahalad amp Bettis 1986 )

Itami described sources of competitive power by classifying physical (visible) assets and invisible assets Itami identifi ed information-based resources for exam-ple technology customer trust and corporate culture as invisible assets and the real source of competitive advantage while stating that the physical (visible) assets are critical to business operations but donrsquot contribute as much to source of competitive advantage Firms are both accumulators and producers of invisible assets and since it is diffi cult to obtain them having them can lead to competitive advantage Itami classifi ed the invisible assets into environment corporate and internal categories Environmental information fl ows from the environment to the fi rms such as cus-tomer information Corporate information fl ows from the fi rm to its ecosystem such as corporate image Internal information rises and gets consumed within the fi rm such as morale of workers

In another parallel research stream Teece and Prahalad et al (Prahalad amp Bettis 1986 Teece 1980 ) had started looking at resource-based logic to describe corporate diversifi cation Prahalad in particular stresses the importance of sharing intangible assets and its impact on diversifi cation Prahalad and Bettis called these intangible assets the fi rmrsquos dominant logic ldquoa mindset or a worldview or conceptualization of the business and administrative tools to accomplish goals and make decisions in that busi-nessrdquo Hamel and Prahalad ( 1990 ) extended dominate logic into the corporation ldquocore competence rdquo meaning ldquothe collective learning in the organization especially how to coordinate diver production skills and integrate multiple streams of technologiesrdquo


54

354 A Complete List of Terms Used to Refer to Factors of Production in Literature

For the purposes of this proposal the various forms of factors of production have been extracted from literature and presented here in Table 32 The table includes the name of the view its source and some brief notes

Table 32 List of names used for factors of production in literature

Nameunit Source Notes

1 Firmrsquos distinctive competencies

Learned et al ( 1969 ) Hrebiniak and Snow ( 1982 ) Hitt and Ireland ( 1985a 1985b ) Hitt and Ireland ( 1986 )

Aka general management capability

2 Factors of production

Ricardo ( 1817 ) For example the total supply of land

3 Bundle of productive resources

Penrose ( 1959 ) Managers exploit the bundle of productive resources controlled by a fi rm through the use of the administrative framework that had been created in a fi rm

4 Invisible assets and physical (visible) assets

Itami and Roehl ( 1987 )

Invisible assets are necessary for competitive success Physical (visible) assets must be present for business operations to take place

5 Shared intangible assets (called fi rmrsquos dominant logic)

Prahalad and Bettis ( 1986 )

A mindset or a worldview or conceptualization of the business and administrative tools to accomplish goals and make decisions in that business

6 Corporationrsquos ldquocore competencerdquo

Hamel and Prahalad ( 1990 )

The collective learning in the organization especially how to coordinate diverse production skills and integrate multiple streams of technologies

7 Resources Barney ( 1991 Wernerfelt ( 1984 )

Simply called these assets ldquoresourcesrdquo and made no effort to divide them into any fi ner categories

8 Capabilities Stalk Evans and Shulman ( 1992 )

Argued that there was a difference between competencies and capabilities

9 Dynamic capabilities

Teece Pisano and Shuen ( 1997 )

The ability of fi rms to develop new capabilities

10 Knowledge Grant ( 1996 Liebeskind 1996 Spender and Grant 1996 )

Knowledge-based theory

11 Firm attributes Barney and Clark ( 2007 )

A causal reference to factors of production

12 Organizational capabilities (organizational routines)

Nelson and Winter ( 1982 )

Organizational routines are considered basic components of organizational behavior and repositories of organizational capabilities


55

355 Typology and Classifi cation of Factors of Production

A variety of researchers have created typologies of fi rm resources competencies and capabilities (Amit amp Schoemaker 1993 Barney amp Clark 2007 Collis amp Montgomery 1995 Grant 1991 Hall 1992 Hitt Hoskisson amp Kim 1997 Hitt amp Ireland 1986 Thompson amp Strickland 1983 Williamson 1975 )

36 Modeling Component Descriptions

During research when modeling ecosystems or problem domains for the purposes of system analysis a variety of complementary and sometimes redundant methods exist Choosing the right combination is important and is a multistep process First the need for problem analysis or modeling has to be clear Second a set of alterna-tive solutions needs to be developed and third well-suited combination of tools needs to be picked to demonstrate the problemsolution In order to be able to effectively execute these three steps the researcher needs to be familiar with the tools of the trade Figure 312 shows the building blocks of these tools and the relationships among them A description of each of these building blocks follows in this section

Fig 312 Research and modeling components and their relationships


56

361 Model

A model is a miniature representation or description created to show the structural components of a problem and their interactions They are often limited replicas of real-ity and are used to assist in understanding complex ideas for further studies Models come in a variety of formats including textual mathematical graphical and hybrid

362 Diagram

A diagram is a symbolic representation of information used for visualization pur-poses A diagram is almost always graphical and shows collection(s) of objects and relationships Often the terms model and diagram are incorrectly used in an inter-changeable manner Diagrams can be part of a model however models are usually collection of multiple types of information including text and graphics Models are used to understand problems and are multiple perspectives while diagrams are used to show a specifi c window on an issue

363 View

A view is a representation of a system from a particular perspective Views or view-point frameworks are techniques from systems engineering and software engineer-ing which describe a logical set of related matters to be used during systems analysis and development A view can be part of a model and diagrams can be used to help further elaborate a view However views donrsquot exist without being part of a model or are rendered meaningless that way

364 Domain

Domain is a set of expertise or applications that assist us in defi ning and solving everyday problems Software engineering and healthcare are two examples of domains

365 Modeling Language

A modeling language is an artifi cial language that describes a set of rules which are used to describe structures of information or systems The rules are what provide meaning and description to the various artifacts for example in a graphical


57

diagram Modeling languages are usually graphical or textual Diagrams contain-ing symbols and lines are usually graphical modeling languages such as Unifi ed Modeling Language (UML) and textual modeling languages use mechanisms such as standardized keywords or other constructs to create understandable expressions

An important point to keep in mind is that not all modeling languages are execut-able For example although UML can be used to generate parts of code itrsquos not executable whereas graphical models such as stock and fl ow diagrams from system dynamics models (even though analysis wise much less descriptive than UML dia-grams) are an executable model Executable models are given values as inputs and after calculations they are able to provide results as outputs

366 Tool

In a general sense a tool is an object that interfaces between two or more domains It enables a useful action from one domain on another For example a system dynamics model which is a tool from the engineering domain can act as an interface for a problem in the healthcare domain

367 Simulation

Simulation is the reproduction of a concept that may be rooted in reality a process or an organization etc Simulation requires modeling key behavior and characteris-tics of the targeted system Simulation is often used to show eventual results of alternative paths or solutions

37 Modeling Technique Trade-Off Analysis for Proposed HIT Diffusion Study

For the proposed HIT diffusion study the following modeling needs can be identifi ed

bull Decompose the HIT adoption ecosystem into actors behaviors etc bull Look at the HIT adoption and diffusion process from various perspectives bull Look at the behavior such as relationships and data exchanged between the

actors bull Document the model bull Simulate or forecast over time


58

Table 33 Need vs solution matrix

UML Theories Systems science and system dynamics

Qualitative methods

Understand and model Actors X X Actor behavior X X Relationships X X Flow of info X X Decisions X X Capabilities X X Policy X X Other X X Prototype Structure X X Behavior X X Model X Simulate Scenarios X X X Model X X Decisions X X Policy X Time X Facilitator and barriers X

bull Prototype bull Communicate the model

In each row of Table 33 the needs mentioned above are shown with more detail The columns list the domain or fi eld that would be used to satisfy that need It is effectively a need vs solution matrix which describes for example UML will be used to prototype structure

Table 34 is an exhaustive list of potential modeling techniques methodologies and tools from softwaresystems engineering and technology management relevant to analyzing and simulating models Members of list that were more relevant to the research are described in detail in the following sections and they include soft sys-tem methodology (SSM) structured system analysis and design method (SSADM) business process modeling (BPM) system dynamics system context diagrams (SCD) data fl ow diagrams (DFDs) fl ow charts UML and Systems Modeling Language (SysML) These tools were examined for applicability in detail before deciding to use the combination listed in Table 33


59

Table 34 List of relevant system modeling techniques

Full name Abbreviation

Soft systems methodology SSM Business process modeling BPM Systems engineering ndash Software engineering ndash Software development methodology ISDM System development methodology ndash Structured systems analysis and design method SSADM Dynamic systems development method DSDM Structured analysis SA Software design SD Soft systems methodology SSM Structured design ndash Yourdon structured method ndash Jackson structured programming ndash Structured analysis ndash WarnierOrr diagram ndash Soft OR ndash System dynamics ndash Systems thinking ndash General-purpose modeling GPM Graphical modeling languages ndash Algebraic modeling language ndash Domain-specifi c modeling language ndash Framework-specifi c modeling language ndash Object modeling languages ndash Virtual reality modeling languages ndash Fundamental modeling concepts FMC Flow chart ndash Object role modeling ndash Unifi ed modeling language UML Model-driven engineering MDE Model-driven architecture MDA Systems modeling language SysML Functional fl ow block diagram FFBD Mathematical model ndash Functional fl ow block diagram (FFBD) FFBD Data fl ow diagram (DFD) DFD n2 (n-squared) chart ndash idef0 diagram ndash Universal systems language function maps and type maps USL The open group architecture framework TOGAF The British Ministry of Defence Architectural Framework MODAF

(continued)


60

371 Soft System Methodology

Developed by academics at the University of Lancaster Systems Department in the late 1960s SSM is a means to organizational process modeling or also known as BPM (van de Water Schinkel amp Rozier 2006 ) In SSM researchers start with a real-world situation and study the situation in a pseudo-unstructured approach Subsequently rough models of the situation are developed SSM develops specifi c perspectives on the situation builds models from these perspectives and iteratively compares it to the real life (Williams 2005 ) SSM is comprised of seven stages that address the real and conceptual world for the situation under study (Finegan 2003 ) SSM is most useful when the situation under analysis contains multiple stakeholder goals assumptions and perspectives and if the problem is extremely entangled

SSM tries to address many perspectives as a whole and this leads to a complex challenge Clarity is best achieved when addressing key perspectives separately and integrating fi nding from multiple perspectives downstream to this end Checkland developed the mnemonic CATWOE to help (Checkland 1999 Checkland amp Scholes 1990 ) The new tool proposed that the starting point of situation analysis is a transformation (T) asking the question that from a given perspective what is actually transformed moving from input to output Once the transformation has been identifi ed research can proceed to identify other elements of the system (Williams 2005 )

bull Customers who (or what) benefi t from this transformation bull Actors who facilitate the transformation to these customers bull Transformation from ldquostartrdquo to ldquofi nishrdquo bull Weltanschauung what gives the transformation some meaning bull Owner to whom the ldquosystemrdquo is answerable andor could cause it not to exist bull Environment that infl uences but does not control the system



Zachman framework ndash Performance moderator function (PMF) models ndash Human behavior models ndash System dynamics ndash Ecosystem model ndash Wicked problem ndash Operations research ndash Stock and fl ow diagrams ndash Causal loop diagrams ndash Dynamical system ndash


61

372 Structured System Analysis and Design Method

SSADM was developed as a systems approach for the Offi ce of Government Commerce of the UK in the 1980s for the analysis and design of information sys-tems (Robinson amp Berrisford 1994 ) SSADM is comprised of three layers for (1) logical data modeling for modeling the system data requirements (2) data fl ow modeling for documenting how data moves around and (3) entity behavior model-ing to identify events that affect each entity ( SSADM Diagram Software Structured Systems Analysis and Design Methodology ) Figure 322 shows a sample DFD drawn using the SSADM style SSADM consists of fi ve stages which include ( SSADM Diagram Software Structured Systems Analysis and Design Methodology )

Feasibility study A high-level analysis of the situation to a business area to under-stand whether developing a system is feasible Data Flow modeling and (high- level) logical data modeling techniques are used during this stage

Requirement analysis Requirements are identifi ed and the environment is mod-eled Alternative solutions are proposed and a particular option is selected to be further refi ned Data fl ow modeling and logical data modeling technique are used during this stage

Requirement specifi cation Functional and nonfunctional requirements are described

Logical system specifi cation The development and implementation environment is described

Physical design The logical system specs and technical specs are used to create and design a program

373 Business Process Modeling

In systems and software engineering BPM is the activity of describing the enter-prise processes for analysis BPM is often performed to improve process effi -ciency and quality and often involves information technology Newly arriving applications from large-platform vendors make some inroads for allowing BPM models to become executable and capable of use for simulations (Smart Maddern amp Maull 2008 )

374 System Dynamics (SD)

Created during the mid-1950s by Professor Jay Forrester of the Massachusetts Institute of Technology system dynamics is a modeling tool that allows us to build formal computer simulation of complex problem Examples of system dynamics application include studying corporate growth diffusion of new technologies and policy forecasting System dynamics helps us understand better in what ways the


62

fi rmrsquos performance is related to its internal structure (Hendrickson et al 1993 ) SD roots are in control theory and the modern theory of nonlinear dynamics System dynamics is the preferred choice for studying systems at a high level of abstraction where agent-based simulation is better suited for studying phenomena at the level of individuals or other micro levels (Wakeland et al 2004 ) The main components of a system dynamic model include a causal loop diagram (CLD) stock and fl ow dia-gram and its mathematical equations

3741 Causal Loop Diagram

A CLD is a visual illustration of the feedback structures in a system A CLD shows variables connected with arrows illustrating causal infl uences among them CLD can be used for quickly capturing a hypothesis about dynamics of the situation capturing mental models of stakeholders and communicating important feedback that are responsible for the problem being studied CLDs do not show accumulation of resource or rates of change in system that will be in stock and fl ows An example CLD is shown in Fig 313 (Behkami 2009 )

3742 Stock and Flow Diagram

In system dynamics after creating a CLD the next step is to create a stock and fl ow diagram Stocks are accumulations (they characterize the state of the system) and fl ows are rate of accumulation or depletion over time Stocks can create delays by accumulat-ing difference in infl ow versus outfl ow Figure 314 shows a stock and fl ow diagram for a Bass diffusion model Figure 315 shows a sample output for adoption rates from the stock and fl ow diagram in Fig 314 And Fig 316 is a snippet of the differential equi-tations (the behind the scene parts) of the same system dynamics model

375 System Context Diagram and Data Flow Diagrams and Flow Charts

SCD are used to represent external objects or actors that interact with a system (Kossiakoff amp Sweet 2003 ) An SCD illustrates a macro view of a system under investigation showing the whole system with its inputs and outputs related to exter-nal objects This type of diagram is system centric with no details of its interior

LargePotentialAdaptors

SmallPotentialAdaptors

Adaptors Fig 313 Adopter population


63

PotentialAdopters

P

Total LargePractice Population

N

AdoptionFraction

i

Contact Ratec

MarketSaturation

AdvertisingEffectiveness

a

Adoption fromAdvertising inConferences

B

B

R

MarketSaturation

Adoption RateAR

Word ofMouth

AdoptersA

Adoption fromInstitutional word of

Mouth

+

+

+

+ +

+

-

+

+

Fig 314 Bass diffusion model with system dynamics

20

10100

00

0 10 20 30 40 50

Time (Month)Adoption from Advertising in Conferences CurrentAdoption from Institutional word of Month Current

60 70 80 90 100

200 Fig 315 Sample system dynamics output graph

structure but bounded by interactions and an external environment (Kossiakoff amp Sweet 2003 ) SCD are related to DFD they both show interactions among systems and actors They are often used in the initial phases of problem analysis in order to build consent between stakeholders The building blocks of context diagrams include labeled box and relationships

To describe fl ow of data in a graphical representation DFD is used (Stevens Myers amp Constantine 1979 ) DFDs donrsquot provide information about sequence of operations or timing DFDs are different from fl ow charts since the latter describe fl ow of control in a situation However unlike DFDs fl ow charts donrsquot show the details of data that is fl owing in the situation (Stevens et al 1979 ) On a DFD data items fl ow from an external data source or an internal data store to an internal data store or an external data sink via an internal process


64

Fig 316 System dynamics sample code

376 Unifi ed Modeling Language

UML is a general-purpose modeling language that is a widely accepted industry standard created and managed by the Object Management Group for Software Engineering problems ( UML 20 ) UML is comprised of a set of graphical notation


65

techniques to create model of software systems UML offers a standard means to illustrate structural and behavior components of system artifacts including actors process components activities database schemas and more UML builds on the notations of the Booch method object modeling technique (OMT) and object- oriented software engineering (OOSE) and effectively combines 1-dimensional tra-ditional workfl ow and datafl ow diagrams into much richer yet condensed and concrete graphical diagrams and models Although UML is a widely accepted stan-dard it has been criticized for standard bloat and being diffi cult to learn and linguis-tically incoherent (Henderson-Sellers amp Gonzalez-Perez 2006 Meyer 1997 )

Using UML two different views of a situation can be represented using static and behavioral types of diagrams Static (or structural) views describe the fi xed struc-ture of the system using objects attributes operations and relationships Dynamic (or behavioral) views describe the fl uid and changing behavior of the situation by documenting collaborations among objects and changes to their internal states

3761 Structural Diagrams

The set of diagrams listed here describe the elements that are in the system being modeled ( Unifi ed Modeling LanguagemdashWikipedia the free encyclopedia )

bull Class diagram describes the structure of a system by showing the systemrsquos classes their attributes and the relationships among the classes

bull Component diagram depicts how a software system is split up into compo-nents and shows the dependencies among these components

bull Composite structure diagram describes the internal structure of a class and the collaborations that this structure makes possible

bull Deployment diagram serves to model the hardware used in system implemen-tations and the execution environments and artifacts deployed on the hardware

bull Object diagram shows a complete or partial view of the structure of a modeled system at a specifi c time

bull Package diagram depicts how a system is split up into logical groupings by showing the dependencies among these groupings

bull Profi le diagram operates at the metamodel level to show stereotypes as classes with the ltltstereotypegtgt stereotype and profi les as packages with the ltltpro-fi legtgt stereotype The extension relation (solid line with closed fi lled arrow-head) indicates what metamodel element a given stereotype is extending

3762 Behavioral Diagrams

These sets of diagrams listed here illustrate the things that happen in the system thatrsquos being modeled ( Unifi ed Modeling LanguagemdashWikipedia the free encyclopedia )

bull Activity diagram represents the business and operational step-by-step workfl ows of components in a system An activity diagram shows the overall fl ow of control


66

bull State machine diagram standardized notation to describe many systems from computer programs to business processes

bull Use case diagram shows the functionality provided by a system in terms of actors their goals represented as use cases and any dependencies among those use cases

bull Communication diagram shows the interactions between objects or parts in terms of sequenced messages They represent a combination of information taken from class sequence and use case diagrams describing both the static structure and dynamic behavior of a system

bull Interaction overview diagram is a type of activity diagram in which the nodes represent interaction diagrams

bull Sequence diagram shows how objects communicate with each other in terms of a sequence of messages Also indicates the life-spans of objects relative to those messages

bull Timing diagrams are specifi c types of interaction diagram where the focus is on timing constraints

377 SysML

For modeling system engineering application SysML is a general-purpose model-ing language It can be used for specifi cation analysis design verifi cation and vali-dation of a variety of systems SysML is developed as an extension of the UML

The main standard for SysML is maintained by the OMG group which also man-ages the UML standard ( OMG SysML ) Figure 338 shows the four pillars of SysML Several modeling tool vendors offer SysML support Improvements over UML that are of importance to system engineers include the following ( SysML ForummdashSysML FAQ ) SysML is a smaller language that is easier to learn and use SysML model management components support views (compliant with IEEE-Std- 1471-2000 Recommended Practice for Architectural Description of Software Intensive Systems) and SysML semantics are more fl exible and less software centric as the ones in UML

38 Conclusions for Modeling Methodologies to Use

After reviewing the candidate methodologies as described in the previous sections the matrix in Fig 317 was generated This matrix shows the needs for modeling as rows and lists the candidate methodologies across the top The intersections of a need and methodology (each cell) are then rated for usefulness (fi t for modeling purpose) In conclusion the only method that was capable of mathematical simulation was system dynamics And the only method capable of adequately separating and model-ing the dynamic and static aspects of the problem was UML


67

39 Qualitative Research Grounded Theory and UML

391 An Overview of Qualitative Research

The difference between qualitative and quantitative research is man selecting the appropriate methodology depends on the objectives and preferences of the researcher Largely selecting qualitative or quantitative depends on the variables of available time familiarity with research topic access to interview subjects and data research data consumer preference and relationship of researcher to study subjects (Hancock amp Algozzine 2006 )

Quantitative methods can be appropriate when resources and time are limited Since these methods use instruments such as surveys to quickly gather specifi c vari-ables from large groups of people for example political preferences these instru-ments can produce meaningful data in a short amount of time even for small investments However for collecting data qualitative methods require individual interviews observations or focus groups which require a considerable investment in time and resources to adequately represent the domain being studied

In case little is known about a situation qualitative research is a good starting methodology since it attempts to investigate a large number of factors that may be infl uencing a situation However quantitative methods typically investigate the impact of just a few variables For example often a holistic qualitative approach can investigate an array of variables about a problem and later serve as a starting point for a comparative quantitative study

Quantitative research can often be performed with minimal involvement from participants In case access to study subject is diffi cult a quantitative approach is pre-ferred In distinction diffi culties of delays in access to participants for observations or focus group and types of qualitative research could slow down the researcher efforts

Fig 317 Methodology selection matrix


68

Another important factor in considering qualitative or quantitative method is the preference of the consumer of the research results If the potential consumers of research fi nding prefer words and themes to numbers and graphs a qualitative approach would be better suited On the other hand for example a policy setting committee may need and prefer quantifi able data about a community rather than feelings and explain for general policy setting purposes

Finally in qualitative study itrsquos the goal to understand the situation from the insider perspective (the participants) and not from the researcher perspective However in qualitative researcher to maintain objectivity often it is sought to remain blind to the experimental conditions to avoid infl uences of variables being investigated

We can conclude from the reasoning about qualitative and quantitative approaches that they differ in many ways They are each appropriate for certain situation and nei-ther is right or wrong even in some cases researchers combine the activities of both qualitative and quantitative in their research efforts (Hancock amp Algozzine 2006 )

Since this proposal for HIT diffusion is proposing a mainly qualitative method apparent from the reasons above and nature of the problem being studied the rest of the discussion will focus on the qualitative methods There are various fl avors of qualitative research and while they share common characteristics differences among them exist (Creswell 2006 ) Table 35 presents a comparison of general research traditions and fi ve of these major types are important to highlight (Hancock amp Algozzine 2006 )

392 Grounded Theory and Case Study Method Defi nitions

Grounded theory (GT) and case study method are often used independently or together to study social and technological systems In order to select the appropriate methodology and especially for this proposed HIT diffusion research itrsquos important to understand the defi nition of GT and case study They both have been used in conjunction with UML to study information systems among others

Case study method can be used to study one or more cases in detail and its fundamental research question is the following ldquoWhat are the characteristics of this single case or of these comparison casesrdquo (Johnson amp Christensen 2004 ) A case study is often bounded by a person a group or an activity and is interdisciplinary Once classifi cation of case study types includes the following (Stake 1995 )

1 Intrinsic case studymdashonly to understand a particular case 2 Instrumental case studymdashto understand something at a more general level than

the case 3 Collective case studymdashstudying and comparing multiple cases in a single

research study

In a case study approach for data collection multiple methods such as interviews and observations can be used The fi nal output of a case study is a rich and compre-hensive description of the case and its environment


69

Where case study is detailed account and analysis of one or more cases grounded theory is developed inductively and bottom-up GTrsquos fundamental research question is the following ldquoWhat theory or explanation emerges from an analysis of the data collected about this phenomenonrdquo (Johnson amp Christensen 2004 ) Grounded the-ory is usually used to generate theory and it can also be used to evaluate previously grounded theories The following are important characteristics of a grounded theory (Johnson amp Christensen 2004 )

bull Fit (ie Does the theory correspond to real-world data) bull Understanding (ie Is the theory clear and understandable) bull Generality (ie Is the theory abstract enough to move beyond the specifi cs in the

original research study) bull Control (ie Can the theory be applied to produce real-world results)

Table 35 Research methodology summary (Hancock amp Algozzine 2006 )

Quantitative studies Qualitative studies Case studies

Researcher identifi es topic or question(s) of interest and selects participants and arranges procedures that provide answers that are accepted with predetermined degree of confi dence research questions are often stated in hypotheses that are accepted or rejected using statistical test and analyses

Researcher identifi es topic or question(s) of interest collects information from a variety of sources often as a participant observer and accepts the analytical task as one of discovering answers that emerge from information that is available as a result of the study

Research identifi es topic or question(s) of interest determines appropriate unit to represent it and defi nes what is known based on careful analysis of multiple sources of information of the ldquocaserdquo

Research process may vary greatly from context being investigated (eg survey of how principals spend their time) or appropriately refl ect it (eg observation of how principals spend their time)

Research process is designed to refl ect as much as possible the natural ongoing context being investigated information is often gathered by participant observers (individuals actively engaged immersed or involved in the information collection setting or activity)

Research process is defi ned by systematic series of steps designed to provide careful analysis of the case

Information collection may last a few hours or a few days but generally is of short-term duration using carefully constructed measures designed specifi cally to generate valid and reliable information under the conditions of the study

Information collection may last a few months or as long as it takes for an adequate answer to emerge the time frame for the study is often not defi ned at the time the research is undertaken

Information collection may last a few hours a few days a few months or as long as is necessary to adequately ldquodefi nerdquo the case

Report of the outcomes of the process is generally expository consisting of a series of statistical answers to questions under investigation

Report of outcomes of the process is generally narrative consisting of a series of ldquopages to the storyrdquo or ldquochapters to the bookrdquo

Report of outcomes of the process is generally narrative in nature consisting of a series of illustrative descriptions of key aspects of the case


70

In grounded theory data analysis includes three steps

1 Open coding read transcripts and code themes emerging from data 2 Axial coding organize discovered themes into groupings 3 Selective coding focus on main themes and story development

In a grounded theory approach when no more new themes emerge from data theoretical saturation has been achieved and the fi nal report will include a detailed description of the grounded theory

393 Using Grounded Theory and Case Study Together

Grounded theory is a general method of analysis that can accept quantitative quali-tative or hybrid data (Glaser 1978 ) however it has mainly been used for qualitative researcher (Glaser 2001 ) When using grounded theory and case study together care has to be taken as principles of case study research do not interfere with the emergence of theory in grounded theory (Glaser 1998 ) As Hart ( 2005 ) points out Yin ( 1994 ) states ldquotheory development prior to the collection of any case study data is an essential step in doing case studiesrdquo While Yinrsquos statement is valid for some types of case study research it violates the key principle of open-mindedness (no theory before start) that is in grounded theory Therefore when combining grounded theory and case study the researcher has to explicitly mention which method is driv-ing the investigative research

Supporting the close relationship of GT and case study Hart ( 2005 ) in his own research found that reasons for using grounded theory were consistent with reasons for using case study research set forth (Benbasat Goldstein amp Mead 1987 Hart 2005 )

1) the research can study IS in a natural setting learn the state of the art and generate theo-ries from practice

2) The researcher can answer the questions that lead to an understanding of the nature and complexity of the processes taking place

3) It is an appropriate way to research a previously little studied area

Various researchers have identifi ed generated theory grounded in case study data as a preferred method (Eisenhardt 1989 Lehmann 2001 Maznevski amp Chudoba 2000 Orlikowski 1993 Urquhart 2001 ) Cheryl Chi calls combing grounded the-ory and case studies a ldquotheory building case studyrdquo ( Chi Method-Case Study vs Grounded Theory ) and Eisenhardt ( 1989 ) identifi es the following strength for using case data to build grounded theories

1 Theory building from case studies is likely to produce novel theory this is so because ldquocreative insight often arises from juxtaposition of contradictory or par-adoxical evidencerdquo (p 546) The process of reconciling these accounts using the constant comparative method forces the analyst to a new gestalt unfreezing thinking and producing ldquotheory with less researcher bias than theory built from incremental studies or armchair axiomatic deductionrdquo (p 546)


71

2 The emergent theory ldquois likely to be testable with constructs that can be readily measured and hypotheses that can be proven falserdquo (p 547) Due to the close connection between theory and data it is likely that the theory can be further tested and expanded by subsequent studies

3 The ldquoresultant theory is likely to be empirically validrdquo (p 547) This is so because a level of validation is performed implicitly by constant comparison questioning the data from the start of the process ldquoThis closeness can lead to an intimate sense of thingsrsquo that lsquooften produces theory which closely mirrors realityrdquo (p 547) [4]

394 Grounded Theory in Information Systems (IS) and Systems Thinking Research

While application of grounded theory in information science (IS) is relatively recent scientists in social science have been using grounded theory method (GTM) for about 40 years The growth of GT in IS while being successful however has miscon-ceptions and misunderstanding associated with it A paper by Orlikowski which was the winner of the MIS Quarterly Best Paper Award for 1993 is a seminal example of grounded theory in information systems (Orlikowski 1993 ) Grounded theory enabled Orlikowski to focus on actions and important stakeholders associated with organizational change Others have published research using grounded theory in IS (Baskerville amp Pries-Heje 1999 Lehmann 2001 Maznevski amp Chudoba 2000 Trauth amp Jessup 2000 Urquhart et al 2001 Zenobia 2008 ) but the appliers still remain in the minority (Lehmann 2001 ) While adoption of grounded theory increases there remains a shortage on how to apply it correctly in IS and one paper tried to contribute as shown in the next fi gure (Lehmann 2001 ) and highlighted the following for GT and IS that need more guidance ldquo(a) describing the use of the grounded theory method with case study data (b) presenting a research model (c) discussing the critical characteristics of the grounded theory method (d) discussing why grounded theory is appropriate for studies seeking both rigor and relevance and (e) highlighting some risks and demands intrinsic to the methodrdquo

In IS research grounded theory has been used to investigate infl uence of systems thinking on the practice of information system practitioners (Goede amp Villiers 2003 ) As discussed by Strauss and Corbin (Strauss amp Corbin 1998 ) qualitative research can be seen as an interpretive research Using the proposed seven princi-ples of interpretive fi eld research summarized (Klein amp Myers 1999 ) one IS study used ldquoGrounded Theory as proposed in this study is used to fulfi ll the fourth of the seven principles The aim is to develop a theory on how IS practitioners unknow-ingly use systems thinking techniques in their work that can be generalized in simi-lar situations Other techniques to fulfi ll this principle include Actor Network Theory and the Hermeneutic processrdquo (Goede amp Villiers 2003 )

Another study examined applying GTM to derive enterprise system require-ments (Chakraborty amp Dehlinger 2009 ) This application was driven by the need for initial design and system architecture to be aligned The paper proposed using


72

grounded theory to extract functional and nonfunctional enterprise requirements from system description They stated that a qualitative data analysis technique GTM could be used to interpret requirements for a software system Their use of GTM generated enterprise requirements and resulted in system model in UML The use of GTM in that study had the following contributions

bull Presents a structured qualitative analysis method to identify enterprise requirements

bull Provides a basis to verify enterprise requirements via high-level EA objectives bull Allows for the representation of business strategy in a requirements engineering

context bull Enables the traceability of EA objectives in the requirements engineering and

design phases

Yet another study analyzed Object-Oriented Analysis amp Design (OOAampD) as a representative of information systems development methodologies (ISDMs) and grounded theory (GT) as a representative of research methods ( What Could OOAampD Benefi t From Gounded Theory ) where ldquoThe basic assumption is that both the research and systems development process are knowledge acquisition processes where methods are used which guide the work of acquiring knowledgerdquo The reason for the study was because the researchers felt that there were both similarities and dissimilarities between the OOAampD and GT and wanted to see how one could ben-efi t from using them together An example of dissimilarity is that GT focuses on describing people and their actions while OOAampD focuses on how IS is used to support people with information Another difference is that OOAampD has a design (of a system) purpose where GT is for understanding and theory building ldquoOn a basic level both research methods and ISDMs are support for asking good questions and presenting good answers in order to acquire knowledgerdquo

395 Criticisms of Grounded Theory

Various researchers have criticized grounded theory The earliest riff is a contro-versy that developed among the originators Strauss has further developed GT (Strauss amp Corbin 1998 ) while Glaser ( 1992 ) criticized this version for violating basic principles Others have proposed a newer multi-GTM that would integrate empirical grounding theoretical grounding and internal grounding (Goldkuhl amp Cronholm 2003 )

Other problems with GT include how to deal with large amounts of data since there is no explicit support for where to start the analysis (Goldkuhl amp Cronholm 2003 ) The open-mindedness in the data collection phase can lead to meaninglessly diverging amount of data (Goldkuhl amp Cronholm 2003 ) Another is that GT practi-tioners are advised to discard pre-assumptions they hold so the real nature of the study fi eld comes out GT researchers are encouraged to avoid reading literature until the completion of the study (Rennie Phillips amp Quartaro 1988 ) Ignoring


73

existing theory can lead to duplicating effort for theories or constructs already discovered elsewhere (Goldkuhl amp Cronholm 2003 ) Lack of adequate illustration technique is yet another weakness of GT (Goldkuhl amp Cronholm 2003 )

396 Current State of UML as a Research Tool and Criticisms

Current issues in UML research concern with the extent and nature of UML use and UML usability One study found that the use of UML by practitioners varies and non-IT professionals are involved in the development of UML diagrams (Dobing amp Parsons 2005 ) The study concluded that the variation in use was contrary to the idea that UML is a ldquounifi edrdquo language

Another study while acknowledging the popularity that UML has gained in sys-tem engineering felt ldquoit is not fulfi lling its promiserdquo (Batra 2009 ) Others have stated that UML is too big and complicated (Siau amp Cao 2001 ) suffers from vague semantics (Evermann amp Wand 2006 ) and steep learning curve (Siau amp Loo 2006 ) and doesnrsquot allow for easy interchange between diagrams and models At a higher level some have highlighted that it is diffi cult to model a correct and reliable appli-cation using UML and to understand such a specifi cation (Peleg amp Dori 2000 ) Others have claimed that UML is low in usability because it requires multiple models to completely specify a system (Dori 2002 ) and have proposed another methodology namely the object process methodology (OPM) (Dori 2001 )

397 To UML or Not to UML

The emergence of UML has provided an accessible visualization of models which facilitates communication of ideas But as one research study found out UML lacks formal precise semantics and they used the B Language to supplement UML for their need (Snook amp Butler 2006 ) The B language is a state model-based formal specifi cation notation (Abrial 1996 ) But when the clients of the research study found the B Language artifacts hard to understand they asked the research team ldquocouldnrsquot you use UMLrdquo (Amey 2999 )

398 An Actual Example of Using Grounded Theory in Conjunction with UML

A study used the hierarchical coding procedure offered by GTM with UML to create the requirements for an organizationrsquos enterprise application Figure 318 summa-rizes the coding procedures of GTM that were incorporated into the requirements


74

engineering process for the enterprise application (Chakraborty amp Dehlinger 2009 ) For this example the study chose a ldquohigh-level description for a university support system comprising of a student record management system (SRMS) a laboratory management system a course submission system and an admission management sys-temrdquo (Sommerville 2000 ) Recall from earlier sections that grounded theory coding processes are done in three steps of open coding axial coding and selective coding

3981 Open Coding

In this step the transcript of interview or case is read line by line The text is broken down into concepts Concepts are any part of textual description that the researchers believe are descriptive of the system being studied Table 36 shows the concepts extracted after this study applied GTM to a subsystem of the university support system (SRMS) The preliminary concepts are highlighted in bold The open coding led to the identifi cation of other supporting information as expressed in UML shown in Fig 319

3982 Axial Coding

The goal of this step is to organize the concepts identifi ed during open coding into a hierarchical relationship First the higher order categories are sorted out and later sub-categories add more descriptive information The process is continued until all

Fig 318 Categories for SRMS (Chakraborty amp Dehlinger 2009 )


75

Subsystem

-Student record

Management system

System functionality

-usabilityrequirements

Querying Mechanism Summary reports

Users

-Computational Skill

Student

-Personal Details-course grade

Classescourses

-Courses Name-

Data Item

-Name-Type

Implementation technique

-Database language

-VisualBasic

User Interfaces

Fig 319 Axial coding-description of the SRMS (Chakraborty amp Dehlinger 2009 )

Table 36 Concept extraction (Chakraborty amp Dehlinger 2009 )

Subsystem descriptionmdashStudent record system

The aim of this project is to maintain a student record system maintaining student records within a university or college department The system should allow personal details to be recorded as well as classes taken grades etc It shall provide summary facilities giving information about groups of students to be retrieved Assume that the system is intended for use by departmental administrative staff with no computing background This project may be implemented in a database language or in a language such as Visual Basic

categories have been associated Figure 320 shows the result of this process expressed in UML

3983 Selective Coding

The pervious step of axial coding has provided description for each of the subsys-tems present in the problem space Selective coding integrates the categories and descriptions from the individual subsystems into an overall description of the sys-tem Figure 41 shows this fi nal description derived from grounded theory and pre-sented with UML


76

References

ldquoBasic Flow Chart Samplerdquo ldquoNDE Project Managementrdquo ldquoOMG SysMLrdquo ldquoSysML ForummdashSysML FAQrdquo ldquoUML 20rdquo

Fig 320 System description after selective coding (Chakraborty amp Dehlinger 2009 )


77

ldquoWhat Could OOAampD Benefi t From Gounded Theoryrdquo ldquoData fl ow diagrammdashWikipedia the free encyclopediardquo ldquoUnifi ed Modeling LanguagemdashWikipedia the free encyclopediardquo ldquoUnifi ed Modeling LanguagemdashWikipedia the free encyclopediardquo Abrahamson E (1991) Managerial fads and fashions The diffusion and refection of innovations

Academy of Management Review 16 586ndash612 Abrahamson E (1996) Management fashion Academy of Management Review 21 254ndash285 Abrahamson E amp Fombrun C J (1994) Macrocultures Determinants and consequences

Academy of Management Review 19 728ndash755 Abrahamson E amp Rosenkopf L (1993) Institutional and competitive bandwagons Using math-

ematical modeling as a tool to explore innovation diffusion Academy of Management Review 18 487ndash517

Abrial J R (1996) The B-book assigning programs to meanings Cambridge Univ Press Adams D A Nelson R R amp Todd P A (1992) Perceived usefulness ease of use and usage

of information technology A replication MIS Quarterly 16 227ndash247 Ajzen I (1985) ldquoFrom intentions to actions A theory of planned behavior SSSP Springer Series

in Social Psychology (pp 11ndash39) New York NY Springer Ajzen I (1991) The theory of planned behavior Organizational Behavior and Human Decision

Processes 50 179ndash211 Ajzen I amp Fishbein M (1973) Attitudinal and normative variables as predictors of specifi c

behaviors Journal of Personality and Social Psychology 27 41ndash57 Ambler S W (2004) The object primer Agile model-driven development with UML 20

Cambridge University Press Amey P Dear sir Yours faithfully An everyday story of formality Proc 12th Safety-Critical

Systems Symposium pp 3ndash18 Amit R amp Schoemaker P J (1993) Strategic assets and organizational rent Strategic

Management Journal 14 33ndash46 Bain J S (1956) Barriers to new competition Cambridge Harvard Univ Press Barney J B (1986) Strategic factor markets Expectations luck and business strategy

Management Science 32 1231ndash1241 Barney J (1991) Special theory forum The resource-based model of the fi rm Origins implica-

tions and prospects Journal of Management 17 97ndash98 Barney J B amp Clark D N (2007) Resource-based theory Creating and sustaining competitive

advantage Oxford Oxford University Press Baskerville R amp Pries-Heje J (1999) Grounded action research A method for understanding IT

in practice Accounting Management and Information Technologies 9 1ndash23 Batra D (2009) Unifi ed modeling language (UML) topics Cognitive issues in UML research

Journal of Database Management Behkami N A (2009) Diffusion of Innovation (Healthcare IT)--System Dynamics Portland State

University Department of Engineering amp Technology Management Working Paper Series Benbasat I Goldstein D K amp Mead M (1987) The case research strategy in studies of infor-

mation systems MIS quarterly 369ndash386 Chakraborty S amp Dehlinger J (2009) Applying the Grounded Theory Method to Derive

Enterprise System Requirements Software Engineering Artifi cial Intelligence Networking and ParallelDistributed Computing ACIS International Conference on Los Alamitos CA USA IEEE Computer Society 2009 pp 333ndash338

Checkland P (1999) Systems thinking systems practice Includes a 30-year retrospective Wiley Checkland P Scholes J (1990) Soft systems methodology in action John Wiley amp Sons Ltd

(Import) Chi C Method-Case Study vs Grounded Theory Chuttur M (2009) Overview of the technology acceptance model Origins developments and

future directions


78

Collan M Teacutetard F (2007) Lazy user theory of solution selection Proceedings or the CELDA 2007 conference pp 7ndash9

Collis D J amp Montgomery C A (1995) Competing on resources Strategy in the 1990s Knowledge and Strategy 25ndash40

Cooper R B amp Zmud R W (1990) Information technology implementation research A tech-nological diffusion approach Management Science 36 123ndash139

Cousins J B amp Simon M (1996) The nature and impact of policy-induced partnerships between research and practice communities Educational Evaluation and Policy Analysis 18 (Autumn) 199ndash218

Creswell J W (2006) Qualitative inquiry and research design Choosing among fi ve approaches Sage Publications Inc

Damanpour F (1988) Innovation type radicalness and the adoption process Communication Research 15 545ndash567

Damanpour F (1991) Organizational innovation A meta-analysis of effects of determinants and moderators Academy of Management Journal 34 555ndash590

Damanpour F amp Evan W M (1984) Organizational innovation and performance The problem of ldquoorganizational lagrdquo Administrative Science Quarterly 29 392ndash409

Data Flow DiagrammdashSSADM DiagramsmdashSmartDraw Tutorials Davis F D (1985) A technology acceptance model for empirically testing new end-user informa-

tion systems Theory and results Cambridge MA Massachusetts Institute of Technology Sloan School of Management

Davis F D (1989) Perceived usefulness perceived ease of use and user acceptance of informa-tion technology MIS Quarterly 13 319ndash340

Davis F D Bagozzi R P amp Warshaw P R (1989) User acceptance of computer technology A comparison of two theoretical models Management Science 35 982ndash1003

Demsetz H (1973) Industry structure market rivalry and public policy Journal of Law and eco-nomics 16 1ndash9

Dierickx I amp Cool K (1989) Asset stock accumulation and sustainability of competitive advan-tage Management Science 1504ndash1511

Dobing B amp Parsons J (2005) Current practices in the use of UML Perspectives in Conceptual Modeling 2ndash11

Dori D (2001) Object-process methodology applied to modeling credit card transactions Journal of Database Management 12 4ndash14

Dori D (2002) Why signifi cant UML change is unlikely Eagly A H amp Chaiken S (1993) The psychology of attitudes Fort Worth TX Harcourt Brace

Jovanovich College Publishers Fort Worth Eisenhardt K M (1989) Building theories from case study research Academy of Management

Review 532ndash550 Erdil N amp Emerson C R (2008) Modeling the dynamics of electronic health records adoption

in the us healthcare system Proceedings of the 26th international conference of the system dynamics society 2008

Evermann J amp Wand Y (2006) Ontological modeling rules for UML An empirical assessment Journal of Computer Information Systems 46 14

Finegan A D (2003) Wicked problems organizational complexity and knowledge manage-mentndasha systems approach The International Journal of Knowledge Culture and Change Management 3

Fishbein M (1967) Attitude and the prediction of behavior Readings in attitude theory and mea-surement 477ndash492

Fishbein M Ajzen I (1975) Belief attitude intention and behavior An introduction to theory and research

Forrester J W (1994) System dynamics systems thinking and soft OR System Glaser B G (1978) Theoretical sensitivity Advances in the methodology of grounded theory

Sociology Press


79

Glaser B G (1992) Basics of grounded theory analysis Emergence vs forcing Mill Valley CA Sociology Press

Glaser B G (1998) Doing grounded theory Issues and discussions Mill Valley CA Sociology Press

Glaser B G (2001) The grounded theory perspective Conceptualization contrasted with descrip-tion Sociology Press

Goede R amp Villiers C D (2003) The applicability of grounded theory as research methodology in studies on the use of methodologies in IS practices Proceedings of the 2003 annual research conference of the South African institute of computer scientists and information technologists on Enablement through technology South African Institute for Computer Scientists and Information Technologists 2003 pp 208ndash217

Goldkuhl G amp Cronholm S (2003) Multi-grounded theoryndashAdding theoretical grounding to grounded theory European conference on research methodology for business and management studies p 177

Grant R M (1991) The resource-based theory of competitive advantage Implications for strat-egy formulation California Management Review 33 114ndash35

Grant R M (1996) Toward a knowledge-based theory of the fi rm Strategic Management Journal 17 109ndash122

Hall R (1992) The strategic analysis of intangible resources Strategic Management Journal 135ndash144

Hamel G amp Prahalad C K (1990) The core competence of the corporation Harvard Business Review 68 79ndash91

Hancock D R amp Algozzine R (2006) Doing case study research A practical guide for begin-ning researchers Teachers College Press

Hart D N (2005) Information systems foundations ANU E Press Henderson-Sellers B amp Gonzalez-Perez C (2006) Uses and Abuses of the Stereotype

Mechanism in UML 1x and 20 Model Driven Engineering Languages and Systems 16ndash26 Hendrickson A R Massey P D amp Cronan T P (1993) On the test-retest reliability of per-

ceived usefulness and perceived ease of use scales MIS Quarterly 17 227ndash230 Hitt M A Hoskisson R E amp Kim H (1997) International diversifi cation Effects on innova-

tion and fi rm performance in product-diversifi ed fi rms Academy of Management Journal 767ndash798

Hitt M A amp Ireland R D (1985a) Strategy contextual factors and performance Human Relations 38 793

Hitt M A amp Ireland R D (1985b) Corporate distinctive competence strategy industry and performance Strategic Management Journal 6 273ndash293

Hitt M A amp Ireland R D (1986) Relationships among corporate level distinctive competen-cies diversifi cation strategy corporate structure and performance Journal of Management Studies 23 0022ndash2380

Hrebiniak L G amp Snow C C (1982) Top-management agreement and organizational perfor-mance Human Relations 35 1139

Itami H amp Roehl T (1987) Mobilizing intangible assets Cambridge MA Johnson B amp Christensen L B (2004) Educational research Quantitative qualitative and

mixed approaches Research Edition Second Edition Allyn amp Bacon Klein H K amp Myers M D (1999) A set of principles for conducting and evaluating interpretive

fi eld studies in information systems MIS Quarterly 67ndash93 Kossiakoff A amp Sweet W N (2003) Systems engineering Wiley-IEEE Learned E Christensen C Andrews K amp Guth W (1969) Business policy Text and casesrsquo

Homewood IL Richard D Irwin Inc Lehmann H (2001) Using grounded theory with technology cases Distilling critical theory from

a multinational information systems development project Journal of Global Information Technology Management 4 45ndash60


80

Liebeskind J P (1996) Knowledge strategy and the theory of the fi rm Strategic Management Journal 17 93ndash107

Maznevski M L amp Chudoba K M (2000) Bridging space over time Global virtual team dynamics and effectiveness Organization Science 473ndash492

Meyer B (1997) UML The positive spin Cutter IT Journal x Nelson R R amp Winter S G (1982) An evolutionary theory of economic change Belknap Press Nutley S amp Davies H T O (2000) Making a reality of evidence-based practice some lessons

from the diffusion of innovations Public Money amp Management 20 35 ONeill H M Pouder R W amp Buchholtz A K (1998) Patterns in the diffusion of strategies

across organizations Insights from the innovation diffusion literature Academy of Management Review 23 98ndash114

Orlikowski W J (1993) CASE tools as organizational change Investigating incremental and radical changes in systems development MIS Quarterly 309ndash340

Osborne S P (1998) Naming the beast Defi ning and classifying service innovations in social policy Human Relations 51 1133ndash1154

Otto P amp Simon M (2009) Coordinating quality care A policy model to simulate adoption of EHR Proceedings of the 26th international system dynamics conference Albuquerque 2009

Peleg M amp Dori D (2000) The model multiplicity problem Experimenting with real-time specifi cation methods IEEE Transactions on Software Engineering 26 742ndash759

Penrose E (1959) The theory of the growth of the fi rm New York NY Wiley Porter M E (1981) The contributions of industrial organization to strategic management The

Academy of Management Review 6 609ndash620 Porter M E (1985) Competitive advantage Competitive advantage Creating and sustaining

superior performance New York NY Porter Michael E (1979) How competitive forces shape strategy Harvard Business Review 57

137ndash145 Prahalad C K amp Bettis R A (1986) The dominant logic A new linkage between diversity and

performance Strategic Management Journal 485ndash501 Rennie D L Phillips J R amp Quartaro G K (1988) Grounded theory A promising approach

to conceptualization in psychology Canadian Psychology 29 139ndash150 Ricardo D (1817) The principles of political economy and taxation (1817) The Works and

Correspondence of David Ricardo hrsg v Sraffa Piero Bd I Cambridge Robinson K Berrisford G (1994) Object oriented SSADM Prentice Hall PTR Rumelt R P amp Lamb R (1984) Competitive strategic management Toward a Strategic Theory

of the Firm 556ndash570 Scherer M J (2002) Assistive technology Matching device and consumer for successful rehabili-

tation Washington DC APA Books Segars A H amp Grover V (1993) Re-examining perceived ease of use and usefulness A confi r-

matory factor analysis MIS Quarterly 17 517ndash525 Siau K amp Cao Q (2001) Unifi ed modeling language A complexity analysis Journal of

Database Management 12 26ndash34 Siau K amp Loo P P (2006) Identifying diffi culties in learning UML Information Systems

Management 23 43ndash51 Smart P A Maddern H amp Maull R S (2008) Understanding business process management

Implications for theory and practice Snook C amp Butler M (2006) UML-B Formal modeling and design aided by UML ACM

Transactions on Software Engineering and Methodology (TOSEM) 15 122 Sommerville I (2000) Software engineering Addison Wesley Spender J C amp Grant R M (1996) Knowledge and the fi rm Overview Strategic Management

Journal 17 5ndash9 SSADM Diagram SoftwaremdashStructured Systems Analysis and Design Methodology Stake D R E (1995) The art of case study research Sage Publications Inc Stalk G Evans P amp Shulman L E (1992) Competing on capabilities The new rules of corpo-

rate strategy Harvard Business Review


81

Stevens W Myers G amp Constantine L (1979) Structured design Classics in software engi-neering Yourdon Press 205ndash232

Strauss A L Corbin J M (1998) Basics of qualitative research Techniques and procedures for developing grounded theory Sage Pubns

Subramanian G H (1994) A replication of perceived usefulness and perceived ease of use mea-surement Decision Sciences 25 863ndash863

Szajna B (1994) Software evaluation and choice Predictive validation of the technology accep-tance instrument MIS Quarterly 18 319ndash324

Teece D J (1980) Economy of scope and the scope of the enterprise Journal of Economic Behavior and Organization 1 223ndash247

Teece D J Pisano G amp Shuen A (1997) Dynamic capabilities and strategic management Strategic Management Journal 18 509ndash533

Tetard F amp Collan M (1899) Lazy user theory A dynamic model to understand user selection of products and services HICSS (pp 1ndash9) Big Island HI IEEE

Theories Used in IS Research Wiki York University Thompson A A amp Strickland A J (1983) Strategy formulation and implementation Tasks of

the general manager Business Publications Tornatzky L G amp Fleischer M (1990) Processes of technological innovation New York The

Free Press Trauth E M amp Jessup L M (2000) Understanding computer-mediated discussions Positivist

and interpretive analyses of group support system use MIS Quarterly 24 43ndash79 Urquhart C (2001) An encounter with grounded theory Tackling the practical and philosophical

issues Qualitative Research in IS Issues and Trends 104ndash140 van de Water H Schinkel M amp Rozier R (2006) Fields of application of SSM A categoriza-

tion of publications Journal of the Operational Research Society 58 271ndash287 Vandeven A H amp Rogers E M (1988) Innovations and organizations Critical perspectives

Communication Research 15 632ndash651 Venkatesh V Morris M G Davis G B Davis F D DeLone W H McLean E R et al

(2003) User acceptance of information technology Toward a unifi ed view Inform Management 27 425ndash478

Viswanath V Morris M G Davis G B amp Davis F D (2003) User acceptance of information technology Toward a unifi ed view MIS Quarterly 27 425ndash478

WW Wakeland EJ Gallaher LM Macovsky and CA Aktipis ldquoA Comparison of System Dynamics and Agent-Based Simulation Applied to the Study of Cellular Receptor Dynamicsrdquo Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSSrsquo04) mdash Track 3 mdash Volume 3 IEEE Computer Society 2004 p 300862

Wernerfelt B (1984) A resource-based view of the fi rm Strategic Management Journal 171ndash180

Williams B (2005) Soft systems methodology Williamson O E (1975) Markets and hierarchies analysis and antitrust implications Wixom B H amp Todd P A (2005) A theoretical integration of user satisfaction and technology

acceptance Information Systems Research 16 85ndash102 Wolfe R A (1994) Organizational innovation Review critique and suggested research Journal

of Management Studies 31 405ndash431 Yin R K (1994) Case study research Design and methods Sage Publications Inc Zenobia B (2008) A grounded agent model of the consumer technology adoption process

Portland State University



Chapter 4 Field Test


41 Introduction and Objective

The purpose of this section is to demonstrate the feasibility of the research proposal and its corresponding components on a small scale The general objec-tives of the feasibility study include demonstrating the larger research objectives and demonstrating that the right mix of theories and methodologies has been con-sidered The small fi eld study was conducted at Oregon Health amp Science University (OHSU) with the Care Management Plus (CMP) Team CMP is a proven health information technology (HIT) application for older adults and chronically ill patients with multiple conditions and the innovation includes soft-ware clinic processes and training

Use of qualitative research-based case study with application of diffusion theory and dynamic capabilities using the Unifi ed Modeling Language (UML) notation is demonstrated in this fi eld study In the following sections data collection analysis results conclusions and limitations of research along with propositions for future research are discussed



84

42 Background Care Management Plus

421 Signifi cance of the National Healthcare Problem

Today care for patients with complex healthcare needs is in a state of crisis in the USA The aging population lifestyle shifts and environmental factors have led to rapid increases in numbers of patients who suffer from complex illnesses while the healthcare system struggles to adapt Treatment for patients with complex needs succeeds when their needs are known their care is well coordinated and their healthcare team is able to make clinical decisions based on the systematically avail-able evidence Tools such as better health IT systems and robust fi nancial incen-tives can facilitate improved quality of care

Patients suffering from chronic illnesses account for approximately 75 of the nationrsquos healthcare-related expenditures However these patients only receive the appropriate treatment about 50 of the time Inadequacy of care is even more of a problem for patients with multiple chronic illnesses For example a patient on Medicare with fi ve or more illnesses will visit 13 different outpatient physicians and fi ll 50 prescriptions per year (Friedman Jiang Elixhauser amp Segal 2006 ) As the number of a patientsrsquo conditions increases the risk of hospitalizations grows exponentially (Wolff Starfi eld amp Anderson 2002 ) While the transitions between providers and settings increase so does the risk of harm from inadequate informa-tion transfer and reconciliation of treatment plans Such risks are a large part of the reason patients like this account for 40 of all Medicare costs Wolff estimates that a third of these costs may be due to inappropriate variation and failure to coor-dinate and manage care (Wolff et al 2002 ) As costs continue to rise the delivery of care must change to meet these costs Components identifi ed as important include better planning on the part of providers and patientsfamilies both in visits and over time better coordination and communication and increased self-manage-ment of conditions by patients and caregivers (Bodenheimer Wagner amp Grumbach 2002a 2002b )

Two changes to healthcare teams that can provide this systematic approach are nurse-based care management and health information technology (Dorr Wilcox et al 2006 Shojania amp Grimshaw 2005 Shojania et al 2006 ) A meta-analysis for redesign for patients with diabetes showed that nurse care managers and team reorganization were the most successful quality improvement techniques infor-mation technology alone was only moderately successful (Shojania et al 2006 ) A care management model for depression in older adults (who tend to have more complicated depression and concurrent illnesses) demonstrated broad success (Steffens et al 2006 Rubenstein et al 2002 ) Patients with schizophrenia bene-fi tted from care management with HIT using the Medical Informatics Network Tool (Young Mintz Cohen amp Chinman 2004 ) The CMP team and others have shown that reduction in hospitalization visits can occur in models focused on older adults with complex needs (Dorr Brunker Wilcox amp Burns 2006 Counsell et al 2007 )


85

422 Preliminary CMP Studies at OHSU

The CMP model for primary care developed by researchers at Intermountain Healthcare through funding from the John A Hartford Foundation uses specially trained care managers and tracking software to help clinics better care for patients with complex chronic illness

The model helps the clinical team prioritize healthcare needs and prevent com-plications through structured protocols and it provides tools to assist patients and caregivers to self-manage chronic diseases Specialized information technology includes the care manager tracking database patient summary sheet and messaging systems to help clinicians access care plans receive reminders about best practices and facilitate communication between the healthcare team The initial data from implementing CMP was highly positive and demonstrated improved clinical and economic outcomes The initial seven sites for testing CMP were urban practices comprising six to ten clinicians each These clinics employed full-time nurse care managers who each worked with a panel of around 350 active patients

CMP focuses on two primary areas well-trained care managers embedded in the clinic and IT technology to help them manage patients with chronic illnesses Figure 41 describes the primary aspects of the CMP program Physicians refer patients with complex needs (about 3ndash5 of the population in primary care clinics) into the program The care manager then co-creates a care plan with the patient acts as a guide to help the patient and family meet their goals and facilitates access to necessary resources when the patient or family needs navigation ( OHSU )

CMP couples an ambulatory care team with HIT For seniors with complex needs CMP demonstrated a 20 reduction in mortality a 24 reduction in hospi-talizations and a 15ndash25 reduction in complications from diabetes (Dorr Wilcox et al 2006 Dorr Wilcox Donnelly Burns amp Clayton 2005 ) CMP facilitates use of HIT to establish and track care plans and specifi c patient goals to teach and encourage self-management to measure and improve quality and to manage the complex and interleaving tasks as patients and teams prioritize needs Figure 42 shows the system components of CMP (Behkami 2009a ) Experience from the

Care Management

Care Manager

Technology

Referral-For any condition or need-Focus on certainconditions

-Assess amp Plan-Catalyst-Structure

-Access -Best Practices-Communication

Evaluation-Ongoing with feedback-Based on key process and outcome measures

Fig 41 Components of the care management plus program ( OHSU )

4 Field Test

86

dissemination of CMP in more than 75 clinics across the country has led to a deep understanding of the barriers and benefi ts of such HIT Barriers include the need to integrate systems diffi culty communicating with the entire team and representa-tion of workfl ow

43 Research Design

431 Overview

The chart below shows the steps used in conducting the fi eld study Using a litera-ture review a preliminary framework and model were produced Next data was collected using mix methods and various tools were used for analysis and later validation (Fig 43 )

432 Objectives

Objective 1 Identify some dynamic capabilities needed for successful implementa-tion of HIT ( CMP OHSU ) This is the application area that we will derive cases from to develop the dynamic capabilities based on diffusion framework

Fig 42 CMP system view


87

Objective 2 Demonstrate that dynamic capabilities theory can be used and how to meaningfully extend diffusion of innovation theory

Objective 3 Use software and system engineering methods including 4 + 1 view for perspectives and UML to demonstrate documentation and analysis

Objective 4 Build and run a small simulation of the DOI theory extension using system dynamics The simulation will be used to demonstrate the validity of the new diffusion framework

433 Methodology and Data Collection

The methodology used for the research design is an exploratory case study The case study method is chosen because the proposed research needs to know ldquohowrdquo and ldquowhyrdquo HIT adoptiondiffusion program has worked (or not) Such questions deal with operational links needing to be traced over time rather than mere frequencies or incidences The next three subsections describe the data collection tools used and the last explains the sampling for the fi eld study

Fig 43 Field study research process overview

4 Field Test

88

4331 Site Readiness Questionnaire

The Site Readiness questionnaire is a custom-built structured questionnaire created by the CMP team at OHSU which is sent to sites (clinics) considering adopting CMP The questionnaire attempts to capture the multiple perspectives of the physi-cian nurse care manger as well as IT professionals Each site that participated in the CMP project founded by the John A Hartford Foundation over the last few years was required to fi le out one of these to be eligible The questionnaire is broken into multiple sections that include clinic goals and barriers for adoption current staffpatients current services offered information technology landscape quality measures used to gage services and other

4332 Expert Discussion Guide (Interview)

To understand the perspective of physicians and care managers a CMP interview guide was used A discussion guide is a semi-structured interview guide that is meant to be fl exible to provide room for discovery of new items while still providing some structure to data collection

Overall interview objectives

bull Understand the usersrsquo daily activities attitudes and values bull Determine physician and nurse use patterns with current care management and

HIT productsprocesses (if any) bull Identify the functional and emotional benefi ts that the user is seeking from a care

management (HIT) product bull Learn about how the usage environment impacts the use and perception of the

product

4333 Survey Instrument IT and Administrative Users Questionnaire

To understand the perspective of IT and administrative users a structured question-naire was used


bull Understand the strategic role of IT in the clinic bull Determine past success or failure of IT implementation at the clinic bull Identify systems and IT implementation capabilities of the clinic bull Learn about how IT can enhance or challenge adoption of a new care manage-

ment product at that clinic


89

4334 Study Sampling

Readiness Assessment

For the Readiness Assessment sample data from four sites in Oregon and one in California who currently participate in the OHSU CMP trail were reviewed This section provides a brief description of each location and its affi liated organizations

The Oregon clinics are members of the Oregon Rural Practice and Research Network (ORPRN) which is a statewide network of primary care clinicians com-munity partners and academicians dedicated to research into delivery of healthcare to rural residents and research to reduce rural health disparities ORPRN includes 42 rural primary practices which care for over 166000 patients ( ORPRN ) The fol-lowing individual clinics participated in providing data Lincoln City Medical Center Eastern Oregon Medical Associates OHSU Scappoose Family Health Center and Klamath Open Door Family Medicine

The fi fth study participant is HealthCare Partners (HCP) LLC a management service organization that manages and operates medical groups and independent physician networks nationally The organization serves more than 500000 patients of whom more than 100000 are older adults HealthCare Partners Medical Group (HCPMG) has been recognized by health plans and business groups for its medical leadership the high quality of medical care delivered operational effectiveness and high rates of patient satisfaction HCPMG employs 500+ primary care and specialty physicians who care for patients in Los Angeles County and north Orange County California through 40 neighborhood offi ces fi ve urgent care centers two medical spas an ambulatory surgery center and an employer on-site offi ce ( Health Care Partners Medical Group )

Physician Discussion Guide and IT Questionnaire

See Table 41

Table 41 Sampling

Subject Clinic Clinic size

EHR- adoption level

Experience with care management Role at the clinic

Interview 1 Oregon Health amp Science University

Large High High Physician principal investigator


Large High Medium Care management plus program director


Large High Medium Nurse care manager

4 Field Test

90

434 Analysis

Using open coding and focused methods of Thematic Analysis the author created themes from the data (Bailey 2006 ) including recurring patterns topics theories viewpoints and concepts Rogersrsquo diffusion of innovation theory and dynamic capability theory and TAM and adoption barriers and infl uences were used to guide the coding Figure 44 shows the workfl ow used for analysis Figure 45 shows a sample of the coding artifacts created

Fig 44 Analysis workfl ow


91

435 Results and Discussion

After iterating over the themes that emerged from the collected data I was able to group them into eight categories that affected the HIT diffusion process for CMP They included

bull Needs and drivers bull Barriers bull Outcome measures bull Infl uences bull Capabilities bull Adoption decision bull Adoption success criteria bull Awareness of innovation versus actual adoption timeline

Fig 45 Sample fi eld notes

4 Field Test

92

Based on the extracted constructs a process of the adoption from the clinic per-spective was created as shown in Fig 46 The innovation process seems to start for the clinics based on ldquo Drivers rdquo or ldquo Needs rdquo A driver for example is something like the need to more effi ciently manage clinic workfl ow Eventually these needs drive the clinic to adopt the HIT innovation in this case CMP offered by OHSU Then there are ldquo Barriers rdquo and ldquo Infl uences rdquo which are negative and positive reinforce-ments respectively Barriers can discourage both the ldquo Drivers rdquo and the ldquo Adoption Decision rdquo in a negative way For example lack of funding at the clinic for buying an expensive software system can be an example of a barrier Infl uence reinforces both the ldquo Drivers rdquo and the ldquo Adoption Decision rdquo and itrsquos a positive force For example government reimbursement for using HIT in the form of extra revenue for clinic seems to be an example of a positive infl uence on the HIT adoption process

Another theme that emerged from the data which is directly fed related to the adoption decision is ldquo Adoption Success Criteria rdquo This is how a clinic defi nes whether adopting CMP was successful or not These criteria were either mecha-nisms created by the clinic itself or government- or payer-supported ldquo Outcome Measures rdquo that described adoption goals and the progress towards them In time these ldquo Outcome Measures rdquo can either become barriers or infl uences either for the same adopter or future adopters this is similar to the ldquoconfi rmationrdquo stage that Rogers defi ned in Diffusion of Innovation

In all based on the data collected it was clear that the clinics didnrsquot adopt as soon as they became aware of CMP and once they decided to adopt often they didnrsquot know what to do and how to go about adopting it This is where the theme of ldquo Capabilities rdquo comes to light in the adoption process For example having a nurse that was properly trained and skilled in care management to oversee the program was a capability needed and recommended by OHSU for successful adoption As evident from Fig 46 needing ldquo Capabilities rdquo directly became a factor in the

Fig 46 Clinic workfl ow


93

ldquo Adoption Decision rdquo and indirectly acted as a ldquo Infl uence rdquo or ldquo Barrier rdquo depending on if the clinic had it (or could get it) or didnrsquot have it (or couldnrsquot get it) And fi nally some combination of identifi able barriers infl uences and capabilities leads to the remaining theme discovering that awareness and actual adoption happen over time ldquo Awareness of Innovation versus Actual Adoption Timeline rdquo

4351 Structural Aspects

CMP Adoption Class Diagram

Based on the interviews I was able to build a structural diagram of the stakeholders and actors involved in the CMP diffusion ecosystem as shown in Fig 49 The nota-tion used for the diagram is a UML class diagram that shows the static aspects of the important objects in the system As seen in Fig 47 each object is represented as a rectangle box In the top section of each rectangle is the name of the object and in the second subsection is the attributes of that object A stakeholder or actor is con-sidered to be a type of an object The arrows between object boxes as in Fig 48 show the relationships among objects Itrsquos worth mentioning that these links donrsquot represent behavior which will be shown using dynamic types of UML diagrams in later sections of this document The lines with an arrow at the end show a general-ization relationship meaning for example as in Fig 48 a physician is a type of provider and so are nurses and institutional providers (clinic) This notation allows us to analyze these objects as part of the whole while keeping their specializations in mind The dotted lines between objects represent a link and not a hierarchical relationship like the other line types (Fig 49 )

Physician-Education-Comfort with Technology-Specialization-Role

Fig 47 Physician object

Provider

Physician

NurseInstituational Provider

-Size-Location-Technology

-Education-Comfort with Technology-Specialization-Role

Fig 48 Provider parent class

4 Field Test

94

CMP Ecosystem Package Diagram

The ecosystem is made up of fi ve major packages of objects as shown in the top part of Fig 410 as a UML component diagram These packages include the provider government innovation supplier care seeker and payer packages Being able to identify and correctly group these objects is useful in studying the diffusionadop-tion process This eventual categorization will be one of the benefi ts and unique contributions of the proposed research HIT diffusion research

4352 Behavioral Aspects

There are a range of activities that occur at the clinic for adoption of CMP which require analysis These include adoption rejection dissemination developing capabilities implementation usage reconfi rmation developing capabilities and

Fig 49 Field study class diagram


95

Fig 410 Field study packages

4 Field Test

96

managing capabilities In Fig 411 these are expressed in a UML use case diagram notation Within the scope of the fi eld test subset of these activities including the knowledge stage and developing capabilities stage are evaluated in more detail in the following sections

Knowledge Stage for CMP

The UML sequence diagram in Fig 412 was created and shows the stakeholders and sequence of actions that shape the ldquo Knowledge Stage rdquo of Rogersrsquo diffusion process The ldquo HIT Innovation Supplier rdquo (in this case OHSU for CMP) attends a ldquo Conference rdquo such as the Annual AGA Conference (American Geriatrics Association) where a ldquo Physician rdquo comes to their presentation and becomes aware of the innovation (CMP) at the conference If the ldquo Physician rdquo decides that CMP may be useful for their clinic they go back and inform the ldquo Clinic rdquo that they work at about CMP including the ldquo Nurses rdquo ldquo CEO rdquo (or other administrative decision maker) and other ldquo Physician ( s )rdquo The interactions of these multiple stakeholders over time forms the ldquoKnowledge Stagerdquo of Rogersrsquo Diffusion Theory Having this model with such level of detail allows us to examine the precise participants and decision points and examine the time elements of CMP adoption and diffusion processes

Dynamic Capability Development Stage

The UML sequence diagram in Fig 413 was created from data collected and shows the stakeholders and sequence of actions that shape the ldquo Dynamic Capability Development Stage rdquo for adoption of CMP Once a potential adopter gains knowl-edge of an innovation and later decided to adopt the innovation it goes into the loop

Government

Supplier

Care Seeker

Adoption

Rejection

Dissemenation

DevelopCapabilities

Manage Capabilities

Reconfirmation

Usage

Implementation

Provider

Payer

Fig 411 Field study use case diagram


97

of acquiring the dynamic capabilities necessary to successfully adopt the innova-tion Figure 413 shows the dynamic capabilities needing to be in place to adopt CMP which include (1) having CMP software (2) nurse care manager and (3) get-ting reimbursed from the government for using HIT The sequence diagram here only shows the positive path meaning that it assumes that the adopter was able to acquire the capabilities and adopt CMP

Fig 412 Sequence diagram ldquoknowledge stagerdquo

Fig 413 Sequence diagram ldquodynamic capability development stagerdquo

4 Field Test

98

Overall Adoption Decision State Chart

What the sequence diagram in the previous section couldnrsquot show about alternative paths for decisions can be illustrated in Fig 414 using a UML activity diagram The happy path is down the middle of the diagram where when the clinic decides to adopt CMP it already has the three needed capabilities (CMP software a nurse care man-ager and a way to get paid by payers) In that case it can quickly move down the middle and adopt CMP and therefore is less likely it would reject the innovation (CMP) However whatrsquos more interesting about this graph based on the interviews with experts and users is the alternate paths the scenario can take If some of the three needed capabilities are not in place the adoption has to wait until those remaining capabilities are either built or bought before true adoption happens This supports the objective of the proposed research that awareness alone is not enough as described in Rogers to move to next step of adoption Meaning after knowledge of innovation capabilities need to be developed or bought to truly adopt an innovation

4353 Classifi cation of Capabilities

Recall from earlier sections of this document that various researchers have attempted to classify capabilities or competencies necessary for competitive advantage namely Barney Figure 415 and Itami Figure 416 Similar to their works based on the data collected from my feasibility study a classifi cation of dynamic capabilities for HIT adoption (CMP) can be generated (Fig 417 )

4354 Limitations

While the purposed model is fl exible and could accommodate studying various types of organizations (hospitals) patients or providers the following are some of the limitations

bull The proposed model is a qualitative-based descriptive case study What it tries to do is to understand and bound the problem for one case Therefore the fi ndings

NoNo

No

Develop or BuyCapability

(CMP Software)


(Receive Payments)


(Nurse Care Manager)

Decides to AdoptInnovation

AdoptInnovation

RejectInnovation

already haveCapability



Yes Yes Yes

Fig 414 Field study state chart for adoption decision


99

cannot be immediately generalized to a whole population of clinics with wide varying capabilities However it does set the foundations for a second-phase qualitative research studies in the future For example the results can be used in a qualitative study to measure the prevalence of certain type of capabilities across a group of fi rms (clinics)

bull Different fi rms (clinics) that adopt an innovation (CMP) may implement capa-bilities in various ways with varying implementation qualities The quality of capability implementation and its effect on the adoption and diffusion process are not directly captured in this model and are a good future research topic

bull Capabilities that are needed in the context of adoption of one HIT innovation (eg CMP) often exist alongside capabilities used in other hospital systems at the clinic The current research doesnrsquot specifi cally look at the relationship

Fig 415 Barneyrsquos classifi cation of capabilities

Fig 416 Itamirsquos classifi cation of assets

4 Field Test

100

between CMP capabilities (unless directly interfacing with CMP) and other hos-pital systems for example billing electronic health record disease registry etc

bull This research does not look at the internals of the process required for acquiring capabilities itrsquos treated as a black box Existence of (or lack of) these capabili-ties interfacing with them and their timing are of most importance to the proposal

bull Although due to its sophistication the CMP product at OHSU in many ways is a perfect HIT innovation to study but it mostly targets older adults and extremely sick patients A healthier target population such as professional workers less than 40 years of age may have unique infl uences on the HIT adoption and diffusion process that may not be highlighted in this choice of application to study

bull Similar to using multi-perspective to represent stakeholder and views in classi-fi cation of capabilities for HIT innovation (CMP) it could be benefi cial to use levels For example a small clinic may need a subset of capabilities that a larger hospital would need for adoption Using multi-levels would be a constructive endeavor for future research

436 Simulation A System Dynamics Model for HIT Adoption

Adoption of healthcare IT (HIT) is a critical factor in addressing quality and cost of patient care The assessment and diffusion of health IT have been the subjects of numerous studies Through this model factors infl uencing the adoption process and the relationships between them are examined As highlighted in the previous sec-tions healthcare systems are complex systems Their highly fragmented structure

HIT AdoptionCapabilities (CMP)

Technology

Work Flow

CMPSoftware

EHRIntegration

ReimbursementPayment Processing

Training

Nurse CareManager Training

PhysicianTraining

Patient LearningCommunity

Patient PanelManagement

Skilled Worker(Nurse Care Manager)

Fig 417 Field study taxonomy of capabilities


101

makes it diffi cult to clearly understand healthcare problems Without a clear understanding evaluating response strategies becomes a diffi cult endeavor One methodology that can take us closer to a solution is system dynamics This report uses a system dynamics (SD) approach to evaluate a part of the problem (Behkami 2009b ) SD allows exploration of policy options through simulation The main objective of this study is to uncover the basic adoption process in the US healthcare system and evaluate each source of adoption

4361 Reference Behavior Pattern

Actual behavior of the real-world model for this report is based on two theories and two examples

bull Diffusion of innovation theory by Rogers bull Bass diffusion model with modeling disease epidemics example (Sterman amp

Sterman 2000 ) bull Bass diffusion model with cable TV penetration in US households (Sterman amp

Sterman 2000 )

ldquo Diffusion is the process in which an innovation is communicated through certain channels over time among the members of a social system rdquo (Rogers amp Rogers 2003 ) This special type of communication is concerned with new ideas It is through this process that stakeholders create and share information together in order to reach a shared understanding Some researchers use the term ldquodissemina-tionrdquo for diffusion that are directed and planned In his classic work (Rogers amp Rogers 2003 ) Rogers identifi es four main elements in the diffusion process that are virtually present in all diffusion research (1) an innovation (2) communication channels (3) over time and (4) social systems

The diffusion and adoption of new ideas and new products often follows S-shaped growth patterns Adoption of new technologies spreads as those who have adopted them come into contact with those who havenrsquot and persuade them to adopt the new system The new believers in turn then persuade others An example of the Bass diffusion model for adoption of cable TV (Sterman amp Sterman 2000 ) by house-holds can be used as a reference for health IT model The example identifi ed the following important factors in a householdrsquos decision to subscribe to cable TV

bull Favorable word of mouth from existing subscribers bull Positive experience viewing cable at the homes of friends and family bull Keeping up with the Joneses bull Feeling hip because of consuming on cable only knowledge

Similarly adoptions of HIT applications depend on favorable word of mouth from hospitals or clinics that currently use the HIT product Also positive empirical and fi nancial evidence through industry publications shows that the HIT application improved patient care and fi nancials of the clinic

4 Field Test

102

4362 Model Development

In this Bass style model as seen in Fig 418 potential adopters were broken down into large and small practices Small practice is enticed by large government reim-bursement to adopt and is assumed not to be affected by word of mouth or advertis-ing for adoption Itrsquos important to mention that word of mouth may affect the choice of HIT vendor for adoption in a small clinic but nonetheless act of adoption is for certain and itrsquos this part that is of interest to this report

The model in this report captures some of the important variables that have been identifi ed through a literature review and interviewing a physician The model includes three stocks

bull Small Practice Potential Adopters ldquoSPrdquo represents the number of small clinic that have not adopted health IT

bull Large Practice Potential Adopters ldquo LP rdquo represents the number of large clinics that have not adopted health IT

bull Adopters ldquo A rdquo represents the number of small and large clinics that have adopted health IT

In this model potential adopters are grouped into small and large practice The small practices will be receiving a $40000 reimbursement check from the OBAMA stimulus package for adopting health IT Large practices will not receive any stimu-lus and they will continue adopting health IT per their business and strategic plans Adoption rates ldquoLARrdquo and ldquoSARrdquo represent number of clinics adopting per time for large and small practices respectively

1 LAR = Adoption from advertising + adoption from word of mouth

(a) Adoption from advertising = a times SP (b) Adoption from word of mouth = c times i times LP times AN

2 SAR = Adoption from government stimulus = j times LP

Adoption for large clinic can occur from two sources

3 Adoption_from_Advertising = Large_Potential_Adopters times Advertising_ Effectiveness

4 Adoption_from_word_mouth = contact_rate times adoption_fraction_i times (adopterstotal_population)

Adoption for small clinics can happen only because of

5 Adoption_from_Government_ incentive = Small_Potential_Adopters times Adoption_ fraction_j

Total adopters

6 Adopters ldquoArdquo = SAR + LAR



Adaptors Fig 418 Small and large clinic adaptors


103

4363 Assumptions

bull Model refers to health IT as a set of defi nable features that would be benefi cial to use for the clinics and patients For the purposes of this model it is not assuming any particular product(s)

bull Model assumes at time = 0 that there are no adopters in existence from small or large practices

bull Model assumes that small clinics are infl uenced by government stimulus for adoption only while large practices are infl uenced by advertising or word of mouth adoption only

bull All clinics (small or large) will at some point adopt the HIT bull Once a clinic adopts it will not reject the HIT and go back to potential adopters

Table 42 lists the other assumptions and parameters for the model

Table 42 Parameters for system dynamics model

Parameter Description Value

HIT adoption carrying capacity

This is the number of clinics or hospitals that exist in the USA that are potential adopters

There are 52 hospitals in Oregon to get a national level number I simply times 50 states rarr N = 2600

Large clinicshospital N Large practice potential adopters 1000 Small clinichospital Small practice potential adopters 1600 Advertising effectiveness ldquo a rdquo

Is a parameter to be estimated statically from the data on adopters According to interviews for one HIT application a presentation is usually made to 20ndash40 attendees at average of 3ndash5 conference per year

Range of contacts is 60ndash200 person contacts per year Based on very rough data about 1ndash10 of these contacts through conference advertising adopt the particular HIT = 0003

Adoption fraction for word of mouth ldquo i rdquo

Not every encounter results in adoption The portion of contacts that are suffi ciently persuasive to induce the potential adopter to adopt the innovation is termed here the adoption fraction and denoted i

Rough estimate = 001

Contact rate from word of mouth

Adopters and potential adopters encounter one another with a frequency determined by contact rate

8

Adoption fraction ldquo j rdquo for small practices

The government stimulus available for a 2-year period If all small clinics take advantage it can be estimated

02

4 Field Test

104

4364 Role of Feedback (Fig 419 )

Loop ldquoadopters from advertisingrdquo

( LP rarr adoption _ from _ advertising rarr LAR rarr A rarr LP ) When the innovation or new product is introduced the adoption rate consists entirely of people who learned about the innovation from external sources of information such as advertising

Loop ldquoadopters from word of mouthrdquo ( LP rarr adoption _ from _ word _ mouth rarr LAR rarr A rarr LP ) As the pool of potential adopt-

ers declines while the adopter population grows the contribution of advertising to the total adoption rate falls while the contribution of word of mouth rises Soon word of mouth dominates and the diffusion process plays out as in the logistic diffusion model

Loop ldquogovernment incentives accelerate adoption by small clinicsrdquo

( SP rarr Government _ Incentive rarr SAR rarr A rarr SP ) When government incentive is intro-duced small practice adoption rate is stimulated

4365 Model Verifi cation

For verifi cation purposes the implemented model is compared to the conceptual model To build confi dence unintentional errors were removed and the model was checked for common errors such as units of measure data-entry errors (parameters

PotentialAdopters

Large PracticeLP

Potential AdoptersSmall Practice

SP


N

AdoptionFraction

Contact Ratec

MarketSaturation


a


B

B

B

R

MarketSaturation

Adoption RateLAR

Word ofMouth

AdoptersA


Mouth

Adoption RateSAR

AdoptionFraction

j

Adoption from GovermnetSmall Practice Incentive

$40k

+

+

+

+ +

+

-

+

+

+

i

Fig 419 Vensim model for HIT


105

initial values etc) and time scale errors Process of isolating errors include doubting frame of mind outside doubters walkthrough and hypothesis testing techniques

Doubting Frame of Mind

The goal of this activity is to fi nd scenarios that cause the model to fail so that we can isolate and correct errors Table 43 shows the scenarios tested for and their results

Outside Doubters

The model was shown to an engineering graduate student The student knew and understood the modeled system and its intended operation but it was not involved in its construction Model passed outside doubter check and future additions were suggested

Walkthroughs

The modeler explained the modelrsquos logic to a small group of individuals who are familiar with the system being modeled they included a physician and a health-care researcher Model passed walkthrough and three items were highlighted (1) the Bass model of diffusion was the correct theory to apply and (2) healthcare systems and policies are much more complicated than the current model however this is an acceptable and promising fi rst pass at modeling heath IT adoption (Table 44 )

Table 43 Doubting frame of mind tests

Test Expected result Actual result or fi x

Advertising_effectiveness = 0 No move from potential adopters to adopters

Pass Adoption_fraction_word_mouth = 0 Adoption_fraction_advertising = 0 Advertising_effectiveness = 3000 Make sure that advertising_

effectiveness is always less than 1 Total population N (used for word_of_mouth_effectivness calculation not matching starting population of potential adopters 1000 versus 2000)

Model still runs but wrong shape to adoption curve

Correct

Starting population lt 0 Model still works but wrong shape to adoption curve

Make sure that starting population is correct each time (initial condition)

4 Field Test

106

Hypothesis Testing

To fully exercise the model hypothesis tests with various conditions were developed

Tornado Diagram

Tornado diagram is used to summarize results of varying model parameters and initial values Each parameter and its initial condition are varied from baseline by plusmn10 (Fig 420 )

Table 44 Hypothesis testing cases

Conditions Performance estimate Run and compare

Large_Potential_Adopters = 1000 Advertising will dominate word_of_mouth adoption in the fi rst months Government_adoption will be fastest

Pass Small_Potential_Adoptors = 1600 Adopters = 0 Advertising_Effectiveness = 003 Word_of_mouth_adoption_fraction = 001 Contact_Rate = 8 Government_adoption_fraction = 002 Large_Potential_Adopters = 1000 No adopters at all Pass Small_Potential_Adoptors = 1600 Adopters = 0 Advertising_Effectiveness = 0 Word_of_mouth_adoption_fraction = 0 Contact_Rate = 0 Government_adoption_fraction = 0 Large_Potential_Adopters = 1000 Adopters from government_

incentive only Pass

Small_Potential_Adoptors = 1600 Adopters = 0 Advertising_Effectiveness = 0 Word_of_mouth_adoption_fraction = 0 Contact_Rate = 0 Government_adoption_fraction = 002 Large_Potential_Adopters = 1000 Adopters from large

practices only Pass

Small_Potential_Adoptors = 1600 Adopters = 0 Advertising_Effectiveness = 003 Word_of_mouth_adoption_fraction = 001 Contact_Rate = 8 Government_adoption_fraction = 0


107

4366 Model Validation

Having verifi ed the model it is validated against reference behavior pattern (RBP) comparing the conceptual model to reality In validating the health IT adoption model the two validation ldquoparadigmsrdquo of rational and practical are suitable fi ts The model fi ts the rational (conceptual) paradigm by being believable and one is able to reason about its structureassumptionslogic The model fi ts the practical paradigm because it meets its intended goal to understand how quickly hospitals may adopt HIT (under optimistic conditions) The learning realized from the model justifi es its development cost

Earlier in this report in the RBP we identifi ed two theories of diffusion with two real-world examples of innovation adoption Using a multi-perspective approach (of modeler technical evaluator and user) based on the models conceptual validity operation validity and believability were able to validate that the correct model has been built

Conceptual Validity

The created model exhibits the concepts identifi ed by Rogersrsquo classical theory on Diffusion of Innovation (Rogers amp Rogers 2003 ) Theory states that Diffusion of Innovation includes communicating messages This communication requires chan-nels by which messages move from one individual or unit to another The context of the information sharing determines the experience of the communication and whether ultimately the receivers adopt the innovation According to Rogers adoption evaluations can be objective or subjective However they are often subjective based on information reaching the individual through other communication channels

Communication can occur between hemophilic or heterophilic individuals Homophily refers to how similar two interacting individuals are based on their beliefs education etc Heterophily is the opposite and refers to how different from each other interacting individuals are

Two individuals that are homophilous are able to create more meaningful com-munications One of the barriers in innovation of diffusion is that participants are very heterophilous For example an inventor with an engineering background often has diffi culty communicating merits of his or her innovation to investors or poten-tial nontechnical users

+ndash10AdoptorsLarge_Potential_AdoptersSmall_Potential_AdoptorsAdvertising_EffectinvessWord_of_mouth_adoption_fractionContact_RateGovernment_adoption_fraction

ndash20 ndash10 +10 +20260010001600003001

802

Base

Fig 420 Tornado diagram

4 Field Test

108

Time is involved in three stages (1) the time that passes between fi rst knowledge and adoption or rejection of an innovation (2) the earliness or lateness that an individual adopts compared to the group (3) innovation rate of adoption which is the number of people that adopt it during a particular period of time

Operational Validity

Looking and comparing the model-generated behavioral data is characteristic of other real-world system behavioral data In this regard the Bass diffusion model (Sterman amp Sterman 2000 ) has showed that when the innovation or new product is introduced the adoption rate consists entirely of people who learned about the inno-vation from external sources of information such as advertising As the pool of potential adopters declines while the adopter population grows the contribution of advertising to the total adoption rate falls while the contribution of word of mouth rises Soon word of mouth dominates and the diffusion process plays out as in the logistic diffusion model The Bass model solves the start-up problem of the logistic innovation diffusion model because the adoption rate from advertising does not depend on the adopter population

The developed model is further validated by the Bass model used for modeling epidemics in section 92 of Shermanrsquos Business Dynamics book

Believability

Sterman introduced an S-shaped growth discussing the adoption of cable TV view-ing in households in the 1960s This model is widely accepted and verifi ed in aca-demics and industry Additionally the concept of adoption of cable TV is a concept that many individuals can easily comprehend today Therefore using cable TV adop-tion as an analogy the developed model is rendered believable to majority of indi-viduals Cable TV adoptions and HIT share many of the same diffusion dynamics


When an innovation is introduced and the adopter population is zero the only source of adoption will be external infl uences such as advertising The advertising effect will be largest at the state of the diffusion process and steadily diminish as the pool of potential adopters is depleted Figure 421 shows the behavior of the Bass model for CMP The total population N is assumed 2600 hospitals Advertising effectiveness a and the number of contacts resulting in adoption from word of mouth ci were estimated to be 0005 per year and 016 per year respectively The contribution of adoption from advertising is small in general and on a decline after the fi rst year as seen in Figs 422 and 423 Adoption through word of mouth peeks after the second year


109

Adopters A

4000

4000

1000

00 6 12 18 24 30

Time (Month)Adopters A Current

36 42 48 54 60

3000

Fig 421 Adopters

Adoption Rates

40

2020040

000

0 6 12 18 24 30

Time (Month)

Adoption from Advertising in Conferences Current

Adoption from Government Small Practice Incentive $40k Current

Adoption from Institutional word of Mouth Current

36 42 48 54 60

80400

Fig 422 Adoption rates

Selected Variables

4000

2000500

1000

000

0 6 12 18 24 30


Potential Adopters P Current

Small Practice Potential Adopters S Current

36 42 48 54 60

20001000

Fig 423 Other model variables

4 Field Test

110

This report presented an SD model to study the HIT adoption process in the US healthcare system Using a system dynamics view brings a fresh and much-needed means for studying the adoption process The overview of the model does not show an unexpected dominant loop and more work remains to be done to benefi t more comprehensive conclusions

4368 Limitations

The presented model includes several limitations that should be addressed in future work in order to improve the representation of the system For example the model does not explicitly refl ect the interests of patients payers the high-tech industry etc The proposed model is valuable in providing a common ground for interested research parties and presenting an overall view of the system By expanding the model a simulation for evaluating policies and strategies can be obtained which is a main objective of developing system dynamics theory

References

Bailey D C A (2006) A guide to qualitative fi eld research Thousand Oak CA Pine Forge Press Behkami N A (2009a) Qualitative research interview design for a health IT application

Portland Department of Engineering amp Technology Management Portland State University Working Paper Series

Behkami N A (2009b) A system dynamics model for adoption of healthcare information tech-nology Portland Department of Engineering amp Technology Management Portland State University Working Paper Series

Bodenheimer T Wagner E amp Grumbach K (2002a) Improving primary care for patients with chronic illness Journal of the American Medical Association 288 (14) 1775ndash1779

Bodenheimer T Wagner E amp Grumbach K (2002b) Improving primary care for patients with chronic illness The chronic care model Journal of the American Medical Association 288 (15) 1909ndash1914

Counsell S Callahan C Clark D Tu W Buttar A Stump T et al (2007) Geriatric care management for low-income seniors A randomized controlled trial Journal of the American Medical Association 298 (22) 2623ndash2633

Dorr D Brunker C Wilcox A amp Burns L (2006) Implementing protocols is not enough The need for fl exible broad based care management in primary care

Dorr D Wilcox A Burns L Brunker C Narus S amp Clayton P (2006) Implementing a multidisease chronic care model in primary care using people and technology Disease Management 9 (1) 1ndash15

Dorr D Wilcox A Donnelly S Burns L amp Clayton P (2005) Impact of generalist care man-agers on patients with diabetes Health Services Research 40 (5) 1400ndash1421


Health Care Partners Medical Group ldquoAbout HealthCare Partnersrdquo OHSU ldquoCare Management Plus Program Websiterdquo ORPRN ldquoOregon Rural Practice-based Research Network Websiterdquo Rogers E amp Rogers E (2003) Diffusion of innovations (5th ed) New York Free Press


111

Rubenstein L Parker L Meredith L Altschuler A dePillis E Hernandez J et al (2002) Understanding team-based quality improvement for depression in primary care Health Services Research 37 (4) 1009ndash1029

Shojania K amp Grimshaw J (2005) Evidence-based quality improvement The state of the sci-ence Health Affairs (Millwood) 24 (1) 138ndash150

Shojania K Ranji S McDonald K Grimshaw J Sundaram V Rushakoff R et al (2006) Effects of quality improvement strategies for type 2 diabetes on glycemic control A meta- regression analysis Journal of the American Medical Informatics Association 296 (4) 427ndash440

Steffens D Snowden M Fan M Hendrie H Katon W amp Unutzer J (2006) Cognitive impairment and depression outcomes in the IMPACT study The American Journal of Geriatric Psychiatry 14 (5) 401ndash409


Wolff J Starfi eld B amp Anderson P G (2002) Expenditures and complications of multiple chronic conditions in the elderly Archives of Internal Medicine 162 (20) 2269ndash2276

Young A Mintz J Cohen A amp Chinman M (2004) A network-based system to improve care for schizophrenia The Medical Informatics Network Tool (MINT) Journal of the American Medical Informatics Association 11 (5) 358ndash367

4 Field Test


Chapter 5 Conclusions

Tugrul U Daim and Nima A Behkami

51 Overview and Theoretical Contributions

Despite the fact that diffusion theory was introduced several decades earlier we still donrsquot seem to truly understand how the phenomenon impacts our society In recent years many researchers including Rogers the father of diffusion theory have called for renewed interest in diffusion research One domain as discussed in this proposal which can benefi t from better understanding of diffusion is the fi eld of healthcare specifi cally improvements in understanding adoption and diffusion process for health information technology (HIT) Due to various factors including changing demographics the US healthcare delivery system is facing a crisis and having real-ized this government and private entities are pouring support into advocating HIT adoption-related research amongst other initiatives

One such research that would help with this agenda is the research proposed in this study This study has shown that indeed an extension of Rogersrsquo diffusion the-ory using the extension of dynamics capabilities can help further our understanding of what it takes for successful innovations to diffuse in the US Healthcare industry This report started by proposing a dynamic capability extension to diffusion theory Then it was reasoned for why diffusion theory rather than other adoption theory due to its macro-level property rather than micro is the appropriate theory for the pro-posed study It was also shown that how dynamic capabilities as a one manifestation of ldquofactors of productionrdquo originating from the strategic management fi eld can be used to further characterize the adoptiondiffusion decision and its life cycles



114

This study also shows that use of a case study or grounded theory types of quali-tative research is necessary to do an exploratory study of the problem Itrsquos through this type of research that we hope to gain in-depth understanding of situation and meaning for those involved In future research the results of such mostly qualitative- based research can be inputs for hybrid or purely quantitative method research on the same topics and in the same fi eld after the problem and whatrsquos really going on have been structured a little more with qualitative methods Additionally in this report various system modeling tools were compared and contrasted for purposes of analysis documentation and communication of research fi ndings It was shown that for this research the use of the Unifi ed Modeling Languages (UML) is a productive fi t UML benefi ts from having constructs for both showing static and dynamics aspects of the system UML also supports multi-perspective views of the problem which was also shown here to be essential for understanding HIT diffusion innovation

In addition to comparing and discussing various methodologies theories and aspects of the problem in this document the proposed research was accompanied and verifi ed for demonstrability and validity by conducting a fi eld study at Oregon Health amp Science University with its Care Management Plus team CMP a HIT- based innovation is an ambulatory care model for older adults and people with multiple conditions components of CMP include software clinic business pro-cesses and training The fi eld study was conducted using site readiness survey and expert interviews The data collected was analyzed using thematic analysis includ-ing open and focus coding Models were created using diffusion and dynamic capa-bility theory and they were documented using multi-perspectives and the UMLrsquos structural and behavior diagrams A system dynamics model based on Bass diffu-sion model was also created and demonstrated And in conclusion conducting the fi eld study was able to demonstrate that the research objectives (generally for pro-posal and specifi cally for fi eld study) were met

Objectives 1 and 2 were about showing that DOI and dynamic capabilities can be combined in a meaningful manner

Objective 2 Demonstrate that dynamic capability theory can be used and how to meaningfully extend diffusion of innovation theory

This objective was demonstrated based on the model constructed from site data collection as described in Fig 51 where itrsquos the clinic need(s) that drives them to consider adopting an innovation And this need and decision have barriers andor infl uences that can affect them in a negative or positive way Additionally as that same fi gure shows whether a clinic has the needed capabilities to adopt or not becomes a pressure point as either an positive infl uence (in case they already have the capabilities) or a barrier (in case clinic doesnrsquot have the needed capability yet)

In further support of the Objective 2 Fig 52 a depiction of the ldquodynamic capa-bility development stagerdquo shows the sequence and time frame of acquiring capabili-ties prior to truly adopting an innovation These two points mentioned indeed validate and support the second objective which helps in drawing the picture in Fig 53 that demonstrates how dynamic capabilities can be used to meaningfully extend diffusion of innovation theory

TU Daim and NA Behkami

115

Objective 1 Identify some dynamic capabilities needed for successful implementa-tion of HIT (Care Management Plus OHSU)

In supporting Objective 1 data collection and analysis from OHSU CMP adop-tion verifi ed that indeed dynamic capabilities needed for successful implementation of HIT can be defi ned Compliant with classifi cations from prior work namely Fig 54 Barneyrsquos classifi cation of factors of production (aka capabilities compe-tences) from Resource Based Theory and Fig 55 Itamirsquos classifi cation of assets for competitive advantage a classifi cation of capabilities for CMP adoption was devel-oped and the taxonomy is shown in Fig 56



5 Conclusions

116





117

Objective 3 Use Software and system engineering methods including ldquo4 + 1 viewrdquo for perspectives and UML to demonstrate documentation and analysis

Support for Objective 3 in the fi eld study was demonstrated by the choice of qualitative data collection methodology The data collection was analyzed using standard qualitative thematic analysis similar to grounded theory with fi rst open coding and then focused coding Then the analysis model was built and documented using UML and later analyzed (in the form of discussing results) using static and behavioral aspects of the system Examples of software engineering artifacts pro-duced in the study included the static UML diagrams of Fig 57 fi eld study class diagram Fig 58 fi eld study package diagram the behavioral UML diagrams of Fig 59 fi eld study use case and the sequence diagrams of Fig 510 ldquoknowledge stagerdquo Fig 52 ldquodynamic capability development stagerdquo and the UML state chart Fig 511 fi eld study start chart for adoption decision The scenarios and use cases used in building the behavioral UML artifacts just mentioned are compliant with the ldquo4 + 1 viewrdquo model for describing system architectures

Generation of these UML diagrams verifi es that indeed software engineering thinking and tools were successfully applied to the research These UML artifacts and the multi-perspective analysis in this document support Osterweilrsquos hypothesis that process is software in spite of domain (Osterweil 1987 1997 ) and demon-strates that software principles also hold for social and organizational processes

Objective 4 Build and run a small simulation of the DOI theory extension using system dynamics

A complete system dynamics model was developed for the fi eld study and docu-mented in this report The model was based on Rogersrsquo diffusion theory and Bass diffusion model In the model adoptiondiffusion rates for CMP at OHSU were


Technology

Work Flow

CMPSoftware

EHRIntegration


Training


PhysicianTraining





5 Conclusions

118

modeled using word of mouth and advertising A complete set of system dynamics components were developed including causal loop diagram (CLD) (Fig 512 ) and stock and fl ow system dynamic model in Vensim software (Fig 513 ) The model was extensively validated and verifi ed using popular methods Verifi cation was per-formed with the techniques of doubting frame of mind outside doubter walk-through hypothesis testing and tornado diagram testing Model was validated using conceptual validity operational validity and the believability test Figure 514 an S-curve of adopter population along with Figs 515 and 516 growth curves showing adoption rates were outputted by the model The generate model and its outputs show that itrsquos possible to effectively model the HIT adoption and diffusion process in a good enough way so that we can experiment with scenarios and forecasting In future research this model can be extended to integrate dynamic capabilities



119


5 Conclusions

120

In conclusion all objectives of the research proposal were met and demonstrated through preparation of this document Along with the results of the included feasi-bility fi eld study itrsquos verifi ed that indeed there is a need for extension of Rogersrsquo theory Dynamic capabilities are a good fi t candidate integrating with Rogersrsquo diffu-sion theory and extending it Additionally the combination of the presented theories and methods in this document can assist healthcare stakeholders understand their problems and solution more effi ciently as they set new policies and investment for their support

Government

Supplier

Care Seeker

Adoption

Rejection

Dissemenation

DevelopCapabilities

Manage Capabilities

Reconfirmation

Usage

Implementation

Provider

Payer




121

NoNo

No


(CMP Software)


(Receive Payments)




AdoptInnovation

RejectInnovation




Yes Yes Yes





PotentialAdopters

Large PracticeLP


SP


N

AdoptionFraction

Contact Ratec

MarketSaturation


a


B

B

B

R

MarketSaturation

Adoption RateLAR

Word ofMouth

AdoptersA


Mouth

Adoption RateSAR

AdoptionFraction

j


$40k

+

+

+

+ +

+

-

+

+

+

i


5 Conclusions

122

Adopters A

4000

4000

1000

00 6 12 18 24 30


36 42 48 54 60

3000

Fig 514 Adopters

Adoption Rates

40

2020040

000

0 6 12 18 24 30

Time (Month)




36 42 48 54 60

80400


Selected Variables

4000

2000500

1000

000

0 6 12 18 24 30




36 42 48 54 60

20001000



123

52 Recommended Proposition for Future Research

The following research propositions are formulated in the context of information discussed in the previous sections

Proposition 1 Even though the clinics obtain knowledge of a new innovation and decide to adopt it it is actually the acquirement of the needed minimum set of capabilities (for meaningfully using the innovation) which strongly infl uences successful adoption

Proposition 2 Only meaningful adoption can be considered the ldquoreal adoptionrdquo and should be the main type used in planning and management Meaningful is using the adopted innovation according to defi ned set of criteria that has some type of agreed on or expected benefi t (eg the recent HIT meaningful use intuitive and measures sponsored by the US Health and Human Services [HHS] department)

Proposition 3 Acquiring capabilities that need to be implemented and using an innovation (part of adoption) will take time The velocity by which a potential adopter can acquire the needed capabilities will strongly infl uence adoption rates and overall diffusion

Proposition 4 Taking inventory and tracking of capabilities across a similar or competing group of fi rms regions or situations can act as a scoreboarddashboard of sorts for better analysis decision making and overall general stra-tegic management

Proposition 5 Investment in acceleration of acquiring of capabilities (for successful adoption) rather than the classical and hard-to-track general fi nancial invest-ments (or the likes) by sponsors can strongly infl uence diffusion rates

Proposition 6 Classical diffusion theory needs to be extended to account for the period in time and effort that fi rms (in this example clinics) expand to contem-plate or acquire capabilities

Proposition 7 When an adopter (clinic) decides to adopt an innovation either it suc-cessfully acquires the needed capabilities and the conditions to use the innova-tion or the adoption eventually fails

Proposition 8 The Software Engineering techniques of Object-Oriented Analysis and Design (OOAD) in conjunction with UML can be used to study social and organizational processes in new and more effective ways

References

Osterweil L J (1987) Software processes are software too In Proceedings of the 9th International Conference on Software Engineering (p 13)

Osterweil L J (1997) Software processes are software too revisited an invited talk on the most infl uential paper of ICSE 9rsquo paper presented to the International Conference on Software Engineering In Proceedings of the 19th International Conference on Software Engineering Boston

5 Conclusions

Part II Evaluating Electronic Health Record Technology Models and Approaches

Liliya Hogaboam and Tugrul U Daim

This part reviews electronic health records and considers technology assessment scenarios for multiple purposes These are the following

(a) The adoption of EHR with focus on barriers and enablers (b) The selection of EHR with focus on different alternatives (c) The use of EHR with focus on impacts

The exploration will assume that the adoption selection and use of EHR relate to the ambulatory EHR accepted in small practices

The fi rst section will highlight the gaps each scenario will address and list match-ing research goals and research questions

The second section will describe a research project matching each objective above In each case we will explain the methodology of choice describe other methods that may also be considered and list the reasons to justify the methodology we are choosing We will develop a preliminary model for each research and list the theories behind

The third section will explain what kind of data we will need and how we will acquire it We will consider the following in this section

(a) The required data size in terms of number of data points respondents or experts

(b) Data access issues such as sample size or access to experts

The fourth section will explain the types of analyses to be done for each scenario We will consider the following in this section

(a) Types of metrics used to measure accuracy (b) Validity and reliability in each case


Chapter 6 Review of Factors Impacting Decisions Regarding Electronic Records


L Hogaboam bull T U Daim () Portland State University Portland OR USA e-mail tugruludaimpdxedu

61 The Adoption of EHR with Focus on Barriers and Enablers

Letrsquos explore the gaps found in the literature that relate to adoption of EHR with focus on enablers and barriers

bull The impact and signifi cance of implementation barriers and enablers (fi nancial technical social personal and interpersonal) have not been satisfactorily studied

bull Signifi cance of the relationship of factors of perceived usefulness perceived ease of use and perceived benefi ts on attitude toward using EHR in ambulatory set-tings has not been adequately shown with global studies

bull Lack of studies in the USA involving TAM models and research on a global scale

bull Lack of quantitative studies in EHR adoption toward small ambulatory settings

Palacio Harrison and Garets ( 2009 ) provided a research that documented an increased adoption of EHR in the US hospitals through the period of 2005ndash2007 The authors also indicate potential barriers of HIT implementation as cost lack of fi nancial incentives for providers and the need for interoperable systems

A systematic literature review on perceived barriers to electronic medical record (EMR) adoption identifi ed eight categories (fi nancial technical time psychologi-cal social legal organizational and change process Boonstra amp Broekhuis 2010 ) The study is bibliographical and explorative in nature and the barriers are not tested

128

for signifi cance rather interpreted as guidelines for EMR adopters and policy mak-ers and as a foundation for future research

Taxonomy of the primary and secondary barriers is listed in Table 61 below (Boonstra amp Broekhuis 2010 )

Boonstra and Broekhuis ( 2010 ) also noted that barriers in primary categories vary signifi cantly between small and large practices since small practices face greater diffi culties overcoming those barriers Those differences may greatly impact the focus and the effort needed to overcome fi nancial technical and time barriers

Table 61 Taxonomy of the primary and secondary barriers (Boonstra amp Broekhuis 2010 )

Primary category Primary barriers

Secondary category Associated barriers

Financial bull High start-up costs bull High ongoing costs bull Uncertainty about return

on investment (ROI) bull Lack of fi nancial

resources

Psychological bull Lack of belief in EMRs bull Need for control

Technical bull Lack of computer skills of the physicians andor the staff

bull Lack of technical training and support

bull Complexity of the system bull Limitation of the

system bull Lack of customizability bull Lack of reliability bull Interconnectivity

standardization bull Lack of computers

hardware

Social bull Uncertainty about the vendor

bull Lack of support from other external parties

bull Interference with doctor-patient relationship

bull Lack of support from other colleagues

bull Lack of support from the management level

Time bull Time to select purchase and implement the system

bull Time to learn the system bull Time to enter data bull More time per patient bull Time to convert the

records

Legal bull Privacy or security concerns

Organizational bull Organizational size bull Organizational type

Change process bull Lack of support from organizational culture

bull Lack of incentives bull Lack of participation bull Lack of leadership

L Hogaboam and TU Daim

129

While the study by Lorenzi et al ( 2009 ) reviews the benefi ts and the barriers of EHR in ambulatory settings it does not address EHR models or the barriers associ-ated with interconnectivity of EHR The authors indicate that more research is needed in those fi elds

A group of Canadian researchers (McGinn et al 2011 ) conducted a systematic literature review of EHR barriers and facilitators The review categorized the stud-ies based on the user groups (physicians healthcare professionals managers and patients) while the differences of clinic size and type of setting and the factors that are particular to each type were not discussed The study though is interesting in the sense of general ranking of the factors and commonalities in studies of those factors Technical issues are at the top of the list while organizational factors are not that common (McGinn et al 2011 ) The ranking (from most to least common) is shown in Table 62

The three studies mentioned in McGinn et al ( 2011 ) related to ambulatory care were exploratory andor qualitative in nature

Table of categories of studies examined through literature review is shown in Table 63

Electronic health records have been a topic of research in various countries throughout the world some with high rates of adoption and implementation and others with low ones While researching and working on my independent studies I have found a number of studies in foreign countries (Bates et al 2003 Rosemann et al 2010 Were et al 2010 ) High transition to EHR technology was reported in Australia New Zealand and England through fi nancial support and incentives evidence-based decision support standardization and strategic framework (Bates et al 2003 )

Those studies give a possibility to engage a similar research or test a certain framework here in the USA while studying adoption of EHR by small ambulatory clinics In Table 64 I have summarized some of those important studies

The US research in EHR adoption lacks rich involvement of TAM with structural equation modeling especially in ambulatory care While researching EHR adoption

Table 62 Common EHR implementation factors ranked by the number of studies

Common EHR implementation factors

Number of studies

Design or technical concerns 22 Privacy and security concerns 21 Cost issues 19 Lack of time and workload 17 Motivation to use EHR 16 Productivity 14 Perceived ease of use 13 Patient and health professional interaction 12 Interoperability 10 Familiarity ability with EHR 9

6 Review of Factors Impacting Decisions Regarding Electronic Records

130

in my independent studies projects and performing thorough literature reviews there were some interesting studies on EHR adoption in hospitals that deserve atten-tion Thus the researchers in New York built extended and modifi ed TAM with external variables (age specialty position in hospital attitudes toward HIT cluster ownership) and latent variables of pre- and post-adoption (Vishwanath Brodsky amp Shaha 2009 ) The signifi cant links of the external variable impacts were as follows age rarr perceived usefulness attitudes toward HIT rarr perceived usefulness as well as ease of use and position in hospital and cluster ownership rarr perceived ease of use (Vishwanath et al 2009 ) A study of physicianrsquos adoption of electronic detailing proposed the model that included innovation characteristics (perceived relative advantage compatibility complexity trialability observability) communication channels (peer infl uence) social system (academic affi liation presence of restric-tive policy urban vs rural) and physician characteristics (specialty years in prac-tice attitudes toward the information usefulness)

Some statistical studies related to EHR barriers have been performed For exam-ple a study by Valdes et al had one of the main objectives of the characterization of user and non-users of EHREMR software and identifi ed potential barriers to EHR proliferation (Valdes et al 2004 ) They performed a secondary analysis of member survey data collected by the American Academy of Family Physicians (AAFP) as well as the number of different software vendors reported by users of EHREMR The researchers reported at least of 264 different EHREMR software

Table 63 Categories of related studies examined in preparation to the exam

Type of study Research works

Qualitative or empirical evaluation of TAM or other acceptance models

Chiasson et al ( 2007 ) Dillon and Morris ( 1996 ) Im Kim amp Han ( 2008 ) Premkumar and Bhattacherjee ( 2008 ) Tsiknakis et al ( 2002 ) Szajna ( 1996 ) Scott and Briggs ( 2009 ) Yang ( 2004 ) Yusof et al ( 2008 )

Exploration of particular aspects of the HIT adoption

Burton-Jones and Hubona ( 2006 ) Cresswell and Sheikh ( 2012 ) Degoulet Jean and Safran ( 1995 ) Haron Hamida and Talib ( 2012 ) Janczewski and Shi ( 2002 ) Jeng and Tzeng ( 2012 ) Folland ( 2006 ) Hagger et al ( 2007 ) Karahanna and Straub ( 1999 ) Kim and Malhotra ( 2005 ) Lee and Xia ( 2011 ) Malhotra ( 1999 ) Martich and Cervenak ( 2007 ) McFarland and Hamilton ( 2006 ) Melone ( 1990 ) Shin ( 2010 ) Storey and Buchanan ( 2008 ) Viswanathan ( 2005 )

Applications of TAM and its derivatives in other countries

Jimoh et al ( 2012 ) Maumlenpaumlauml et al ( 2009 ) Polančič Heričko and Rozman ( 2010 ) Ortega Egea and Romaacuten Gonzaacutelez ( 2011 ) Yu Li and Gagnon ( 2009 )

Frameworks of IT adoption in healthcare that differed greatly from TAM

Davidson and Heineke ( 2007 ) Hatton et al ( 2012 )

Frameworks of IT adoption experimental in nature

Andreacute et al ( 2008 ) Ayatollahi Bath and Goodacre ( 2009 ) Becker et al ( 2011 )


131

Table 64 Summary of studies and a variety of methodologies and analyses used

Authors Country Study

Ludwick and Doucette ( 2009 )

Canada Lessons-learned study from EHR implementation in seven countries Concluded that systemsrsquo graphical user interface design quality feature functionality project management procurement and user experience affect implementation outcomes Stated that quality of care patient safety and provider-patient relations were not impacted by system implementation

Aggelidis and Chatzoglou ( 2009 )

Greece Examined the use of health information technology acceptance with the use of modifi ed and extended TAM Facilitating conditions (new computers support during information system usage and fi nancial rewards) was the main factor that positively impacted behavioral intention Perceived usefulness and ease of use were the most important factors of direct infl uence on behavioral intention Anxiety during system use shown to be reduced by facilitating conditions perceived usefulness and self-effi cacy

Melas et al ( 2011 )

Greece Researchers implemented confi rmatory factor analysis (CFA) structural equations modeling (SEM) and multi-group analysis of structural invariance (MASI) in a study of examining the intention to use clinical information systems in Greek hospitals The results showed direct effect of perceived ease of use on behavioral intention to use

Chen and Hsiao ( 2012 )

Taiwan Modifi ed TAM was used for IT acceptance research Confi rmatory factor analysis for reliability and validity of the model and SEM for causal model estimation were used According to the results of the study top management support had signifi cant impact on perceived usefulness while project team competency and system quality signifi cantly impact perceived use

Hung Ku and Chien ( 2012 )

Taiwan Modifi ed TBP was used and results indicated that physiciansrsquo intention to use IT was signifi cantly impacted by attitude subjective norm and perceived behavior control Studied impactful factors included interpersonal infl uence personal innovativeness in IT and self-effi cacy

Cheng ( 2012 ) Taiwan The researchers looked at IT adoption by nurses in two regional hospitals with extended TAM where the other factors impacting intention to use consisted of learner-system interaction instructor-learner interaction learner-learner interaction and fl ow

Pareacute and Sicotte ( 2001 )

Canada The study concluded that IT sophistication and perceived usefulness of clinical applications are moderately to highly correlated while no relationship was found between the level of sophistication and perceived usefulness of administrative applications

Moores ( 2012 )

France The researchers found that there are differences in signifi cant impacts depending on the experience of the users while applying extended and modifi ed TAM in studying adoption of clinical management system by hospital workers

(continued)


132



Handy Hunter and Whiddett ( 2001 )

New Zealand

Conducted longitudinal study into primary care practitionersrsquo adoption of electronic medical record system for maternity patients in a large urban hospital applying TAM with additional variables like individual characteristics system characteristics organizational characteristics and system acceptability They concluded that technical aspects of information system should not be considered in isolation from organizational and social context

Van Schaik et al ( 2004 )

The UK The researchers outlined the need to consider the balance of benefi ts (perceived advantages) and costs (disadvantages) of a new system in technology acceptance modeling

Chow Chan et al ( 2012 ) Chow Herold et al ( 2012 )

Hong Kong

Included external variable for TAMmdashcomputer self-effi cacy in study of the factors impacting the intention to use clinical imaging portal

Pai and Huang ( 2011 )

Taiwan Study of HIT adoption by district nurses head directors and other related personnel where TAM was used with external variables (information quality service quality and system quality)

Duumlnnebeil et al ( 2012 )

Germany SEM model with six external variables (intensity of IT utilization importance of data security importance of documentation eHealth knowledge importance of standardization process orientation) was used to study physicianrsquos acceptance of e-health in ambulatory care The researchers stated that the diversities of public systems throughout the world should be integrated into TAM research in order to correctly explain the drivers Perceived importance of standardization and perceived importance of current IT utilization were the most signifi cant

programs in use which indicates highly fragmented market which authors note as a barrier to proliferation Statistical analysis involving demographic data was per-formed and linear regression was utilized to analyze the variance in EHREMR interest and the amount of willingness to pay (Valdes et al 2004 )

One important study was done to assess intensive care unit (ICU) nursesrsquo accep-tance of EHR technology and examine the relationship between EHR design imple-mentation factors and user acceptance (Carayon et al 2011 ) This study was regional (northeastern USA) and local to the medical center and nurses working in four ICUs It tested only two major components of TAM usability (ease of use) and usefulness Three functionalities of EHR (computerized provider order entry (CPOE) the electronic medication administration record (eMAR) and nursing doc-umentation fl ow chart) were studied using multivariate hierarchical modeling The results showed that EHR usability and CPOE usefulness predicted EHR acceptance while looking at the periods of 3 and 12 months after implementation (Carayon et al 2011 )


133

One study of an outpatient primary care practice at the Western Pennsylvania hospital was conducted for research of social interactionsrsquo infl uence on physician adoption of EHR system (Zheng et al 2010 ) This empirical study involved 55 physiciansmdasha small sample size (most of them graduating or completing the residency program) The researchers used two SNA measures (ldquodensityrdquomdashldquothe number of social relations identifi ed divided by the total number of relations that could possibly be presentrdquo and ldquoFreemanrsquos degree centralityrdquomdashldquothe degree to which a social network is organized around its well-connected central networksrdquo) (Zheng et al 2010 ) Correlation method was used to capture the similarity between interaction patterns of pairs while quadratic assignment procedure (QAP) was used to test network correlations Network effects model (NEM) was used to evaluate the impact of social network structures on the measurements of the physicianrsquos utiliza-tion rates of the EHR system

The use of social contagion lens was engaged in a study of EHR adoption in US hospitals (Angst et al 2010 ) The researchers used the data from a nationwide annual survey of care delivery organizations in the USA (conducted by HIMSS Analytics) and applied the heterogeneous diffusion model technique for their hypothesis testing (Angst et al 2010 )

62 The Selection of EHR with Focus on Different Alternatives

In the study of EHR selection based on different alternatives certain gaps emerge from the body of literature

bull A comprehensive decision-making model of EHR selection in small ambulatory settings has not been successfully introduced andor implemented

bull Combination of elements of human criteria (perceived usefulness and ease of use) fi nancial technical organizational personal and interpersonal criteria in one decision-making model has not been performed

bull There is a lack of large-scale studies in the USA using HDM for EHR selection for small ambulatory setting

Ash and Bates ( 2005 ) indicate that comprehensive national surveys with a high response rate are not available and data in their study comes from the industry resources that may have some vested interests in EHR usage or selection The authors also indicate that small practices are less likely to adopt comparing to larger ones with various adoption gaps between the types of practices (pediatric internal medicine etc) Another interesting aspect provided by the authors is that there is a considerable amount of international experience (for example Sweden the Netherlands and Australia) that the USA can gain insights from (Ash amp Bates 2005 )


134

In the selection of EHR the decision makers should consider factors that are environmental (fi nancial and safety social and behavioral) organizational per-sonal and technical (for example ability of systems to interoperate with each other) in nature (Ash amp Bates 2005 )

Study by Lorenzi et al stresses the need for fl exible change management strategy for EHR introduction in a small practice environment while detailing the EHR implementation through stages of decision selection pre-implementation imple-mentation and post-implementation (Lorenzi et al 2009 )

One important study about the attitudes of physicians toward EHR implementa-tion was performed by Morton and Wiedenbeck using the framework grounded in diffusion of innovations theory and TAM while being conducted at the University of Mississippi Medical Center (UMMC) (Morton amp Wiedenbeck 2009 ) The research-ers acknowledged that their fi ndings might not be generalized to other physicianrsquos offi ces since the study was limited to one large healthcare system however they revealed an overwhelming need for customizable and fl exible EHR products (Morton amp Wiedenbeck 2009 )

One important observational study on selection of EHR software discussed chal-lenges considerations and recommendations for identifying solutions mainly tar-geted toward small practices and presented fi ndings on installation training and use of EHR software as well as a detailed industry analysis of over 200 vendors and their offerings (Piliouras et al 2011 ) According to their analysis successful EHR system implementation has certain aspects (Piliouras et al 2011 )

bull The American Recovery and Reinvestment Act (ARRA) government mandates knowledge and conformance

bull Application of techniques in operations management systems analysis and change management

bull Learning EHR software bull Secure information technology infrastructure installation and maintenance bull Establishment of backup and disaster recovery procedures and processes

Piliouras et al (2011) also describe major challenges and recommendations

1 Conforming to ARRA mandates 2 Adherence to industry best practices 3 Installation and maintenance of secure IT infrastructure 4 Learning complex software

(a) Availability and quality of training (b) Quality software design

EHR systems could be either of a ldquoclient-serverrdquo or a ldquoservice-in-a-cloudrdquo infra-structure with the latter one with data maintained on dedicated vendor facilities and accessed over the Internet having capability of reducing capital outlay for computer and network infrastructure and associated upgrades and allowing expenditures to be


135

monetized as a fi xed monthly expense (Piliouras et al 2011) At the same time the practice needs to make sure that the vendor could satisfy the following criteria

bull Access privileges bull Regulatory compliance bull Data location bull Data segregation bull Data recovery bull Monitoring and reporting bull Vendor viability

The key differences between the two types of EHR software infrastructure taken from small practicersquos offi ce viewinterest are described in Table 65

Cloud computing in healthcare IT particularly for EHR also should not be con-sidered as a single concept with the same privacy and security concerns Zhang and

Table 65 Two types of EHR software infrastructure (Piliouras et al 2011)

Feature

Infrastructure type

Service-in-a-cloud Client-server

Location of system code and execution

Remote (mainly at vendorrsquos premise)

Local (mainly at doctorrsquos offi ce)

System data control Less More Same vendor system migrationextension

Easier Harder and more complex

Security More Less Hardware requirements Fewer More Response time Depends on the Internet

service provider (ISP) network provisioning and EHR vendor

Depends on the system maintenance and confi guration

Reliability Depends on the Internet service provider (ISP) network provisioning and EHR vendor

Depends on the system maintenance and confi guration backup and recovery process

Remote access via the Internet

Easy Possible with extra security measures

Maintenance Easier Harder Data synchronization for clinic with multiple offi ces

Easier Harder

Data backup and disaster recovery

Easier and cheaper Requires extra expense and technical support

Initial cost Lower Higher Total life cycle cost (3ndash5 years)

Lower Higher


136

Table 66 Taxonomy of healthcare clouds (Zhang amp Liu 2010)

Healthcare cloud product layer

Explanation of capability for consumers

Control from the consumerrsquos side Security and privacy

Applications in the cloud (Software as a ServicemdashSaaS)

Can use the providerrsquos applications running on a cloud infrastructure

None Provided as an integral part of the system

Platforms in the cloud (Platform as a ServicemdashPaaS)

Can deploy consumer-created or -acquired applications written using supported programming languages and tools

No control over cloud infrastructure (network servers operating systems storage) control over the deployed applicationshosting environment confi gurations

Lower system levelmdashbasic security mechanisms (end-to-end encryption authentication and authorization) Higher system levelmdashthe consumers defi ne application- dependent access control policies authenticity requirements etc

Infrastructure in the cloud (Infrastructure as a ServicemdashIaaS)

Can provision processing storage networks and other fundamental computing sources to deploy and run arbitrary software operating systems and applications

No control over cloud infrastructure control over operating systems storage deployed applications possibly limited control of select networking components (host fi rewalls)

The healthcare application developers hold full responsibility

Liu ( 2010 ) provide taxonomy of healthcare clouds stressing those issues of privacy and security (Table 66 )

A very recent qualitative phenomenological study (ten interviews with physi-cians) in south-central Indiana looked into physicianrsquos view and perceptions of EHR which could help in the study of EHR selection (Hatton Schmidt amp Jelen 2012 ) Most reported and fi ltered challenges and benefi ts (Hatton et al 2012 ) are shown in Table 67

Roth et al ( 2009 ) also studied EHR use and stated that many EHR users may not always use EHR fully but only a fraction of EHR capabilities Some of the features and possibilities for documentation or structured recording of information may be ignored opted out or dismissed at the beginning of setup and use and the data may not be easily accessible through the automated extraction schemes when needed Free text fi elds (commonly used for patientsrsquo complaints) require natural language processing software While a lot has been accomplished in the area of natural lan-guage parsing and identifi cation many challenges still remain in the area of detec-tion of targeted clinical events from free text documents (Roth et al 2009 ) Through


137

the focus groups participating in the study the researchers learned that providers want EHR that requires less complexitymdasha minimum of keystrokes mouse clicks scrolling window changes etc While the fl exibility that accommodates various data entry styles has been built in it could complicate data extracting accuracy and effi ciency (Roth et al 2009 )

63 The Use of EHR with Focus on Impacts

Below are the gaps found through an extensive literature review of EHR impacts

bull The use of EHR in ambulatory settings and impact on quality of healthcare have not been adequately studied

bull The magnitude of the impacts from EHR use in the small ambulatory setting has not been adequately studied

bull The effects of user satisfaction and quality impacts in ambulatory settings are not adequately analyzed with quantitative measures

Table 67 Challenges and benefi ts of EHR (Hatton et al 2012 )

Challenges Benefi ts

Loss of control (major)

1 Procedural or workfl ow challenges 2 The EMR causing them to work slowly 3 The pace of technology obsolescence 4 Too much information is available to

patients or needs to be gathered from patients

5 The cognitive distraction during physicianrsquos use of the computer in the examination room

Supporting physician decisions (major) (particularly useful in noting drug allergies and drug-to-drug interactions)

Attitude of providers

1 Sense that paper charts were easier than electronic records

2 Technical ability of the physician or lack of it

3 Physicianrsquos age

Physician access to information (major) (structured and retrievable format integrating patient data so that demographic fi nancial and medical information could be accessed transmitted and stored in a digital format)

Financial negatives

1 Cost of the software 2 Cost of maintenance 3 Cost of the support personnel

Financial improvements (major) (sense that EMR makes them cost effective and more effi cient being proactive with patients increases patient loads getting government incentives opportunities for data mining)

Continuity of care (referrals and care coordination)

Time improvements (improved communication with staff though the EMR messaging capability) Patient access to information (better informed patients could provide opportunities for improved care which could also lead to healthier outcomes)


138

bull There is a lack of large-scale studies in the USA using HDM for EHR impacts in small ambulatory setting

While the attention of greater quality of care always persists with research focus on how providers patients and policies could affect factors that infl uence the quality of care despite high investments (over 17 trillion annually) and increased healthcare spending the USA ranks lower compared to other countries on several health measures (Jung 2006 Girosi Meili amp Scoville 2005 ) Jung listed specifi c benefi ts of HIT in regard to quality of care

bull Medical error reduction (improved communication and access to information through information systems could have a great impact in this area)

bull Adherence support (the decision support functions embedded in EHR can show the effect of HIT on adherence to guideline-based care and enhancing preventive healthcare delivery (Dexter et al 2004 Overhage 1996 Jung 2006 )

bull Effective disease management (potential to improving the health outcomes of patients with specifi c diseases)

Jung ( 2006 ) also explained that while effi ciency is a complex concept some effi ciency savings have been reported by researchers as a result of HIT adoption as reduction in administrative time (Wong 2003 Jung 2006 ) and hospital stays Positive effects on cost were documented as

bull Improved productivity bull Paper reduction bull Reduced transcription costs bull Drug utilization bull Improved laboratory tests

Additional benefi ts reported by several (Bates et al 1998 Agarwal 2002 Jung 2006 ) were as follows

bull Improved patient safety (from safety alerts and medication reminders of EHR system)

bull Improved regulatory compliance (record keeping and reporting compliance with federal regulations including Health Insurance Portability and Accountability Act (HIPAA))

Increased emphasis on preventive measures and early detection of diseases primary care intermittent healthcare services and continuity of care are prevalent in our ever-changing healthcare domain (Tsiknakis Katehakis amp Orphanoudakis 2002 ) Information and communication technologies are taking lead in this dynamic environment with the need for improved quality of healthcare services and cost control (Tsiknakis et al 2002 ) Another important trend in the healthcare system is movement toward shared and integrated care (integrated electronic health recordmdashiEHR) growth of home care through sophisticated telemedicine services (facili-tated by intelligent sensors handheld technologies monitoring devices wireless technologies and the Internet) which pushes the need for EHR that supports qual-ity and continuity of care (Tsiknakis et al 2002 ) While the researchers enlisted a


139

number of valuable benefi ts they would need to be examined and the relationships of EHR impacts and their signifi cance would need to be studied further The envi-sioned benefi ts are listed in Fig 61 and Table 68

A systematic review by Goldzweig lists only a few studies of commercial health IT system use with reported results and experiences of the impacts of EHR imple-mentation (Goldzweig et al 2009 ) In one of the studies described in their publica-tion authors concluded that EHR implementation (EpiCare at Kaiser Northwest) had no negative impact on quality of care measures of quality like immunizations and cancer screening did not change (Goldzweig et al 2009 ) In the second study of implementation of a commercial EHR in a rural family practice in New York the authors report various fi nancial impacts (average monthly revenue increase due to better billing practices) clinical practice satisfaction as well as the support of the core mission of providing care

Agency for Healthcare Research Quality defi ned quality healthcare as ldquodoing the right thing at the right time in the right way to the right person and having the best pos-sible resultsrdquo (Agency for Healthcare Research Quality 2004 in Kazley amp Ozcan 2008 )

One important retrospective study in the USA by Kazley and Ozcan looked at EMR impacts on quality performance in acute care hospitals (Kazley amp Ozcan 2008 ) Retrospective cross-sectional format with linear regression is used in order to assess the relationship between hospital EMR use and quality performance (Kazley amp Ozcan 2008 ) The authors concluded that there is a limited evidence of the relationship between EMR use and quality There are some interesting observa-tions made by the authors toward measuring quality and they describe it as a multi-

Vital health informaon is

available 24 hrs a day 7 days a

week regardless of the paents

locaon

Healthcare praccioners are able to view paents relevant medical historybullmore effecve

and efficient treatment

bullmore quality me spent with the paent

Access to informaon of previous lab results or medical procedurebullreduce the

number of redundant procedure

bullresults in greater cost savings

Enhanced ability of health planners and administrators to develop relevant healthcare policies with EHR informaonbullinformaon for

researchersbullpopulon health

stascsbullimproved quality

of care

Access to individuals own personal health recordsbullindividuals can

make informed choices about opons available

bullopportunity to excercise greater control over their health

Fig 61 Envisioned EHR benefi ts


140

faceted and complex construct which may grow and change Ten process indicators related to three clinical conditions acute myocardial infarction congestive heart failure and pneumonia are used to measure quality performance based on their validity (Kazley amp Ozcan 2008 ) The authors noted that they didnrsquot measure such elements of quality as patient satisfaction and long-term outcomes and that EMR implementation and practice should be further explored

Leu et al ( 2008 ) performed a qualitative study with in-depth semi-structured inter-views to describe how health IT functions within a clinical context Six clinical domains were identifi ed by the researchers result management intra-clinic communication patient education and outreach inter-clinic coordination medical management and provider education and feedback Created clinical process diagrams could provide clinicians IT and industry with a common structure of reference while discussing health IT systems through various time frames (Leu et al 2008 )

Table 68 Potential benefi ts and their related features

Potential benefi t Related EHR features

Dissemination and distribution of essential patientclient information

Open communication standards over transparent platforms

Improved protection of personal data Encryption and authentication mechanisms for secure access to sensitive personal information auditing capabilities for tracking purposes

Informed decision making resulting in improved quality of care

Semantic unifi cation and multimedia support for a more concise and complete view of medical history

Prompt and appropriate treatment Fast response times through transparent networks and open interfaces

Risk reduction (access to a wider patientclient knowledge base)

Appropriate usable human-computer interfaces through awareness of contextual factors

Facilitation of cooperation between health professionals of different levels of health social care organization

Role-based access mechanisms and access privileges

Reduction in duplicate recordingquestioning of relevant patient information

A robust and scalable interface (HII) that could extend from corporatehospital to regional and national level

More focused and appropriate use of resources due to shared information of assessment and care plan

Access to all diagnostic information through adaptive user interfaces

Improved communication between professionals

Multimedia information is in the best format by clinical information system for communication without loss of quality

Security and guarantee of continuity of care Permanent access and control of interventions Identifi cation of a single patient across multiple systems

Mechanism for identifying a single client record and associated data that may have been stored on various source systems

Consistent shared language (between professionals)

Mapping tool to display information in a generic format to bridge the gap in terminology and semantic differences


141

Results of 2003 and 2004 National Ambulatory Medical Care Survey indicated that electronic health records were used in 18 of estimated 18 billion ambulatory visits in the USA for years 2003 and 2004 (Linder et al 2007 ) The researchers stated that despite the large number of patient records the sample size was small for some of the used quality indicators The study didnrsquot identify the implementation barriers for such low computerized registry use but outlined 17 ambulatory quality indicators and while some quality indicators showed signifi cance for quality of care the researchers didnrsquot fi nd consistent association between EHR and the quality of ambulatory care The main categories (Linder et al 2007 ) of researched indica-tors were the following

bull Medical management of common diseases (EHR had positive effect on aspirin use for coronary artery disease (CAD) but worse effect on antithrombotic ther-apy for atrial fi brillation (AF))

bull Recommended antibiotic use bull Preventive counseling bull Screening tests bull Avoiding potentially inappropriate prescribing in elderly patients

While it would be expected that EHR-extracted data would allow quality assess-ment and other impact assessment without expensive and time-consuming process-ing of medical documentation some researchers (Roth et al 2009 ) conclude that only about a third of indicators of the quality assessment tools system would be readily available through EHR with some concerns that only components of quality would be measured perhaps to the detriment of other important measures of healthcare quality The researchers provided a table of accessibility of quality indicators (clinical variables) which have been narrated in Table 69

A group of researchers looked into the problem of improving patient safety in ambulatory settings and throughout this qualitative study developed a tool kit of best practices and a collaborative to enhance medication-related practices and patient safety standards (Schauberger amp Larson 2006 ) The list of best practices for the inpatient setting was the following with 6 10 and 3 being the top three pro-cess improvements on best practices

1 Maintaining accurate and complete medication list 2 Ensuring medication allergy documentation 3 Standardizing prescription writing 4 Removing all IV potassium chloride from all locations 5 Emphasizing non-punitive error reporting 6 Educating about look-alike sound-alike drugs 7 Improving verbal orders 8 Ensuring safety and security of sample drugs 9 Following protocols for hazardous drug use 10 Partnering with patients 11 Notifying patients of laboratory results

Figures 62 63 and 64 summarize this chapter


142

Table 69 Accessibility of quality indicators

Accessible indicators (most to least) Hard-to-access indicators (most to least)

Demographics Disease-specifi c history Diagnosis Care site Prescription Physical exam Past medical history Refusal Procedure date Patient education Lab date Social history Problemchief complaint Treatment Vital signweightheight Diagnostic test result Allergy Imaging result Lab result Contraindication Medication history Pathology Diagnostic test date Family history Imaging date EKG result Medications current X-ray result Vaccination X-ray date EKG date

1

Research Gaps Research Goals Research Questions

The impact and significance of implementation barriers and enablers has not been satisfactorily studied

Significance of the relationship of factors of perceived usefulness perceived ease of use and perceived benefits on attitude toward using EHR in ambulatory settings has not been adequately shown with global studies

Lack of large-scale studies in the United States withTAM models application for small ambulatory setting

Lack of quantitative studies engaging SEM on a large scale for small clinics

Define a research framework for impact of EHR barriers and enablers on adoption of EHR system in small ambulatory settings

Assess the impact of barriers and enablers on framework components of EHR adoption in small ambulatory settings

What factors impact perceived ease of use perceived usefulness and perceived benefits in small clinics

Do interpersonal factors have any direct or indirect impacts

Do factors of perceived usefulness ease of use and benefits significantly impact EHR use in small ambulatory settings

Do subjective norms and attitudes impact intention to use EHR

Does perceived ease of use have a significant impact on perceived usefulness in small clinics

What is the impact significance of intention to use EHR into EHR use

Fig 62 Research gaps goals and questions for the adoption of EHR with focus on barriers and enables


Research Gaps Research Goals Research QuestionsA comprehensive decision-making model of EHR selection in small ambulatory settings has not been successfully introduced andor implemented

Combination of elements of human criteria (perceived usefulness and ease of use) financial technical organizational personal and interpersonal criteria in one decision-making model has not been performed

There is a lack of large-scale studies in the United States using HDM for EHR selection for small ambulatory setting

Define a research framework for EHRselection in small ambulatory settings

Assess the importance of criteria and subcriteria and the lower level of HDM through expert judgment quantification

Do criteria of perceived usefulness and ease of use play a significant role in EHR selection

Do interpersonal factors matter in selection of EHR software

Do financial factors impact the decision-making of EHR software in a significant way

Do organizational factors strongly influence decision-making in EHR selection process

Do personal factors of productivity and privacy play an important role in selection of EHR software

Fig 63 Research gaps goals and questions for the selection of EHR with focus on different alternatives

Research Gaps Research Goals Research QuestionsThe use of EHR in ambulatory settings andimpact on quality of healthcare has not been adequately studied

The magnitude of the impacts from EHR use in the small ambulatory setting has not been adequately studied

The effects of user satisfaction and quality impacts in ambulatory settings are not adequately analyzed with quantitative measures

Define a research framework relating EHR use in small ambulatory settings with comprehensive impacts hierarchy including quality criteria

Assess the impact of criteria and subcriteria of the model as a result of EHR use in ambulatory settings from physicianrsquos point of view

Which quality measures (system information or service) have higher importance from physicianrsquos point of view

Does EHR use greatly impacts organizational criteria of structure and environment

From physicianrsquos point of view does EHR use improve clinical outcomes andor save costs

There is a lack of large-scale studies in the United States using HDM for EHR impacts in small ambulatory setting

Fig 64 Research gaps goals and questions for the use of EHR with focus on impacts

144

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Ayatollahi H Bath P A amp Goodacre S (2009) Paper-based versus computer-based records in the emergency department Staff preferences expectations and concerns Health Informatics Journal 15 (3) 199ndash211 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed19713395

Bates D W et al (1998) Effect of computerized physician order entry and a team intervention on prevention of serious medication errors The Journal of the American Medical Association 280 (15) 1311ndash1316 httpwwwncbinlmnihgovpubmed9794308

Bates D W et al (2003) A proposal for electronic medical records in US primary care Journal of American Informatics Association 10 (1) 1ndash10

Becker A et al (2011) A new computer-based counselling system for the promotion of physical activity in patients with chronic diseasesmdashResults from a pilot study Patient Education and Counseling 83 (2) 195ndash202 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed20573467

Boonstra A amp Broekhuis M (2010) Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions BMC Health Services Research 10 231 httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2924334amptool=pmcentrezamprendertype=abstract

Burton-Jones A amp Hubona G S (2006) The mediation of external variables in the technology acceptance model Information and Management 43 (6) 706ndash717 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS0378720606000504

Carayon P et al (2011) ICU nursesrsquo acceptance of electronic health records Journal of the American Medical Informatics Association 18 (6) 812ndash819 Retrieved November 8 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=3197984amptool=pmcentrezamprendertype=abstract

Chen R-F amp Hsiao J-L (2012) An investigation on physiciansrsquo acceptance of hospital infor-mation systems A case study International Journal of Medical Informatics (60) 1ndash11 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed22652011

Cheng Y-M (2012) Exploring the roles of interaction and fl ow in explaining nursesrsquo e-learning acceptance Nurse Education Today Retrieved November 10 2012 from httpwwwncbinlmnihgovpubmed22405340

Chiasson M et al (2007) Expanding multi-disciplinary approaches to healthcare information technologies What does information systems offer medical informatics International Journal of Medical Informatics 76 Suppl 1 S89ndashS97 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed16769245


145

Chow M Chan L et al (2012) Exploring the intention to use a clinical imaging portal for enhancing healthcare education Nurse Education Today 1ndash8 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed22336478

Chow M Herold D K et al (2012) Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education Computers and Education 59 (4) 1136ndash1144 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS0360131512001327

Cresswell K amp Sheikh A (2012) Organizational issues in the implementation and adoption of health information technology innovations An interpretative review International Journal of Medical Informatics Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS1386505612001992

Davidson S amp Heineke J (2007) Toward an effective strategy for the diffusion and use of clini-cal information systems Journal of the American Medical Informatics Association 14 (3) 361ndash367 Retrieved November 12 2012 from http17167114118content143361abstract

Degoulet P Jean F C amp Safran C (1995) The health care professional multimedia worksta-tion Development and integration issues International Journal of Bio-medical Computing 39 (1) 119ndash125 httpwwwncbinlmnihgovpubmed7601524

Dexter P R et al (2004) Inpatient computer-based standing orders vs physician reminders to increase infl uenza and pneumococcal vaccination rates A randomized trial The Journal of the American Medical Association 292 (19) 2366ndash2371 httpwwwncbinlmnihgovpubmed15547164

Dillon A amp Morris M G (1996) User acceptance of new information technologymdashTheories and models In M Williams (Ed) Annual review of information science and technology (Vol 31 pp 3ndash32) Medford NJ Information Today

Duumlnnebeil S et al (2012) Determinants of physiciansrsquo technology acceptance for e-health in ambulatory care International Journal of Medical Informatics 81 (11) 746ndash760 Retrieved November 6 2012 from httpwwwncbinlmnihgovpubmed22397989

Folland S (2006) Health care in small areas of three command economies What do the data tell us Eastern European Economics 43 (6) 31ndash52 httpmesharpemetapresscomopenurlaspgenre=articleampid=doi102753EEE0012-8755430602

Girosi F Meili R amp Scoville R (2005) Extrapolating evidence of health information technol-ogy savings and costs pub no MG-410 Santa Monica CA

Goldzweig C L et al (2009) Costs and benefi ts of health information technology New trends from the literature Health Affairs (Project Hope) 28 (2) w282ndashw293 Retrieved March 29 2012 from httpwwwncbinlmnihgovpubmed19174390

Hagger M S et al (2007) Aspects of identity and their infl uence on intentional behavior Comparing effects for three health behaviors Personality and Individual Differences 42 (2) 355ndash367 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS0191886906002881

Handy J Hunter I amp Whiddett R (2001) User acceptance of inter-organizational electronic medical records Health Informatics Journal 7 (2) 103ndash107 Retrieved November 12 2012 httpjhisagepubcomcgidoi101177146045820100700208

Haron S N Hamida M Y amp Talib A (2012) Towards healthcare service quality An under-standing of the usability concept in healthcare design ProcediamdashSocial and Behavioral Sciences 42 (July 2010) 63ndash73 Retrieved November 12 2012 httplinkinghubelseviercomretrievepiiS187704281201049X

Hatton J D Schmidt T M amp Jelen J (2012) Adoption of electronic health care records Physician heuristics and hesitancy Procedia Technology 5 706ndash715 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS2212017312005099

Hung S-Y Ku Y-C amp Chien J-C (2012) Understanding physiciansrsquo acceptance of the Medline system for practicing evidence-based medicine A decomposed TPB model International Journal of Medical Informatics 81 (2) 130ndash142 Retrieved November 5 2012 from httpwwwncbinlmnihgovpubmed22047627


146

Im I Kim Y amp Han H-J (2008) The effects of perceived risk and technology type on usersrsquo acceptance of technologies Information and Management 45 (1) 1ndash9 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS0378720607000468

Janczewski L amp Shi F X (2002) Development of information security baselines for health-care information systems in New Zealand Computers and Security 21 (2) 172ndash192 Retrieved November 12 2012 from httpwwwsciencedirectcomsciencearticlepiiS0167404802002122

Jeng D J-F amp Tzeng G-H (2012) Social infl uence on the use of Clinical Decision Support Systems Revisiting the unifi ed theory of acceptance and use of technology by the fuzzy DEMATEL technique Computers and Industrial Engineering 62 (3) 819ndash828 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS0360835211003895

Jimoh L et al (2012) A model for the adoption of ICT by health workers in Africa International Journal of Medical Informatics 81 (11) 773ndash781 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed22986218

Jung S (2006) The perceived benefi ts of healthcare information technology adoption Construct and survey development Retrieved March 22 2013 from httpetdlsuedudocsavailableetd-11162006-125102

Karahanna E amp Straub D W (1999) The psychological origins of perceived usefulness and ease-of-use Information and Management 35 (4) 237ndash250 httplinkinghubelseviercomretrievepiiS0378720698000962

Kazley A S amp Ozcan Y A (2008) Do hospitals with electronic medical records (EMRs) pro-vide higher quality care An examination of three clinical conditions Medical Care Research and Review 65 (4) 496ndash513 Retrieved May 14 2012 from httpwwwncbinlmnihgovpubmed18276963

Kim S amp Malhotra N (2005) A longitudinal model of continued IS use An integrative view of four mechanisms underlying postadoption phenomena Management Science 51 (5) 741ndash755 Retrieved November 12 2012 from httpmanscijournalinformsorgcontent515741short

Lee G amp Xia W (2011) A longitudinal experimental study on the interaction effects of persua-sion quality user training and fi rst-hand use on user perceptions of new information technol-ogy Information and Management 48 (7) 288ndash295 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS0378720611000772

Leu M G et al (2008) Centers speak up The clinical context for health information technology in the ambulatory care setting Journal of General Internal Medicine 23 (4) 372ndash378 Retrieved March 1 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2359517amptool=pmcentrezamprendertype=abstract

Linder J A et al (2007) Electronic health record use and the quality of ambulatory care in the United States Archives of Internal Medicine 167 (13) 1400ndash1405 httpwwwncbinlmnihgovpubmed17620534

Lorenzi N M et al (2009) How to successfully select and implement electronic health records (EHR) in small ambulatory practice settings BMC Medical Informatics and Decision Making 9 (15) 1ndash13 Retrieved May 14 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2662829amptool=pmcentrezamprendertype=abstract

Ludwick D A amp Doucette J (2009) Adopting electronic medical records in primary care Lessons learned from health information systems implementation experience in seven coun-tries International Journal of Medical Informatics 78 (1) 22ndash31 Retrieved February 29 2012 from httpwwwncbinlmnihgovpubmed18644745

Maumlenpaumlauml T et al (2009) The outcomes of regional healthcare information systems in health care A review of the research literature International Journal of Medical Informatics 78 (11) 757ndash771 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed19656719

Malhotra Y (1999) Bringing the adopter back into the adoption process A personal construction framework of information technology adoption The Journal of High Technology Management Research 10 (1) 79ndash104 httplinkinghubelseviercomretrievepiiS1047831099800042


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Martich G amp Cervenak J (2007) Eyes wide shut The ldquohiddenrdquo costs of deploying health infor-mation technology Journal of Critical Care 7ndash8 Retrieved November 12 2012 from httpwwwjournalselsevierhealthcomperiodicalsyjcrcarticleS0883-9441(06)00217-6abstract

McFarland D J amp Hamilton D (2006) Adding contextual specifi city to the technology accep-tance model Computers in Human Behavior 22 (3) 427ndash447 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS074756320400130X

McGinn C A et al (2011) Comparison of user groupsrsquo perspectives of barriers and facilitators to implementing electronic health records A systematic review BMC Medicine 9 (46) 1ndash10 httpwwwpubmedcentralnihgovarticlerenderfcgiartid=3103434amptool=pmcentrezamprendertype=abstract

Melas C D et al (2011) Modeling the acceptance of clinical information systems among hospi-tal medical staff An extended TAM model Journal of Biomedical Informatics 44 (4) 553ndash564 Retrieved November 7 2012 from httpwwwncbinlmnihgovpubmed21292029

Melone N (1990) A theoretical assessment of the user-satisfaction construct in information sys-tems research Management Science 36 (1) 76ndash91 Retrieved November 12 2012 from httpmanscijournalinformsorgcontent36176short

Moores T T (2012) Towards an integrated model of IT acceptance in healthcare Decision Support Systems 53 (3) 507ndash516 Retrieved November 12 2012 from httplinkinghubelse-viercomretrievepiiS0167923612001108

Morton M E amp Wiedenbeck S (2009) A framework for predicting EHR adoption attitudes A physician survey Perspectives in Health Information ManagementAHIMA American Health Information Management Association 6 1 httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2804456amptool=pmcentrezamprendertype=abstract

Ortega Egea J M amp Romaacuten Gonzaacutelez M V (2011) Explaining physiciansrsquo acceptance of EHCR systems An extension of TAM with trust and risk factors Computers in Human Behavior 27 (1) 319ndash332 Retrieved November 7 2012 from httplinkinghubelseviercomretrievepiiS0747563210002530

Overhage J M (1996) Computer reminders to implement preventive care guidelines for hospital-ized patients Archives of Internal Medicine 156 (14) 1551

Pai F-Y amp Huang K-I (2011) Applying the Technology Acceptance Model to the introduction of healthcare information systems Technological Forecasting and Social Change 78 (4) 650ndash660 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS0040162510002714

Palacio C Harrison J P amp Garets D (2009) Benchmarking electronic medical records initia-tives in the US A conceptual model Journal of Medical Systems 34 (3) 273ndash279 Retrieved May 12 2012 from httpwwwspringerlinkcomindex101007s10916-008-9238-5

Pareacute G amp Sicotte C (2001) Information technology sophistication in health care An instrument validation study among Canadian hospitals International Journal of Medical Informatics 63 (3) 205ndash223 httpwwwncbinlmnihgovpubmed11502433

Piliouras Teresa (Raymond) Yu Pui Lam Huang Housheng Liu Xin Kumar Vijay Siddaramaiah Ajjampur Sultana Nadia Selection of electronic health records software Challenges considerations and recommendations Systems Applications and Technology Conference (LISAT) 2011 IEEE Long Island Issue Date 6ndash6 May 2011

Polančič G Heričko M amp Rozman I (2010) An empirical examination of application frame-works success based on technology acceptance model Journal of Systems and Software 83 (4) 574ndash584 Retrieved October 26 2012 from httplinkinghubelseviercomretrievepiiS0164121209002799

Premkumar G amp Bhattacherjee A (2008) Explaining information technology usage A test of competing models Omega 36 (1) 64ndash75 Retrieved November 5 2012 from httplinkinghubelseviercomretrievepiiS0305048305001702

Rosemann T et al (2010) Utilisation of information technologies in ambulatory care in Switzerland Swiss Medical Weekly 140 (September) w13088 Retrieved April 20 2012 from httpwwwncbinlmnihgovpubmed20853193


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Roth C P et al (2009) The challenge of measuring quality of care from the electronic health record American Journal of Medical Quality 24 (5) 385ndash394 Retrieved May 14 2012 from httpwwwncbinlmnihgovpubmed19482968

Schauberger C W amp Larson P (2006) Implementing patient safety practices in small ambula-tory care settings Journal on Quality and Patient Safety 32 (8) 419ndash425

Scott P J amp Briggs J S (2009) A pragmatist argument for mixed methodology in medical informatics Journal of Mixed Methods Research 3 (3) 223ndash241 Retrieved November 12 2012 from httpmmrsagepubcomcgidoi1011771558689809334209

Shin D-H (2010) The effects of trust security and privacy in social networking A security- based approach to understand the pattern of adoption Interacting with Computers 22 (5) 428ndash438 Retrieved November 4 2012 from httplinkinghubelseviercomretrievepiiS0953543810000494

Storey J amp Buchanan D (2008) Healthcare governance and organizational barriers to learning from mistakes Journal of Health Organisation and Management 22 (6) 642ndash651 Retrieved November 12 2012 from httpwwwemeraldinsightcom10110814777260810916605

Szajna B (1996) Empirical evaluation of the revised technology acceptance model Management Science 42 (1) 85ndash92 Retrieved November 12 2012 from httpmanscijournalinformsorgcontent42185short

Tsiknakis M Katehakis D G amp Orphanoudakis S C (2002) An open component-based information infrastructure for integrated health information networks International Journal of Medical Informatics 68 (1-3) 3ndash26 httpwwwncbinlmnihgovpubmed12467787

Valdes I et al (2004) Barriers to proliferation of electronic medical records Informatics in Primary Care 12 3ndash9 Retrieved May 15 2012 from httpwwwingentaconnectcomcon-tentrmpipc20040000001200000001art00002

Van Schaik P et al (2004) The acceptance of a computerised decision-support system in primary care A preliminary investigation Behaviour and Information Technology 23 (5) 321ndash326 Retrieved November 12 2012 from httpwwwtandfonlinecomdoiabs1010800144929041000669941

Vishwanath A Brodsky L amp Shaha S (2009) Physician adoption of personal digital assistants (PDA) Testing its determinants within a structural equation model Journal of Health Communication 14 (1) 77ndash95 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed19180373

Viswanathan S (2005) Competing across technology-differentiated channels The impact of net-work externalities and switching costs Management Science 51 (3) 483ndash496 Retrieved November 12 2012 from httpmanscijournalinformsorgcontent513483short

Were M C et al (2010) Evaluating a scalable model for implementing electronic health records in resource-limited settings Journal of the American Medical Informatics Association 17 (3) 237ndash244 Retrieved March 15 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2995711amptool=pmcentrezamprendertype=abstract

Wong D H (2003) Changes in intensive care unit nurse task activity after installation of a third- generation intensive care unit information system Critical Care Medicine 31 (10) 2488

Yang H (2004) Itrsquos all about attitude Revisiting the technology acceptance model Decision Support Systems 38 (1) 19ndash31 Retrieved November 9 2012 from httpportlandstateworld-catorgtitleits-all-about-attitude-revisiting-the-technology-acceptance-modeloclc198488645ampreferer=brief_results

Yu P Li H amp Gagnon M-P (2009) Health IT acceptance factors in long-term care facilities A cross-sectional survey International Journal of Medical Informatics 78 (4) 219ndash229 Retrieved November 7 2012 from httpwwwncbinlmnihgovpubmed18768345

Yusof M M et al (2008) An evaluation framework for Health Information Systems Human organization and technology-fi t factors (HOT-fi t) International Journal of Medical Informatics 77 (6) 386ndash398 Retrieved October 29 2012 from httpwwwncbinlmnihgovpubmed17964851


149

Rui Zhang and Ling Liu ldquoSecurity Models and Requirements for Healthcare Application Cloudsrdquo Proceedings of the 3rd IEEE International Conference on Cloud Computing (Cloud 2010) July5ndash10 2010 Miami Florida USA

Zheng K et al (2010) Social networks and physician adoption of electronic health records Insights from an empirical study Journal of the American Medical Informatics Association 17 (3) 328ndash336 Retrieved March 5 2012 from httpwwwpubmedcentralnihgovarticleren-derfcgiartid=2995721amptool=pmcentrezamprendertype=abstract



Chapter 7Decision Models Regarding Electronic Health Records


71 The Adoption of EHR with Focus on Barriers and Enables

Modifications to the models and extensions also have roots in theoretical back-ground and have proven to be effective in studying various cases of IT adoption under various conditions Knowledge of specific implementation barriers and their impact and statistical significance on the improvement of EHR use could lead to the creation of guidelines and incentives toward elimination of those barriers in ambula-tory settings Focused incentives training and education in the right direction could speed up the process of adoption and use of computerized registries as well as implementation of more sophisticated IT systems (Miller amp Sim 2004)

711 Theory of Reasoned Action

In their study of perceived behavioral control and goal-oriented behavior Ajzen and Fishbein proposed TRA (Ajzen amp Madden 1986) The fundamental point of TRA is that the immediate precedent of any behavior is the intention to perform behavior in question Stronger intention increases the likelihood of performance of the action according to the theory (Ajzen amp Madden 1986) Two conceptually independent determinants of intention are specified by TRA attitude toward the behavior (the degree to which an individual has favorable evaluation of behavior in mind or oth-erwise) and subjective norm (perceived social pressure whether the behavior should

L Hogaboam bull TU Daim () Department of Engineering and Technology Management Portland State University SW 4th Ave Suite LL-50-02 1900 97201 Portland OR USAe-mail liliyanascentiacom tugruludaimpdxedu

152

be performed or not ie acted upon or not) TRA also states that the behavior is a function of behavioral beliefs and normative beliefs which are relevant to behavior (Ajzen amp Madden 1986)

Atude toward the behavior

Subjec13ve norm

Inten13on Behavior

712 Technology Acceptance Model

In 1985 Fred Davis presented his work that was centered toward improving the understanding of user acceptance process for successful design and implementation of information systems and providing theoretical basis for a practical methodology of ldquouser acceptancerdquo through TAM which could enable implementers and system designers to evaluate proposed systems (Davis 1985) Perceived usefulness and perceived use are outlined to be the main two variables influencing attitude toward using the system Perceived usefulness is ldquothe degree to which individual believes that using a particular system would enhance his or her job performancerdquo Perceived ease of use is ldquothe degree to which an individual believes that using a particular system would be free of physical and mental effortrdquo Davis also shows that per-ceived ease of use has a causal effect on the variable of perceived usefulness (Davis 1985 Davis amp Venkatesh 1996)

Conceptual framework from Davis is shown in Fig 71His proposed model sheds light on the behavioral part of the concept with over-

all attitude of a potential user toward system use being a main determinant of the systemrsquos use On the other hand perceived usefulness and perceived use are out-lined to be the main two variables influencing attitude toward using the system Perceived usefulness is ldquothe degree to which individual believes that using a particu-lar system would enhance his or her job performancerdquo Perceived ease of use is ldquothe degree to which an individual believes that using a particular system would be free of physical and mental effortrdquo He argues that system that is easier to use will result in increased job performance and greater usefulness for the user all else being equal Davis also shows that perceived ease of use has a causal effect on the variable of


153

perceived usefulness (Davis 1985 Davis amp Venkatesh 1996) While ease of use is important with a lot of emphasis on user friendliness of the applications that increase usability no amount of ease of use could compensate for the reality of the useful-ness of the system (Davis 1993) Causal relationships in the model are represented by arrows (Fig 72) Attitude toward use is referred to as the degree of evaluative effect that an individual associates with using the target system in hisher job while actual system use is the individualrsquos direct usage of the given system (Davis 1985 Davis amp Venkatesh 1996)

Described mathematically TAM will look like this (Davis 1985)

Perceived easeof use EOU Xi n

i i( ) = +=aring1

b e

(71)

Perceived usefulness USEF iX EOUi n

i n( ) = + +=

+aring1

1

b b e

(72)

Attitude toward using ATT EOU USEF( ) = + +b b e1 2

(73)

Actual useof thesystem USE ATT( ) = +b e1

(74)

System Features and Capabili13es

Users Mo13va13on

to Use System

Actual System Use

S13mulus Organism Response

Fig 71 Conceptual framework for building TAM (Davis 1985)

x1

x2

Perceived Usefulness

Atude Toward Using

Actual System Use

Perceived Ease of Usex3

User Movaon

Design Features

Cognive Response

Affecve Response

Behavioral Response

Fig 72 Technology acceptance model (Davis 1985)

7 Decision Models Regarding Electronic Health Records

154

where

Xi is a design feature I i = 1hellipnβi is a standardized partial regression coefficientε is a random regression term

713 Theory of Planned Behavior

TPB extends TRA by including the concept of behavioral control The importance of control could be observed through the fact that the resources and opportunities available to individuals have to dictate to some extent the likelihood of behavioral achievement (Ajzen amp Madden 1986) According to the TPB a set of beliefs that deals with the presence or absence of requisite resources and opportunities could ultimately determine intention and action The more opportunities and resources individuals think they possess the fewer obstacles they anticipate and the greater their perceived control over behavior should be (Ajzen amp Madden 1986) (Fig 73)

Holden amp Karsh (2010) analyzed studies where TAM was used and compared the percentage of variance explained by this theoretical framework The percentage varies from 30 to 70 but in most cases tested in healthcare the percentage of variance is higher than 40 which means that the model explains at least 40 of phenomenon

The proposed framework for assessing EHR adoption in ambulatory settings has elements of TAM TRA and TPA along with important elements described in the literature that were frequently mentioned showed significant relationships or were expressed in qualitative and quantitative way This framework consists of barriers and enablers since some of those variables might have a positive influence on the system use The concepts of perceived ease of use and perceived usefulness and subjective norm have been explained earlier in this part of the exam The external factors have been constructed through the comprehensive literature review during the independent studies and the short and extended version of external element con-structs is shown in Fig 74

Extended taxonomy is listed in Table 71The summarized taxonomy barriers and enablers are displayed in Fig 75Mathematical description of the proposed model is presented below

Perceived easeof use EOU Xi

i i( )= +=

aring1 5

b e

(75)

Perceived usefulness USEF iX EOUi

i n( ) = + +=

+aring1 5

1

b b e

(76)


(77)


155

Atude toward the

behavior

Subjec13ve norm

Inten13on Behavior

Perceived

behavioral

control

Fig 73 Theory of planned behavior (Ajzen amp Madden 1986)

Perceived

usefulness

Perceived

ease of use

Atude toward

using EHR

Intention to

use EHR

system

EHR system use

Technical

factors

Financial

factors

Subjective

Norm

Interpersonal

Influence

Social

(organizatio

nal) factors

Personal

factors

Fig 74 Proposed framework for Study 1


156

Tabl

e 7

1 E

xten

ded

taxo

nom

y of

ext

erna

l fa

ctor

s

Fin

anci

al

bull S

tart

-up

cost

s(B

oons

tra

amp B

roek

huis

201

0 C

ress

wel

l amp

She

ikh

201

2 F

onky

ch amp

Tay

lor

200

5 M

cGin

n et

al

201

1

Men

ache

mi

amp B

rook

s 2

006

Pal

acio

et

al

2009

S

hoen

amp O

sbor

n 2

006

Sim

on e

t al

20

07

Val

des

et a

l 2

004

Zar

ouki

an

2006

)

bull O

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ng c

osts

Ash

amp B

ates

200

5 B

oons

tra

amp B

roek

huis

20

10

DeP

hill

ips

200

7 M

arti

ch amp

Cer

vena

k 2

007

Pol

ice

et a

l 2

011

W

itte

r 2

009)

bull F

inan

cial

unc

erta

inti

es (

lack

of

tang

ible

ben

efits

la

ck o

f fi

nanc

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retu

rn r

eim

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emen

t)

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men

thal

200

9 C

haud

hry

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l 2

009

Men

ache

mi

et a

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bull L

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of fi

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urce

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n so

me

sour

ces

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rred

to

as l

ack

of c

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al l

ack

of f

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ng e

tc)

(Ash

amp B

ates

200

5 B

oons

tra

amp B

roek

huis

201

0 B

owen

s et

al

201

0 F

onky

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Tay

lor

200

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al

200

8

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al

2009

P

alac

io e

t al

20

09

Rob

ert

Woo

d Jo

hnso

n F

ound

atio

n 2

010

Shi

elds

et

al

2007

S

imon

et

al

2008

S

hen

amp G

inn

201

2 S

imon

et

al

2007

)

Tec

hnic

al f

acto

rs

bull In

form

atio

n qu

alit

y (a

ccur

acy

co

nten

t f

orm

at t

imel

ines

s)(B

oden

heim

er amp

Gru

mba

ch 2

003

Che

n amp

Hsi

ao

2012

C

ress

wel

l amp

She

ikh

201

2 K

im amp

Cha

ng 2

006

Lia

ng e

t al

20

11

Mor

es 2

012

Wu

et a

l

2007

)

bull In

tens

ity

of I

T u

tili

zati

on

inte

nsit

y of

IT

uti

liza

tion

(da

ta

secu

rity

doc

umen

tati

on te

chni

cal

supp

ort

com

plex

ity

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20

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Bat

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20

03

Blu

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200

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tra

amp B

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20

10

Bow

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et a

l

2010

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et a

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Che

n amp

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ao 2

012

Duumln

nebe

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t al

20

12

Gla

ser

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l 2

008

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et a

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l

2009

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2001

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ence

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200

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200

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s 2

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Mil

ler

amp S

im 2

004

Ort

ega

Ege

a amp

Rom

aacuten G

onzaacute

lez

201

1

Pal

acio

et

al

2009

P

olic

e et

al

20

11

Rah

impo

ur e

t al

20

08

Rin

d amp

Saf

ran

199

3 R

osem

ann

et a

l 2

010

Rob

ert

Woo

d Jo

hnso

n F

ound

atio

n 2

010

Sim

on

et a

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007

Tsi

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t al

20

02

Tyl

er 2

001

Val

des

et a

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004

Ved

vik

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009

Yoo

n-F

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ery

et a

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008

Z

hang

amp L

iu 2

010)

Soc

ial

orga

niza

tion

al

bull T

op m

anag

emen

t su

ppor

t(A

ndreacute

et

al

2008

C

hen

amp H

siao

20

12

Kim

amp C

hang

200

6 L

egri

s et

al

200

3 M

orto

n amp

Wie

denb

eck

200

9 Y

usof

et

al

200

8)

bull P

roje

ctt

eam

com

pete

ncy

(Car

ayon

et

al

2011

C

hen

amp H

siao

20

12

Cho

w e

t al

20

12a

201

2b Y

arbr

ough

amp S

mit

h 2

007

Zar

ouki

an

2006

)

bull P

roce

ss o

rien

tati

on(C

hias

son

et a

l 2

007

Duumln

nebe

il e

t al

20

12)

bull S

tand

ardi

zati

on(B

oons

tra

amp B

roek

huis

201

0 C

ress

wel

l amp

She

ikh

201

2 G

lase

r et

al

200

8 G

reen

halg

h et

al

20

09

Hel

ms

amp

Wil

liam

s 2

011

Hol

den

amp K

arsh

201

0 K

azle

y amp

Ozc

an

2008

K

umar

amp A

ldri

ch 2

010

Lan

ham

et

al

2012

L

apin

sky

et a

l 2

008

Leu

et

al

2008

L

oren

zi e

t al

20

09

Lud

wic

k amp

Dou

cett

e 2

009

Mat

ysie

wic

z amp

Sm

ycze

k

2009

R

ande

ree

200

7 T

sikn

akis

et

al

2002

T

yler

200

1 W

agne

r amp

Wei

bel

200

5 Z

arou

kian

200

6)


157

bull S

taff

rea

lloc

atio

nem

ploy

men

t(G

reen

halg

h et

al

200

9 P

apat

heod

orou

199

0 J

ancz

ewsk

i amp

Shi

20

02)

bull S

ecur

ity

confi

dent

iali

typ

riva

cy

conc

erns

(Alp

er amp

Ols

on 2

010

Ang

st e

t al

20

10 A

sh amp

Bat

es

2005

B

oons

tra

amp B

roek

huis

20

10

Bow

ens

et a

l 2

010

D

uumlnne

beil

et

al

2012

M

orto

n amp

Wie

denb

eck

200

9 P

ilio

uras

et

al

2011

R

ind

amp S

afra

n 1

993

R

osem

ann

et a

l

2010

T

yler

200

1)

bull In

cent

ives

(Ash

amp B

ates

200

5 B

ates

et

al

2003

B

ecke

tt e

t al

20

11

Boo

nstr

a amp

Bro

ekhu

is

2010

C

ress

wel

l amp

She

ikh

201

2

For

d et

al

200

6 G

oldz

wei

g et

al

200

9 G

reen

halg

h et

al

200

9 K

umar

amp A

ldri

ch 2

010

Ros

eman

n et

al

20

10

Sch

oen

et a

l 2

006)

bull P

olic

y dr

awba

cks

and

supp

orts

(And

reacute e

t al

20

08

C

hen

amp H

siao

20

12

Chu

mbl

er e

t al

20

11

Gor

oll

et a

l 2

008

Mil

ler

amp S

im

2004

S

choe

n et

al

200

6 S

imon

et

al

2008

V

ishw

anat

h et

al

200

9 W

itte

r 2

009)

bull T

rans

ienc

e of

ven

dors

(Bat

es e

t al

20

03

For

d et

al

200

6 R

ande

ree

200

7)

bull W

orkfl

ow r

edes

ign

(Boo

nstr

a amp

Bro

ekhu

is 2

010

Bow

ens

et a

l

2010

C

haud

hry

et a

l 2

006

Dix

on e

t al

20

10

Fur

ukaw

a 2

011

G

orol

l et

al

200

8 L

oren

zi e

t al

20

09

Men

ache

mi

amp B

rook

s 2

006

Mil

ler

amp S

im 2

004

Zan

dieh

et

al

2008

Z

arou

kian

20

06)

Per

sona

l

bull A

ge s

peci

alty

pos

itio

n

fam

ilia

rity

(Ang

st e

t al

20

10

Ber

gman

-Eva

ns e

t al

20

08

Che

n amp

Hsi

ao

2012

E

gea

amp G

onza

lez

201

1 H

andy

et

al

2001

Je

ng amp

Tze

ng 2

012

Kim

amp H

an 2

008

Mil

ler

amp S

im 2

004

Mor

ton

amp W

iede

nbec

k 2

010

Pai

amp H

uang

20

11

Pol

ice

et a

l 2

011

Rah

impo

ur e

t al

20

08

Ros

eman

n et

al

201

0 V

ishw

anat

h et

al

200

9 W

u et

al

20

07)

bull M

otiv

atio

n(B

ecke

tt e

t al

20

11

Cre

ssw

ell

amp S

heik

h 2

012

Dix

on 1

999

Fra

mba

ch amp

Sch

ille

wae

rt 2

002

Gre

enha

lgh

et a

l

2009

P

ilio

uras

et

al

2011

W

u et

al

20

07 Y

arbr

ough

amp S

mit

h 2

007

Yu

et a

l 2

009)

bull P

rodu

ctiv

ity

(Bow

ens

et a

l 2

010

D

eLia

et

al

2004

M

orto

n amp

Wie

denb

eck

200

9 Y

oon-

Fla

nner

y et

al

200

8)

bull P

erso

nal

inno

vati

vene

ss(F

ram

bach

amp S

chil

lew

aert

200

2 H

ung

et a

l 2

012

Jen

g amp

Tze

ng

2012

M

oore

s 2

012

Vis

hwan

ath

et a

l 2

009

Y

i et

al

200

6)

bull S

elf-

effi

cacy

(Cha

u amp

Hu

200

2 C

hen

amp H

siao

20

12

Cho

w e

t al

20

12a

201

2b

Cre

ssw

ell

amp S

heik

h 2

012

Dix

on 1

999

Kuk

afka

et

al

200

3 L

egri

s et

al

200

3 M

cFar

land

amp H

amil

ton

200

6 R

ahim

pour

et

al

2008

W

u et

al

20

07

Wu

et a

l

2009

Yu

et a

l 2

009)

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nxie

ty(A

ggel

idis

amp C

hatz

oglo

u 2

009

Che

ng 2

012

Kuk

afka

et

al

2003

L

udw

ick

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ouch

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2

009

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amp B

ucha

nan

20

08

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et a

l 2

007

Yar

brou

gh amp

Sm

ith

200

7)

Inte

rper

sona

l(C

hang

201

2 C

hen

amp H

siao

20

12

Chi

asso

n et

al

200

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uumlnne

beil

et

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2012

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ram

bach

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chil

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20

02

L

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nd M

a 2

005

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et a

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g 2

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brou

gh amp

Sm

ith

200

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u an

d G

agno

n 2

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of e

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08)

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octo

r-do

ctor

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r-nu

rse

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octo

r-pa

tien

t


158

Intention to useEHRsystem INT ATT SN( ) = + +b b e1 2

(78)

Actual useof thesystem USE INT( ) = +b e1

(79)

where

Xi is an enablerbarrier factor I i = 1hellip5SN is subjective normβi is a standardized partial regression coefficientε is a random regression term

Based on the above-presented framework the following hypothesis will be tested

HA n External barriers and enablers impact PEoU and PU in small ambulatory clinics (n is the number of barriers and enablers that will be finalized through expert validation)

HB1-B2 Interpersonal implementation factors influence subjective norm and atti-tude toward EHR use in clinician practices

HC1-C2 PU and PEoU have significant impact on the attitude toward EHR use

Impactfactors

Financial Technical Socialorganizational Personal Interpersonal

doctor-doctor

doctor-nurse

doctor-patient

start-upcosts

ongoingcosts

financialuncertainties

lack offinancial

resources

information

quality

intensit of

IT utilization

data securty

documentation

technical support

complexity

customization

reliability

interconnectivity

interoperability

hardware issues

accuracy

content

format

timeliness

top managementsupport

projectteamcompetency

process orientation

standardization

staff reallocation

employment

securityconfidentialityprivancy concerns

incentives

policy drawbacks andsupports

transience of vendors

workflow redesign

age

specialty

position

familiarity

motivation

productivity

personalinnovative-

ness

self-efficacy

anxiety

Fig 75 Taxonomy of barriers and enablers


159

HD1-D2 Intention to use EHR system is impacted by subjective norms and attitude toward using EHR and PU

HE PEoU influences PU of EHR in small ambulatory settingsHF Positive intention to use EHR system translates into EHR use


When we are trying to select a product or technology based on a number of alterna-tives we engage in a decision-making process While we make our decisions every day some of them are more complex than the routine kind and require established managerial methodologies created for this purpose Hierarchical decision model (HDM) is used to decompose the problem into hierarchical levels and using pair-wise comparison scales and judgment quantification technique the researcher arrives at the calculated alternative However the process of decision analysis is even more of a value than the answer it brings since it forces systematic assessment of the alternatives (Henriksen 1997) Decision analysis provides information so that managers of technology in this case healthcare information technology spe-cifically EHR can make more informed decisions Some interesting examples of HDM in healthcare were described by Bohanec and others (Bohanec 2000) and were clinical in nature (assessment of breast cancer risk assessment of basic living activities in community nursing risk assessments in diabetic foot care etc) using DEX an expert system shell for multi-attribute decision support

Community-wide implementation of EHR was studied by Goroll et al where Massachusetts eHealth Collaborative (MAeHC) was formed in order to improve patient safety and quality of care through HIT use promotion (Goroll et al 2008) The working group outlined a set of system features that were involved in the selec-tion of vendors Those were (Goroll et al 2008)

bull User friendlinessbull Functionalitybull Clinical decision support capabilitybull Interoperabilitybull Securitybull Reliabilitybull Affordability

The authors also stress that despite the national push of EHR implementation positive encouragements in terms of vendor certification and system standards the current state of standards cannot ensure sufficient specific fit for a routine use by practices interoperability and ease of use therefore considerable technical as well as organizational efforts need to be engaged in the system (Goroll et al 2008)


160

Below are some figures depicting the bodies of knowledge surrounding organi-zational issues in HIT innovation (Fig 76) and theoretical approaches that concep-tualize interaction between technology humans and organizations (Cresswell amp Sheikh 2012) (Table 712)

Table 72 is the table of theoretical approaches that conceptualize interaction between technology humans and organizations (Cresswell amp Sheikh 2012)

Table 73 shows some information derived from Table 31 of 2009 Oregon Ambulatory EHR survey (Witter 2009)

The model is shown in Fig 77

721 Criteria

Seven criteria were chosen based on the extensive literature review Perceived use-fulness and perceived ease of use are based on the elements of the TAM Since the above-described research indicates that the acceptance of the technology is based on perceptions of users (physicians of small clinics with decision-making power in this

Organizaonal issues in HIT innovaon

Human factors

ergonomics

Organizational occupational

social psychology

Management amp organizational

change management

Information systems

Fig 76 Bodies of knowledge surrounding organizational issues in HIT innovation


161

Table 72 Theoretical approaches of interaction between technology humans and organizations

Name of the theory Explanations and definitions

Diffusion of innovations

Focuses on how innovations spread in and across organization over time

Normalization process Describes the incorporation of complex interventions in healthcare into the day-to-day work of healthcare staff

Sense making Assumes that organizations are not existing entities as such but produced by sense-making activities and vice versa they discover meaning of the status quo often by transforming situations into words and displaying a resulting action as a consequence

Social shaping theory Views technology as being shaped by social processes and highlights the importance of wider macro-environmental factors in influencing technology

Sociotechnical changing

Conceptualizes change as a nonlinear unpredictable and context- dependent process assuming that social and technical dimensions shape each other in a complex and evolving environment over time

Technology acceptance model

Assumes that individualrsquos adoption and usage of the system are shaped by the attitude toward use perceived ease of use and perceived usefulness

The notion of ldquofitrdquo Accentuates that social technological and work process factors should not be considered in isolation but in the appropriate alignment with each other

Table 73 Organizations and clinicians not planning to implement EHR in Oregon in 2009

Percent of organizations and clinicians with no plan to implement an EHREMR All entities

Clinicians all entities

Total organizations and clinicians 626 2313

Barriers

Security and privacy issues 181 112

Confusing number of EMR choices 03 01

Lack of expertise to lead or organize the project 195 166

No currently available EMR product satisfies our [needs] 182 208

Staff would require retraining 260 310

Expense of purchase 802 841

Expense of Implementation 586 684

Inadequate return on investment 361 298

Concern the product will fail 179 156

Staff is satisfied with paper-based records 348 259

Practice is too small 478 257

Plan to retire soon 173 77

Other 147 231

case) those criteria were included in the model It is assumed that EHR systems comply with ARRA mandates and have legal compliance

Those seven criteria and subcriteria will also be reviewed and justified by the experts in the field Experts will be chosen from academia in the field of healthcare and healthcare management and physicians


162

Fig 77 Hierarchical model of EHR software selection

7211 Perceived Usefulness

This criteria has its roots in TAM (Davis 1989) and identifies the userrsquos perception of the degree to which using a particular system will improve his or her perfor-mance The psychological origins of the concept are grounded in social presence theory social influence theory and Triandis modifications to the TRA (Karahanna amp Straub 1999) Perceived usefulness has been shown to have a great impact on technology acceptance in healthcare (Chen amp Hsiao 2012 Cheng 2012 Cresswell amp Sheikh 2012 Despont-Gros et al 2005 Kim amp Chang 2006 King amp He 2006 McGinn et al 2011 Melas et al 2011 Morton amp Wiedenbeck 2009 Yusof et al 2008) The concepts of TAM and relative research have been instrumental in explaining how beliefs about systems lead users to have positive attitudes toward systems intentions to use these systems and system use (Karahanna amp Straub 1999)

With the concepts of perceived usefulness the subcriteria that were selected from the literature review included the following

bull Data securityThe concept of data security has been brought up by many researchers as well as the government (Alper amp Olson 2010 Bowens Frye amp Jones 2010 Chen et al 2010 Duumlnnebeil et al 2012 Liu amp Ma 2005 Lorence amp Churchill 2005 Rind amp Safran 1993 Tsiknakis Katehakis amp Orphanoudakis 2002 Vedvik Tjora amp Faxvaag 2009 Yusof et al 2008 Zhang amp Liu 2010) The concept of


163

data security encryption and secure storage has been described in the literature review sections above Differences of in-cloud vs remote storage have been discussed as having various security features

bull InteroperabilityThe system should be able to function well with other applications in the net-work local and shared Alper and Olson (2012) note that interoperability is important to improve and coordinate care delivery While in the USA most patients receive care from several providers a lack of interoperability in the network would mean that physicians do not have access to a complete record for a patient and a ldquomaster recordrdquo might not exist or might not be complete at any point in time (Alper amp Olson 2012) Different systems will provide various levels of interoperability and the users may require more or less advanced sys-tems for their clinics A number of researchers stressed the importance of interop-erability of the EHR system as expressed by administrators physicians and other EHR users and the need to invest in improvements in it (Alper amp Olson 2012 Ash amp Bates 2005 Blumenthal 2009 Blumenthal 2010 Box et al 2010 Bufalino et al 2011 Cresswell amp Sheikh 2012 Degoulet Jean amp Safran 1995 DePhillips 2007 Dixon Zafar amp Overhage 2010 Duumlnnebeil et al 2012 Fonkych amp Taylor 2005 Furukawa 2011 Glaser et al 2012 Goldzweig et al 2009 Goroll et al 2008 Jian et al 2012 Jung 2006 Kazley amp Ozcan 2008 Lapinsky et al 2008 Maumlenpaumlauml et al 2009 McGinn et al 2011 Palacio Harrison amp Garets 2009 Tsiknakis et al 2002 Yao amp Kumar 2013 Yoon- Flannery et al 2008 Zaroukian 2006 Zhang amp Liu 2010)

bull CustomizationCustomization is an extremely important concept since various clinics with their unique specializations services provided and clientspatients of various needs have different needs in software customization as far as costs complexities and training required are concerned While some prefer a system that could be tai-lored in a unique way others may prefer a low-cost off-the-shelf product without elaborate customization capabilities (Alper amp Olson 2012) The issue of cus-tomization in EHR selection has been stressed by a number of researchers (Alper amp Olson 2012 Ash et al 2001 Cresswell amp Sheikh 2012 Degoulet et al 1995 Kim amp Chang 2006 Ludwick amp Doucette 2009 Menachemi amp Brooks 2006 Randeree 2007 Roth et al 2009 Witter 2009 Zandieh et al 2008)

bull ReliabilityReliability is a complex issue as well since a certain level of reliability of the system and the vendor must be present for the successful use of the EHR Thus Alper and Olson (2010) stated that the health information network that is able to be aggregated with a reasonable degree of accuracy and reliability would improve the ability to track known epidemics and identify new epidemics or other threats to public health such as bioterrorism or environmental exposures at an early stage Cresswell and Sheikh (2012) look at the lack of reliability of the system from the view of system stabilitymdashsoftware crashes etc Other researchers


164

include the concept of reliability when they study healthcare IT and EHR in par-ticular (Alper amp Olson 2010 Box et al 2010 Cresswell amp Sheikh 2012 Degoulet et al 1995 Despont-Gros et al 2005 Goroll et al 2008 Liu amp Ma 2005 Maumlenpaumlauml et al 2009 Moores 2012 Yusof et al 2008 Zaroukian 2006)

bull Product life cycleGenerally product life cycle of software (EHR as well) is short (Goroll et al 2008) therefore the physicians that are planning to acquire those systems should look into the fact of how fast they would need to upgrade and change the system when it will become obsolete and how long could it run and be supported after being installed It is closely tied with concepts of upgradability and system obso-lescence This concept is mentioned by a number of authors (Carayon et al 2011 David amp Jahnke 2005 DePhillips 2007 Goroll et al 2008 Hatton Schmidt amp Jelen 2012 Randeree 2007 Vedvik et al 2009 Witter 2009 Zaroukian 2006 Zhang amp Liu 2010)

7212 Perceived Ease of Use

Just like perceived usefulness the concept of ease of use has been known from Davisrsquos TAM (Davis 1989) and it is the userrsquos perception of the extent to which using a particular system would be free of effort A large body of research has shown that perceived ease of use significantly impacts technology acceptance and influences userrsquos decision-making process (Ayatollahi et al 2009 Carayon et al 2011 Chen amp Hsiao 2012 Cheng 2012 Chow Chan et al 2012a 2012b Chow Herold et al 2012b Cresswell amp Sheikh 2012 Davis amp Venkatesh 1996 Despont- Gros 2005 Dixon 1999 Duumlnnebeil et al 2012 Garcia-Smith amp Effken 2013 Jian et al 2012 Karahanna amp Straub 1999 Kim amp Chang 2006 King amp He 2006 Legris et al 2003 Liu amp Ma 2005 Melas et al 2011 Vishwanath et al 2009 Yusof et al 2008 and others)

The subcriteria for ldquoperceived ease of userdquo are the following

bull Ease of data extractionaccessThe EHR system could be packed with valuable data but if it is not easy for the user to access it (in a timely manner with not a significant amount of effort) the value of that system to the user diminishes greatly Easy access to information facilitates communication and decision making in healthcare (Kim amp Chang 2006) Certain decision support tools could be enabled in EHR software for improving physicianrsquos ease of access to data (Bodenheimer amp Grumbach 2003) The concept of accessibility and data extraction is studied in the context of health-care management IT acceptance and software or application selection (Ayatollahi et al 2009 Chumbler et al 2011 Duumlnnebeil et al 2012 Furukawa 2011 Garcia-Smith amp Effken 2013 Leu et al 2008 Maumlenpaumlauml et al 2009 Millstein amp Darling 2010 Rind amp Safran 1993 Roth 2009 Zhang amp Liu 2010)


165

bull Search abilitySystemrsquos user should be able to search the system in a timely effortless manner with acceptable and meaningful results Search capabilities could be one of the most important subcriteria as having a good-quality search engine with quick searching capabilities could greatly benefit a small practice however some phy-sicians may not feel like they need an elaborate searching system and may opt out for software with a modest acceptable searching capabilities Researchers have noted the feature of good data mining or data search (Alper amp Olson 2010 Ayatollahi et al 2009 Palacio et al 2009 Randeree 2007)

bull InterfaceConvenient interface that is easy to use and adjust to is possibly one of the most and first noticeable user-friendly features of the EHR system However the user might not require a fancy interface and may need an interface that fits the need of the clinic A user interface that is poorly designed with fragmented screens and multiple sign-ins can increase computer time and also lead to dissatisfaction (Furukawa 2011) Interface is a discussed topic in research and is often men-tioned in phrases as ldquointerface designrdquo or ldquointerface design qualityrdquo (Alper amp Olson 2010 Ayatollahi et al 2009 Becker et al 2011 Cresswell amp Sheikh 2012 Davis 1989 Degoulet et al 1995 Despont-Gros 2005 Ludwick amp Doucette 2009 Melas et al 2011 Moores 2012 Valdes et al 2004 Yusof et al 2008)

bull ArchivingArchiving and storing of the data is also an important concept since the quality of archiving can impact quality of retrieval of information Also the ease of archiving or the simplicity of it should benefit the physician the patient and the clinic overall The importance of archiving is captured in various research jour-nals and reports (Alper amp Olson 2010 Chen et al 2010 Goldberg 2012 Ludwick amp Doucette 2009 Maumlenpaumlauml et al 2009 Sanchez et al 2013 Vedvik et al 2009 Wu et al 2009 Zhang amp Liu 2010)

7213 Financial Criterion

A financial criterion is well mentioned in the literature as affordability of EHR by small clinics is a large issue Some researchers indicated that facilitating conditions like financial rewards have been main factors to positively affect behavioral inten-tion (Aggelidis amp Chatzoglou 2009) Shen and Ginn (2012) devoted their research to analyzing financial position and adoption of electronic health records through a retrospective longitudinal study Their conclusions stated that financial position indeed relates to EHR adoption in midterm and long-term planning (Shen amp Ginn 2012) Goldzweig et al (2009) have noted that the costs still remain the number one barrier cited by surveys assessing adoption and stressed the need for a better align-ment between ldquowho paysrdquo and ldquowho benefitsrdquo from health IT Miller and Sim (2004)


166

indicated that EMR use could be increased through implementation of financial rewards for quality improvement and for public reporting of quality performance measures

Through my independent studies besides the abovementioned articles I have found a large number of researchers studying importance of financial incentives identification of financial barriers and outlining financial attributes that are funda-mental for healthcare IT implementation (Andreacute et al 2008 Ash amp Bates 2005 Blumenthal 2009 Boonstra amp Broekhuis 2010 Cresswell amp Sheikh 2012 Dixon et al 2010 Fonkych amp Taylor 2005 Furukawa 2011 Goldberg 2012 Im et al 2008 Jung 2006 Leu et al 2008 Linder et al 2007 Martich amp Cervenak 2007 McGinn et al 2011 Ortega Egea amp Roman Gonzalez 2011 Randeree 2007 Simon et al 2007 Zandieh et al 2008)

bull Start-up costs (affordability)Major investment in EHR begins with costs required in order to acquire EHR system Small clinics could do it from their own savings investorsrsquo capital financial incentive or loans Researchers have stressed importance of this sub-criterion (Boonstra amp Broekhuis 2010 Cresswell amp Sheikh 2012 Fonkych amp Taylor 2005 McGinn et al 2011 Menachemi amp Brooks 2006 Palacio et al 2009 Shoen amp Osborn 2006 Simon et al 2007 Valdes 2004 Zaroukian 2006)

bull Ongoing and maintenance costsIn addition to initial costs required to obtain a system there are various costs associated with maintaining the system possibly updating it personnel costs associated with system upkeep etc Other researchers also note the importance of these costs (Ash amp Bates 2005 Boonstra amp Broekhuis 2010 DePhillips 2007 Martich amp Cervenak 2007 Police et al 2011 Witter 2009) and it would be interesting to assess physicianrsquos concerns about those costs as well as report about physicianrsquos awareness of those costs during the decision-making process

bull Ease of upgradeJust like with any software with an ongoing innovations and process changes in the industry and shorter life cycles of the products the upgrade may bring techni-cal and financial difficulties Those financial difficulties could be associated with a need to hire additional personnel to compensate for delays in patientrsquos care during the process of upgrade need to updatechangepurchase new computers install new additional programs etc Those costs could be 5ndash10 of providerrsquos current EHR costs (Alper amp Olson 2010) Randeree (2007) also discusses physi-ciansrsquo need to weigh in the costs of creating and supporting their IT structure as well as applications compared to using the external vendors for those services Those additional costs (upgrade coordination monitoring negotiating and governance) may delay the adoption since for small practices a typical EMR soft-ware costs approximately $10000 per physician not including the maintenance costs and costs for hardware and other software (Randeree 2007) Those issues are noted in other papers (Carayon et al 2011 David amp Jahnke 2005 DePhillips 2007 Dixon 1999 Goroll et al 2008 Janczewski amp Shi 2002 Kumar amp


167

Aldrich 2010 Martich amp Cervenak 2007 Menachemi amp Brooks 2005 2006 Piliouras et al 2011 Vedvik et al 2009 Witter 2009 Zaroukian 2006)

7214 Technical Criterion

With constant technological advances in the area of information technology and particularly EHR technical aspects are very important to consider but most impor-tant is to assess how well they will fit in within the organizational and social aspect whether those technical capabilities would be a good fit and whether they get a good use under the current circumstances While technical criteria is difficult to keep current because of ever-changing capabilities of the system and the types and brands of software coming out on the market we would ask the experts to closely examine the subcriteria and assess the additional technical aspects based on the selection of software Technical criterion is mentioned extensively in the literature (Angst et al 2010 Bates et al 2003 Blumenthal 2009 Bodenheimer amp Grumbach 2003 Boonstra amp Broekhuis 2010 Bowens et al 2010 Chen et al 2010 Chen amp Hsiao 2012 Cresswell amp Sheikh 2012 Duumlnnebeil et al 2012 Glaser et al 2008 Goroll et al 2008 Greenhalgh et al 2009 Handy et al 2001 Jian et al 2012 Kim amp Chang 2006 Liang et al 2011 Lorence amp Churchill 2005 Ludwick amp Doucette 2009 Menachemi amp Brooks 2006 Miller amp Sim 2004 Mores 2012 Ortega Egea amp Romaacuten Gonzaacutelez 2011 Palacio et al 2009 Police et al 2011 Rahimpour et al 2008 Rind amp Safran 1993 Robert Wood Johnson Foundation 2010 Rosemann et al 2010 Simon et al 2007 Tsiknakis et al 2002 Tyler 2001 Valdes et al 2004 Vedvik et al 2009 Wu et al 2007 Yoon-Flannery et al 2008 Zhang amp Liu 2010)

bull Supporting databasesThis is a subcriteria that has its links to interconnectivity of an EHR system since it may be important for many doctors to have access to certain clinical databases or other medical databases helpful in providing better healthcare since doctors may be able to provide more informed diagnoses may have access to new infor-mation about prescription drugs and their effects and newest clinical trials etc For example McCabe (2006) did some research into available databases for mental health in an effort to promote and study evidence-based practice which is a strategy to incorporate research results into the process of care They found that some sources like Cochrane Database of Systematic Reviews provide high- quality reviews of randomized controlled trials (RCTs) and other sources like the Database of Abstracts of Reviews and Effectiveness and the Agency for Health Care Research and Quality offer structured abstracts and clinical guide-lines for medical treatments (McGabe 2006)

There is some evidence that medication dispensation data obtained from claims databases improves the medication reconciliation and refill process in clinics (Leu et al 2008) Other supporting literature for database support was also found (Chen et al 2010 Degoulet et al 1995 Henrickren 1997 Hung Ku amp Chien 2012 Janczewski amp Shi 2002 Jung 2006 Lorenzi et al 2009


168

Pareacute amp Sicotte 2001 Police et al 2011 Randeree 2007 Vishwanath et al 2009 Zaroukian 2006 Zhang amp Liu 2010)

bull CompatibilityEnsuring compatibility of the EHR system with current work practices one of the key beliefs that influence adoptionmdashthe extent to which the system fits or is com-patible with the way the user likes it to work is a necessary component of IT acceptance (Moores 2012) The system must fit the needs of the user however some users may require higher degree of compatibility due to specialization of the practice certain procedures and particular processes in place while others may not perceive it as such a deciding factor in EHR selection Other researchers stressed the importance of the compatibility issue (Aggelidis amp Chatzoglou 2009 Alhateeb et al 2009 Chow et al 2012a 2012b Goroll et al 2008 Helfrich et al 2007 Holden amp Karsh 2010 Hung et al 2012 Kukafka et al 2003 Pynoo et al 2011 Randeree 2007 Shibl et al 2013 Staples et al 2002 Wu et al 2007 Yi et al 2006 Zaroukian 2006) Compatibility also is mentioned in diffu-sion theory as one of the five characteristics of innovation that affect their diffu-sion as innovationrsquos consistency with usersrsquo social practices and norms (Dillon amp Morris 1996) The other four are relative advantage (the extent to which technol-ogy offers improvements over tools that are currently available) complexity (innovationrsquos ease of use or learning) trialability (the opportunity of trying an innovation before committing to use it) and observability (the extent to which the outputs and gains of the new technology are clearly seen) (Dillon amp Morris 1996)

bull Clinical data exchangeClinical data exchange system gives the capability to move clinical information electronically across organization while maintaining the meaning of the informa-tion being exchanged (Li et al 1998) Communication standardization fund-ing and interoperability are some of the main barriers for the global clinical data exchange networks While selecting EHR the importance of clinical data exchange system to the users of the EHR system would be very interesting to assess Other researchers that studied the importance of clinical data exchange or included it as one of the important aspects of EHR use are the following Bowens et al (2006) Dixon et al (2010) Goroll et al (2008) Jian et al (2012) Maumlenpaumlauml et al (2009) Miller and Sim (2004) and Moores (2012)

7215 Organizational Criterion

In addition to the technical and financial aspects of EHR selections it is also impor-tant to consider organizational aspect that plays a crucial role in a decision-making process Box et al (2010) state that throughout health information technology imple-mentation success requires a careful balance of technical clinical and organiza-tional factors Cresswell and Sheikh (2012) dedicate an empirical and interpretative review study on organizational issues in HIT adoption and implementation


169

Organizational issues were described by the number of researchers Alper and Olson (2010) Ash and Bates (2005) Boonstra and Broekhuis (2010) Brand et al (2005) Burton-Jones and Hubona (2006) Chen et al (2010) Chumbler et al (2011) Davis (1989) Goldberg et al (2012) Johnson et al (2012) Kim and Chang (2006) Kukafka et al (2003) Lanham et al (2012) McGinn et al (2011) Moores (2012) Morton and Wiedenbeck (2009) Pynoo et al (2011) Weiner et al (2011) Yarbrough and Smith (2007) Yi et al (2006) and Zaroukian (2006)

bull StandardizationConforming to specific standards is an important issue and as various EHR sys-tems exist as well as various standards some systems might be more standardized than others From another perspective some standardization may be required in physicianrsquos practices for implementation of EHR McGinn et al (2012) talk about a lack of uniform standards at all levels (local regional national) which may contribute to physicianrsquos and managerrsquos disorientation when choosing an EHR system Hatton et al (2012) explain that even simple attempts at standard-ization (like ordering common blood chemistry tests) could be challenging for physicians which authors associate with physiciansrsquo challenges with EHR implementation Various perspectives of standardization issue have been men-tioned in the literature (Cresswell amp Sheikh 2012 Duumlnnebeil et al 2012 Kumar amp Aldrich 2010 Lanham et al 2012 Li et al 1998 Ludwick amp Doucette 2009)

bull TrainingWith any new system there will be some time for adjustment from an organiza-tional point of view and some training required Some systems may require more or less training and physicians need to be aware of those variables In addition to the possible financial impact the process of training will require it may also involve hiring more personnel or using vendorsrsquo training human resources The intensity timing and availability of training and support post-implementation affect user experience (Ludwick amp Doucette 2009) The issue of training is an important one to consider and has been mentioned by various researchers (Ayatollahi et al 2009 Chaudhry et al 2006 Kumar amp Aldrich 2010 Lee amp Xia 2011 Ludwick amp Doucette 2009 McGinn et al 2011 Moores 2012 Morton amp Wiedenbeck 2009 Noblin et al 2013 Pilouras et al 2011 Police et al 2011 Yeager et al 2010 Yi et al 2006 and others)

bull Tech SupportThe availability of tech support is important in EHR selection with some that may have straightforward personalized system or online-only system or the vendor might not provide tech support Depending on the IT infrastructure and the in-house capabilities physicians need to carefully examine this aspect to decide how important tech support is for them and how much tech support they will require Tech support or lack of thereof is an issue described by


170

researchers with bright examples in qualitative studies (Boonstra amp Broekhuis 2010 Goroll et al 2008 Holden amp Karsh 2010 Lustria et al 2011 Miller amp Sim 2004 Pynoo et al 2011 Valdes et al 2004 Wu et al 2007 Yu et al 2009)

7216 Personal Factors

There is some empirical research that expresses concern about EHR systems infring-ing on physiciansrsquo personal and professional privacy and acting as management control mechanisms (McGinn et al 2011) Boonstra and Broekhuis (2010) also discuss physicianrsquos personal issues about the questionable quality improvement associated with EHR and worry about a loss of professional autonomy Pilouras et al (2011) note that some practitioners use personal references and place high reliance on the experiences of other practices to help them make decision on which package to select

bull Privacy issuesPrivacy concerns have been some of the well-noted issues for physicians while choosing an EHR system

Issues of privacy are mentioned in numerous research articles (Angst et al 2010 Ash amp Bates 2005 Bates et al 2003 Blumenthal 2010 Bufalino et al 2011 Dephillips 2007 Glaser et al 2008 Goroll et al 2008 Handy et al 2001 Kazley amp Ozcan 2007 Lorenzi et al 2009 Lustria et al 2011 Morton amp Wiedenbeck 2010 Palacio et al 2009 Randeree 2007 Simon et al 2007 Tyler 2001 Yoon-Flannery et al 2008 Zheng et al 2012)

bull ProductivityPhysiciansrsquo concerns about losses in productivity and time have been discussed throughout my literature reviews and in this part Some users reported decrease in productivity right after the implementation of an EHR system (Cresswell amp Sheikh 2012) There are numerous research papers especially qualitative stud-ies that recorded interviews with physicians and other users of the system describing issues of productivity with selection and implementation of an EHR system (Andreacute et al 2008 Boonstra amp Broekhuis 2010 Bowens et al 2010 Chaudhry et al 2006 Davidson amp Heineke 2007 Ford et al 2006 Hatton et al 2012 Maumlenpaumlauml et al 2009 McGinn et al 2011 Morton amp Wiedenbeck 2009 Piliouras et al 2011 Police et al 2011 Storey amp Buchanan 2008 Yi et al 2006 Yoon-Flannery et al 2008) According to a survey of Medical Group Management Association Report more than four out of five users of paper records (783 ) believed that there would be a ldquosignificantrdquo to ldquovery signifi-cantrdquo loss of provider productivity during implementation and two-thirds (674 ) had concerns about the loss of physician productivity after the transi-tion period with EHR (MGMA 2011)


171

7217 Interpersonal Criterion

bull Sharing among doctors (doctor-doctor relationship)bull Interconnectivity between doctor and nurses (doctor-nurse relationship)bull Sharing with patients (doctor-patient relationship)

The importance of various relationships in peoplersquos lives and workplaces can impact decision-making processes Perceived impact of dynamics of the relation-ship whether itrsquos doctor-doctor doctor-nurse and doctor-patient should not be overlooked Interpersonal criterion has some elements of social organizational and personal dynamics (Cresswell amp Sheikh 2012) The importance of sharing and communication among various levels in the organization and outside (doctor- patient) and the ability of EHR software to provide that capability and perhaps improve the communication and important flow of information should be consid-ered during an EHR selection process Interpersonal issues have been discussed in the research literature (Beckett et al 2011 Chen amp Hsiao 2012 Cheng 2012 Chiasson et al 2007 Duumlnnebeil et al 2012 Frambach amp Schillewaert 2002 Liu amp Ma 2005 Wu et al 2007 Yang 2004 Yarbrough amp Smith 2007 Yu et al 2009 Yusof et al 2008) Kumar and Aldrich performed an SWOT analysis of a nationwide EMR system implementation in USA and in the section of ldquothreatsrdquo included statements that greater standardization could remove the ldquohuman touchrdquo between healthcare practitioners and patients and the doctor-patient relationship might turn into a new triad where EMR could be acting as a proxy for all who provide patient with care

The following hypotheses will be examined

HA1-A2 Perceived usefulness and ease of use have a high influence in the process of decision making for EHR selection

HB Interpersonal implementation factors greatly impact the EHR selection process

HC Financial factors significantly impact physicianrsquos decision-making process for EHR selection

HD Organizational factors significantly impact physicianrsquos decision-making pro-cess for EHR selection

HE1-E2 Productivity and privacy play an important role in EHR selection from physicianrsquos point of view

7218 Methodology

Multi-criteria decision tools like Saatyrsquos Analytic Hierarchy Process (AHP) (Saaty 1977) and HDM (Kocaoglu 1983) have some important steps in the application process

1 Structuring the decision problem into levels consisting of objectives and their associated criteria


172

2 Eliciting decision makerrsquos preferences through pairwise comparison among all variables at every hierarchical level of the decision model

3 Processing the input from the decision maker and calculating the priorities of the objectives

4 Checking consistency of the decision makerrsquos responses to ensure logical and not random comparison of the criteria

The last level of the hierarchy will be the software choices By the time the research is conducted the software selection might need to be evaluated again but currently according to the literature search performed for this exam the software choices are listed in Table 69

In HDM a variance-based approach is used for the inconsistency calculations and 10 limit is recommended on it in the constant sum method (CSM) While the HDM approach is similar to Saatyrsquos AHP the computational phase uses the CSM instead of the eigenvectors (Kocaoglu 1983) As explained by Dr Kocaoglu in the hierarchical decision process the problem is considered as a network of relation-ships among major levels (impact target and operational) of hierarchy with multi- criteria objectives at the top leading to multiple benefits and at the bottommdashmultiple outputs resulting from multiple actions (Kocaoglu 1983)

The CSM (Kocaoglu 1983) consists of the following

1 n(n minus 1)2 are randomized for the n elements under consideration 2 The decision makers distribute a total of 100 points between elements with

respect to each other (If they are of equal importance both elements get 50 points if one is four times highermore important with respect to another the allocation will be 80ndash20 points etc)

3 The data is written into Matrix A through comparing column elements with row elements

4 Matrix B is obtained by taking the ration of comparisons for each pair from Matrix A

5 Matrix C is constructed through division of each element in a column of Matrix B by the element in the next column

6 Element d is assigned a value of 1 and the calculation of other elements is per-formed by ratios as the mean of each column in Matrix C


In the study about impacts of EHR system use itrsquos important to consider impact factors found in the literature For example such effect factors were described by DesRoches et al in the New England Journal of Medicine (DesRoches et al 2008) with percentages of positive survey responses upon adoption of EHR Those were

bull Quality of clinical decisionsbull Quality of communication with other providers


173

bull Quality of communication with patientsbull Prescription refillsbull Timely access to medical recordsbull Avoiding medication errorsbull Delivery of preventive care that meets guidelinesbull Delivery of chronic illness care that meets guidelines

While the positive effect was shown in many cases the significance of p lt 0001 was reported only for the quality of clinical decisions delivery of preventive care that meets guidelines and delivery of chronic illness care that meets guidelines

Lanham at al who focused on social underpinning of EHR use or the ldquohuman elementrdquo of EHR acceptance implementation and use also noted about research in the area of EHR impacts particularly EHR influence of fundamental outcomes like cost and quality of healthcare delivery as well as reshaping organizational culture and clinical workflow (Lanham et al 2012)

Goroll et al (2008) also talked about the impact on safety and impact on quality Those types of EHR impacts may be hard to assess but are extremely important in growing the healthcare information management field and constantly improving it Chaudhry et al (2006) performed systematic review of the impact of HIT on qual-ity efficiency and cost The researchers outlined the components of an HIT imple-mentation (Chaudhry et al 2006)

bull Technological (for example system applications)bull Organizational process change (workflow redesign)bull Human factors (user friendliness)bull Project management (archiving project milestones)

Chaudhry et al (2006) also discussed what elements are behind the major effects of quality efficiency and cost

1 Effect on quality was predominantly in the role of increasing adherence (with decision support) to guideline- or protocol-based care In addition to the men-tioned variable clinical monitoring based on large-scale screening and aggrega-tion of data could show how health IT can support new ways of care delivery Reduction of medication errors was also reported measure of the effect on quality

2 Effects on efficiency

(a) Utilization of care (could be measured through the monetized estimates through the average cost of the examined service at the researched institu-tion could be analyzed through provided decision support (display of labo-ratory test costs computerized reminders display of previous test results automated calculation of pretest probability for diagnostic tests) at the point of care)

(b) Provider time (physician time could be examined in relation to computer use)


174

3 Effects on costs (changes in utilization of services cost data on aspects of system implementation or maintenance)

A summary table indicating key points of the systematic review on impacts of HIT from (Chaudhry et al 2006) is displayed in Table 74 above

While a lot of studies on barriers to adoption and impacts of EHR have been mentioned in this exam one particular study by Yusof et al (2008) examined previ-ous models of IS evaluation particularly the IS success model and the IT-organization fit model as well as introduced another HOT-fit model based on the system of human organization and technology-fit factors Before our EHR impacts model will be introduced letrsquos look at the theoretical history behind it

Updated DeLone and McLean IS success model was developed in 2003 based on the original DeLone and McLean IS success model introduced 20 years ago as a framework and model for measuring the complex-dependent variable in IS research (DeLone amp McLean 2003) The model is shown in Fig 78

As can be seen from the framework (Fig 78) the measures are included in the six system dimensions (Yusof et al 2008 DeLone amp McLean 2003)

bull System quality (the measures of the information processing system itself)bull Information quality (the measures of IS output)bull Service quality (the measures of technical support or service)bull Information use (recipient consumption of the output of IS)bull User satisfaction (recipient response to the use of the output of IS)bull Net benefits (IS impact overall)

While the model illustrates clear grounded well-observed and specific dimen-sions or impacts of IS successeffectiveness and their relationships it does not include organizational factors which have been included in HOT-fit model (Yusof et al 2008) Before depicting HOT-fit model there is another model that requires our attention in order to improve understanding of our research model

Table 74 Summary points of impact studies Chaudhry et al (2006)

Main summary points of impact studies

Health information technology has been shown to improve quality throughbull Increasing adherence to guidelinesbull Enhancing disease surveillancebull Decreasing medication errors

Primary and secondary preventive care holds much evidence on quality improvement

Decreased utilization of care is reported as the major efficiency benefit

Effect on time utilization is mixed

Empirically measured data on the aspects of costs is limited and inconclusive

Four benchmark research institutions supply most of the high-quality literature on multifunctional HIT systems

Effect of multifunctional commercially developed systems is not well documented

Interoperability and consumer HIT impacts have little evidence

Generalizability is a major limitation in the literature


175

IT-organizational fit model was presented in 1991 by Scott Morton and includes both internal and external elements of fit Modelrsquos internal fit is attained through combination and dynamic equilibrium of organizational components of business strategy organizational structure management processes and roles and skills while modelrsquos external fit is achieved due to formulation of organizational strategy grounded in environmental trends and market industry and technology changes (Yusof et al 2008) The enablermdashITmdashis shown to affect the management process also impacting organizational performance and strategy IT-organizational fit model (Yusof et al 2008) is shown in Fig 79

In 2008 Yusof et al combined elements of both models to create humanndashorga-nizationndashtechnology fit (HOT-fit) framework and proposed it for applications in healthcare while testing it with subjectivist case study strategy approach employ-ing qualitative methods (Yusof et al 2008) The researchers also presented exam-ples (Table 75) of the evaluation measures of the proposed network The HOT-fit proposed framework is shown in Fig 710

In our research model we are going to use hierarchical decision modeling in order to study impacts of EHR system as perceived by physicians of small ambula-tory clinics The criteria in the levels have been explained through the theoretical background and literature sources The methodology has been explained in detail

NETBENEFITS

USERSATISFACTION

INTENTIONTO USE

INFORMATIONQUALITY

SYSTEM QUALITY

SERVICEQUALITY

USE

Fig 78 Updated DeLone and McLean IS success model (DeLone amp McLean 2003)

Structure

Strategy

External EnvironmentRoles amp Skills

ManagementProcess

InformationTechnology

Fig 79 IT-organizational fit model by Scott Morton


176

during the use of HDM for the second study explained in this exam Just like in the previous model the components of the model are arranged in an ascending hierar-chical order At each level those criteria and subcriteria are compared with each other using a pairwise comparison scheme (also explained in the previous study) The questionnaire will be administered online through Qualtrics and the results will be put into PCM software for pairwise comparisons as well as Excel and pos-sibly SPSS to analyze some additional demographic and other information (age gender job position years of experience years of experience with EHR type and brand of EHR system implemented year of implementation number of implemen-tation (first system or replacement))

Table 75 Explanation of impact criteria through evaluation measures

Impact criteria Subcriteria Evaluation measures

Technology System quality Data accuracy data currency database contents ease of use ease of learning availability usefulness of system features and functions flexibility reliability technical support security efficiency resource utilization response time turnaround time

Information quality

Importance relevance usefulness legibility format accuracy conciseness completeness reliability timeliness data entry methods

Service quality Quick responsiveness assurance empathy follow-up service technical support

Human System use Amountduration (number of inquiries amount of connect time number of functions used number of records accessed frequency of access frequency of report requests number of reports generated) use by whom (direct vs chauffeured use) actual vs reported use nature of use (use for intended purpose appropriate use type of information used) purpose of use level of use (general vs specific) recurring use report acceptance percentage used voluntaries of use motivation to use attitude expectationsbelief knowledgeexpertise acceptance resistancereluctance training

User satisfaction

Satisfaction with specific functions overall satisfaction perceived usefulness enjoyment software satisfaction decision-making satisfaction

Organization Structure Nature (type size) culture planning strategy management clinical process autonomy communication leadership top management support medical sponsorship champion mediator teamwork

Environment Financial source government politics localization competition interorganizational relationship population served external communication

Net benefits Clinical practice (job effects task performance productivity work volume morale) efficiency effectiveness (goal achievement service) decision- making quality (analysis accuracy time confidence participation) error reduction communication clinical outcomes (patient care morbidity mortality) cost


177

TECHNOLOGY

HUMAN

ORGANIZATION

SystemQuality

InformationQuality

ServiceQuality

System Use

Net Benefits

Fit

Influence

User Satisfaction

Structure

Environment

Fig 710 The HOT-fit proposed framework (Yusof et al 2008)

Some open-ended questions will be asked in this questionnaire since they may provide important qualitative information and depending on the response rate will be used for further descriptive or other statistical analysis for example

bull How many clinical measures are reported by your systembull What clinical measures are reported by your system Please at least name the

main five you use or perceive useful if there are too many to reportbull What are the three major benefits to your practice from EHRbull What are the three main frustrations with your EHRbull Are you happy with your EHR system (5-point Likert scale) Why

(Fig 711)

Impacts of EHR system

Technological

Sys

tem

Qua

lity

Info

rmat

ion

Qua

lity

Ser

vice

Qua

lity

Human

Sys

tem

Use

Use

r S

atis

fact

ion

Organizational

Str

uctu

re

Env

ironm

ent

Net BenefitsC

linic

al

Fin

anci

ial

Fig 711 HDM of EHR impacts (Study 3)


178

The following hypotheses will be analyzed

HA1-A3 Quality measures (system quality information quality and service quality) have higher importance as EHR impact from physicianrsquos point of view

HB1-B2 EHR use greatly impacts organizational criteria of structure and environment

HC EHR use improves clinical outcomesHD EHR use saves costs

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Carayon P et al (2011) ICU nursesrsquo acceptance of electronic health records Journal of the American Medical Informatics Association 18(6)812ndash819 Retrieved November 8 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=3197984amptool=pmcentrezamprendertype=abstract

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Chaudhry B et al (2006) Systematic review Impact of health information technology on qual-ity efficiency and costs of medical care Annals of Internal Medicine 144(10) 742ndash752 Wndash168 ndashWndash185

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Chen Y-P et al (2010) An agile enterprise regulation architecture for health information security management Telemedicine Journal and E-Health 16(7)807ndash817 Retrieved April 24 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2956519amptool=pmcentrezamprendertype=abstract

Cheng Y-M 2012 Exploring the roles of interaction and flow in explaining nursesrsquo e-learning acceptance Nurse Education Today Retrieved November 10 2012 from httpwwwncbinlmnihgovpubmed22405340

Chiasson M et al (2007) Expanding multi-disciplinary approaches to healthcare information tech-nologies what does information systems offer medical informatics International Journal of Medical Informatics 76(Suppl 1)S89ndashS97 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed16769245


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Chow M Chan L et al 2012 Exploring the intention to use a clinical imaging portal for enhancing healthcare education Nurse Education Today 1ndash8 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed22336478

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Chumbler NR Haggstrom D Saleem JJ (2011) Implementation of health information technology in Veterans Health Administration to support transformational change telehealth and personal health records Medical Care 49(Suppl 12)S36ndashS42 httpwwwncbinlmnihgovpubmed 20421829


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Davis FD (1985) A technology acceptance model for empirically testing new end-user information systems Theory and results Massachusetts Institute of Technology Sloan School of Management ∎ httpenscientificcommonsorg7894517

Davis F (1989) User acceptance of computer technology a comparison of two theoretical models Management Science 35(8)982ndash1003 Retrieved November 12 2012 from httpmansci journalinformsorgcontent358982short

Davis F (1993) User acceptance of information technology system characteristics user percep-tions and behavioral impacts International Journal of Man-Machine Studies 38475ndash487 Retrieved November 12 2012 from httpdeepbluelibumicheduhandle20274230954

Davis FD Venkatesh V (1996) A critical assessment of potential measurement biases in the tech-nology acceptance model three experiments International Journal of Human-Computer Studies 45(1)19ndash45 httplinkinghubelseviercomretrievepiiS1071581996900403

Degoulet P Jean FC Safran C (1995) The health care professional multimedia workstation development and integration issues International Journal of Bio-Medical Computing 39(1)119ndash125 httpwwwncbinlmnihgovpubmed7601524

DeLia D et al (2004) What matters to low-income patients in ambulatory care facilities Medical Care Research and Review 61(3)352ndash375 Retrieved May 14 2012 from httpwwwncbinlmnihgovpubmed15358971

DePhillips H (2007) Initiatives and barriers to adopting health information technology A US per-spective Disease Management Health Outcomes 15(1)1ndash6 Retrieved May 10 2012 from httpwwwingentaconnectcomcontentadisdmho20070000001500000001art00001

DesRoches CM et al (2008) Electronic health records in ambulatory care mdash A national survey of physicians The New England Journal of Medicine 35950ndash60

Dillon A Morris MG (1996) User acceptance of new information technology - Theories and mod-els Annual Review of Information Science and Technology 313ndash32 Williams M (ed)

Dixon DR (1999) The behavioral side of information technology International Journal of Medical Informatics 56(1-3)117ndash123 httpwwwncbinlmnihgovpubmed10659940

Dixon BE Zafar A Overhage JM (2010) A Framework for evaluating the costs effort and value of nationwide health information exchange Journal of the American Medical Informatics Association 17(3)295ndash301 Retrieved March 14 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2995720amptool=pmcentrezamprendertype=abstract


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Dulcic Z Pavlic D Silic I (2012) Evaluating the intended use of Decision Support System (DSS) by applying Technology Acceptance Model (TAM) in business organizations in Croatia Procedia ndash Social and Behavioral Sciences 581565ndash1575 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS1877042812046058

Duumlnnebeil S et al (2012) Determinants of physiciansrsquo technology acceptance for e-health in ambu-latory care International Journal of Medical Informatics 81(11)746ndash760 Retrieved November 6 2012 from httpwwwncbinlmnihgovpubmed22397989

Fonkych K Taylor R (2005) The state and pattern of health information technology adoption Retrieved May 10 2012 from httpbooksgooglecombookshl=enamplr=ampid=qiALR-nsUrcCampoi=fndamppg=PP1ampdq=The+State+and+Pattern+of+Health+Information+Technology+Adoptionampots=Esaxti6UfVampsig=5XaJzkf0bVuTuwVPnZs5ybWZ8n4

Ford E Menachemi N Phillips T (2006) Predicting the adoption of electronic health records by physicians When will health care be paperless Journal of the American Medical Inform Assoc 13106ndash113 Retrieved May 14 2012 from httpjamiabmjjournalscomcon-tent131106short

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Glaser J et al (2008) Advancing personalized health care through health information technology An update from the American Health Information Communityrsquos Personalized Health Care Workgroup Journal of the American Medical Informatics Association 15(4)391ndash396

Goldberg DG (2012) Primary care in the United States the practice-based innovations and factors that influence adoption Journal of Health Organization and Management 26(1)81ndash97

Goldzweig C L et al(2009) Costs and benefits of health information technology new trends from the literature Health Affairs (Project Hope) 28(2) w282ndash93 Retrieved March 29 2012 from httpwwwncbinlmnihgovpubmed19174390

Goroll AH et al (2008) Community-wide implementation of health information technology the Massachusetts eHealth Collaborative experience Journal of the American Medical Informatics Association 16(1)132ndash139 Retrieved March 29 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2605598amptool=pmcentrezamprendertype=abstract

Greenhalgh T et al (2009) Tensions and paradoxes in electronic patient record research A system-atic literature review using the meta-narrative method The Milbank Quarterly 87(4)729ndash788 Retrieved May 14 2012 from httponlinelibrarywileycomdoi101111j1468-00092009 00578xfull

Handy J Hunter I Whiddett R (2001) User acceptance of inter-organizational electronic medical records Health Informatics Journal 7(2)103ndash107 Retrieved November 12 2012 from httpjhisagepubcomcgidoi101177146045820100700208

Hatton JD Schmidt TM Jelen J (2012) Adoption of electronic health care records physician heu-ristics and hesitancy Procedia Technology 5706ndash715 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS2212017312005099

Helfrich C D et al (2007) Adoption and implementation of mandated diabetes registries by community health centers American Journal of Preventive Medicine 33(1 Suppl) S50ndashS58 quiz S59ndash65 Retrieved May 14 2012 from httpwwwncbinlmnihgovpubmed17584591

Holden RJ Karsh B-T (2010) The technology acceptance model its past and its future in health care Journal of Biomedical Informatics 43(1)159ndash172 Retrieved October 26 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2814963amptool=pmcentrezamprendertype=abstract

Hung S-Y Ku Y-C Chien J-C (2012) Understanding physiciansrsquo acceptance of the Medline system for practicing evidence-based medicine a decomposed TPB model International Journal of Medical Informatics 81(2)130ndash142 Retrieved November 5 2012 from httpwwwncbinlmnihgovpubmed22047627


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Im I Kim Y Han H-J (2008) The effects of perceived risk and technology type on usersrsquo accep-tance of technologies Information amp Management 45(1)1ndash9 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS0378720607000468

Janczewski L Shi FX (2002) Development of information security baselines for healthcare infor-mation systems in New Zealand Computers amp Security 21(2)172ndash192 Retrieved November 12 2012 from httpwwwsciencedirectcomsciencearticlepiiS0167404802002122

Jeng DJ-F Tzeng G-H (2012) Social influence on the use of clinical decision support systems Revisiting the unified theory of acceptance and use of technology by the fuzzy DEMATEL technique Computers amp Industrial Engineering 62(3)819ndash828 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS0360835211003895

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Karahanna E Straub DW (1999) The psychological origins of perceived usefulness and ease-of- use Information amp Management 35(4)237ndash250 httplinkinghubelseviercomretrievepiiS0378720698000962

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King WR He J (2006) A meta-analysis of the technology acceptance model Information amp Management 43(6)740ndash755 Retrieved November 2 2012 from httplinkinghubelseviercomretrievepiiS0378720606000528

Kukafka R et al (2003) Grounding a new information technology implementation framework in behavioral science a systematic analysis of the literature on IT use Journal of Biomedical Informatics 36(3)218ndash227 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS1532046403000844

Kumar S Aldrich K (2010) Overcoming barriers to electronic medical record (EMR) implementa-tion in the US healthcare system A comparative study Health Informatics Journal 16(4)306ndash318 Retrieved March 12 2012 from httpwwwncbinlmnihgovpubmed21216809

Lanham HJ Leykum LK McDaniel RR (2012) Same organization same electronic health records (EHRs) system different use exploring the linkage between practice member communication patterns and EHR use patterns in an ambulatory care setting Journal of the American Medical Informatics Association 19382ndash391 Retrieved April 9 2012 from httpwwwncbinlmnihgovpubmed21846780

Lapinsky SE et al (2008) Survey of information technology in intensive care units in Ontario Canada BMC Medical Informatics and Decision Making 85 Retrieved March 16 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2233621amptool=pmcentrezamprendertype=abstract

Lee G Xia W (2011) A longitudinal experimental study on the interaction effects of persuasion quality user training and first-hand use on user perceptions of new information technology Information amp Management 48(7)288ndash295 Retrieved November 12 2012 from httplinkin-ghubelseviercomretrievepiiS0378720611000772


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Legris P Ingham J Collerette P (2003) Why do people use information technology A critical review of the technology acceptance model Information amp Management 40(3)191ndash204 httplinkinghubelseviercomretrievepiiS0378720601001434

Leu MG et al (2008) Centers speak up the clinical context for health information technology in the ambulatory care setting Journal of General Internal Medicine 23(4)372ndash378 Retrieved March 1 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2359517amptool=pmcentrezamprendertype=abstract

Liang H Xue Y Chase SK (2011) Online health information seeking by people with physical dis-abilities due to neurological conditions International Journal of Medical Informatics 80(11)745ndash753 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed21917511

Linder JA et al (2007) Electronic health record use and the quality of ambulatory care in the United States Archives of Internal Medicine 167(13)1400ndash1405 httpwwwncbinlmnihgovpubmed17620534

Lorence DP Churchill R (2005) Incremental adoption of information security in health-care orga-nizations Implications for document management IEEE Transactions on Information Technology in Biomedicine 9(2)169ndash173 httpwwwncbinlmnihgovpubmed16138533

Lorenzi NM et al (2009) How to successfully select and implement electronic health records (EHR) in small ambulatory practice settings BMC Medical Informatics and Decision Making 9(15)1ndash13 Retrieved May 14 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2662829amptool=pmcentrezamprendertype=abstract

Ludwick DA Doucette J (2009) Adopting electronic medical records in primary care lessons learned from health information systems implementation experience in seven countries International Journal of Medical Informatics 78(1)22ndash31 Retrieved February 29 2012 from httpwwwncbinlmnihgovpubmed18644745

Maumlenpaumlauml T et al (2009) The outcomes of regional healthcare information systems in health care a review of the research literature International Journal of Medical Informatics 78(11)757ndash771 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed19656719


McFarland DJ Hamilton D (2006) Adding contextual specificity to the technology acceptance model Computers in Human Behavior 22(3)427ndash447 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS074756320400130X

McGinn CA et al (2011) Comparison of user groupsrsquo perspectives of barriers and facilitators to implementing electronic health records A systematic review BMC Medicine 9(46)1ndash10 httpwwwpubmedcentralnihgovarticlerenderfcgiartid=3103434amptool=pmcentrezamprendertype=abstract

Melas CD et al (2011) Modeling the acceptance of clinical information systems among hospital medical staff an extended TAM model Journal of Biomedical Informatics 44(4)553ndash564 Retrieved November 7 2012 from httpwwwncbinlmnihgovpubmed21292029

Menachemi N Brooks RG (2006) Reviewing the benefits and costs of electronic health records and associated patient safety technologies Journal of Medical Systems 30(3)159ndash168 Retrieved March 27 2012 from httpwwwspringerlinkcomindex101007s10916-005- 7988-x

Menachemi N et al (2008) The relationship between local hospital IT capabilities and physician EMR adoption Journal of Medical Systems 33(5)329ndash335 Retrieved May 14 2012 from httpwwwspringerlinkcomindex101007s10916-008-9194-0

Miller RH Sim I (2004) Physiciansrsquo use of electronic medical records barriers and solutions Health Affairs (Project Hope) 23(2)116ndash126 httpwwwncbinlmnihgovpubmed22533131

Moores TT (2012) Towards an integrated model of IT acceptance in healthcare Decision Support Systems 53(3)507ndash516 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS0167923612001108


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Morton M E amp Wiedenbeck S (2009) A framework for predicting EHR adoption attitudes a physician survey Perspectives in health information management AHIMA American Health Information Management Association 6 p1a httpwwwpubmedcentralnihgovarticleren-derfcgiartid=2804456amptool=pmcentrezamprendertype=abstract

Morton M E amp Wiedenbeck S (2010) EHR acceptance factors in ambulatory care a survey of physician perceptions Perspectives in health information management AHIMA American Health Information Management Association 7 p1c httpwwwpubmedcentralnihgov articlerenderfcgiartid=2805555amptool=pmcentrezamprendertype=abstract

Ortega Egea JM Romaacuten Gonzaacutelez MV (2011) Explaining physiciansrsquo acceptance of EHCR sys-tems An extension of TAM with trust and risk factors Computers in Human Behavior 27(1)319ndash332 Retrieved November 7 2012 from httplinkinghubelseviercomretrievepiiS0747563210002530

Pai F-Y Huang K-I (2011) Applying the technology acceptance model to the introduction of healthcare information systems Technological Forecasting and Social Change 78(4) 650ndash660 Retrieved November 12 2012 from httplinkinghubelseviercomretrievepiiS0040162510002714

Palacio C Harrison JP Garets D (2009) Benchmarking electronic medical records initiatives in the US a conceptual model Journal of Medical Systems 34(3)273ndash279 Retrieved May 12 2012 from httpwwwspringerlinkcomindex101007s10916-008-9238-5

Pareacute G Sicotte C (2001) Information technology sophistication in health care an instrument vali-dation study among Canadian hospitals International Journal of Medical Informatics 63(3)205ndash223 httpwwwncbinlmnihgovpubmed11502433

Police RL Foster T Wong KS (2011) Adoption and use of health information technology in physi-cian practice organisations Systematic review Informatics in Primary Care 18245ndash259

Rahimpour M et al (2008) Patientsrsquo perceptions of a home telecare system International Journal of Medical Informatics 77(7)486ndash498 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed18023610

Randeree E (2007) Exploring physician adoption of EMRs A multi-case analysis Journal of Medical Systems 31(6)489ndash496 Retrieved April 23 2012 from httpwwwspringerlinkcomindex101007s10916-007-9089-5

Rind D M amp Safran C (1993) Real and imagined barriers to an electronic medical record Computer Application in Medical Care 74ndash78 Retrieved May 15 2012 from httpwwwncbinlmnihgovpmcarticlesPMC2248479

Rosemann T et al (2010) Utilisation of information technologies in ambulatory care in Switzerland Swiss Medical Weekly 140(September) pw 13088 Retrieved April 20 2012 from httpwwwncbinlmnihgovpubmed20853193

Roth CP et al (2009) The challenge of measuring quality of care from the electronic health record American Journal of Medical Quality 24(5)385ndash394 Retrieved May 14 2012 from httpwwwncbinlmnihgovpubmed19482968

Schoen C et al (2006) On the front lines of care primary care doctorsrsquo office systems experi-ences and views in seven countries Health Affairs (Project Hope) 25(6) w555ndashw571 Retrieved March 15 2012 from httpwwwncbinlmnihgovpubmed17102164

Shen JJ Ginn GO (2012) Financial position and adoption of electronic health records a retrospec-tive longitudinal study Journal of Health Care Finance 38(3)61ndash77 Retrieved May 15 2012 from httpwwwncbinlmnihgovpubmed22515045

Shields AE et al (2007) Adoption of health information technology in community health centers results of a national survey Health Affairs (Project Hope) 26(5)1373ndash1383 Retrieved March 26 2012 from httpwwwncbinlmnihgovpubmed17848448

Simon S et al (2007) Correlates of electronic health record adoption in office practices A statewide survey Journal of the American Medical Informatics Association 14(1)110ndash117 Retrieved May 15 2012 from httpwwwsciencedirectcomsciencearticlepiiS1067502706002143

Simon S et al (2008) Electronic health records Which practices have them and how are clinicians using them Journal of Evaluation in Clinical Practice 1443ndash47 Retrieved May 15 2012 from httponlinelibrarywileycomdoi101111j1365-2753200700787xfull


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Storey J Buchanan D (2008) Healthcare governance and organizational barriers to learning from mistakes Journal of Health Organisation and Management 22(6)642ndash651 Retrieved November 12 2012 from httpwwwemeraldinsightcom10110814777260810916605

Tsiknakis M Katehakis DG Orphanoudakis SC (2002) An open component-based information infrastructure for integrated health information networks International Journal of Medical Informatics 68(1-3)3ndash26 httpwwwncbinlmnihgovpubmed12467787

Valdes I et al (2004) Barriers to proliferation of electronic medical records Informatics in Primary Care 123ndash9 Retrieved May 15 2012 from httpwwwingentaconnectcomcontentrmpipc20040000001200000001art00002

Vedvik E Tjora AH Faxvaag A (2009) Beyond the EPR Complementary roles of the hospital- wide electronic health record and clinical departmental systems BMC Medical Informatics and Decision Making 929 Retrieved May 10 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2700794amptool=pmcentrezamprendertype=abstract

Vishwanath A Brodsky L Shaha S (2009) Physician adoption of personal digital assistants (PDA) Testing its determinants within a structural equation model Journal of Health Communication 14(1)77ndash95 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed19180373

Wagner H amp Weibel S (2005) The Dublin Core Metadata Registry Requirements implementa-tion and experience Journal of Digital Information 1ndash20 Retrieved May 15 2012 from httpdialnetuniriojaesservletarticulocodigo=1416626

Weiner BJ et al (2011) Use of qualitative methods in published health services and management research a 10-year review Medical Care Research and Review 68(1)3ndash33 Retrieved March 4 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=3102584amptool=pmcentrezamprendertype=abstract

Wu J-H Chen Y-C Greenes RA (2009) Healthcare technology management competency and its impacts on IT-healthcare partnerships development International Journal of Medical Informatics 78(2)71ndash82 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed18603470

Wu J-H Wang S-C Lin L-M (2007) Mobile computing acceptance factors in the healthcare indus-try a structural equation model International Journal of Medical Informatics 76(1)66ndash77 Retrieved November 12 2012 from httpwwwncbinlmnihgovpubmed16901749

Yang H (2004) Itrsquos all about attitude revisiting the technology acceptance model Decision Support Systems 38(1)19ndash31 Retrieved November 9 2012 from httpportlandstateworldcatorgtitleits-all-about-attitude-revisiting-the-technology-acceptance-modeloclc198488645amp referer=brief_results

Yarbrough AK Smith TB (2007) Technology acceptance among physicians A new take on TAM Medical Care Research and Review 64(6)650ndash672 Retrieved May 14 2012 from httpwwwncbinlmnihgovpubmed17717378

Yi MY et al (2006) Understanding information technology acceptance by individual professionals Toward an integrative view Information amp Management 43(3)350ndash363 Retrieved November 4 2012 from httplinkinghubelseviercomretrievepiiS0378720605000716

Yoon-Flannery K et al (2008) A qualitative analysis of an electronic health record (EHR) imple-mentation in an academic ambulatory setting Informatics in Primary Care 16277ndash285

Yu P Li H Gagnon M-P (2009) Health IT acceptance factors in long-term care facilities a cross- sectional survey International Journal of Medical Informatics 78(4)219ndash229 Retrieved November 7 2012 from httpwwwncbinlmnihgovpubmed18768345

Yusof MM et al (2008) An evaluation framework for Health Information Systems human organi-zation and technology-fit factors (HOT-fit) International Journal of Medical Informatics 77(6)386ndash398 Retrieved October 29 2012 from httpwwwncbinlmnihgovpubmed 17964851

Zandieh SO et al (2008) Challenges to EHR implementation in electronic- versus paper-based office practices Journal of General Internal Medicine 23(6)755ndash761 Retrieved April 15 2012 from httpwwwpubmedcentralnihgovarticlerenderfcgiartid=2517887amptool=pmcentrezamprendertype=abstract

Zaroukian MH (2006) Benefiting from ambulatory EHR implementation Solidarity six sigma and willingness to strive JHIM 20(1)53ndash60


Part III Adoption Factors of Electronic Health

Record Systems

Orhun M Koumlk Nuri Basoglu and Tugrul U Daim

Todayrsquos rapidly changing regulations increasing healthcare costs and most impor-tantly globalization have made health record keeping an important issue Electronic health record systems are rising as a crucial and unavoidable way of record keeping for healthcare However as other information technology implementations elec-tronic health records also have their own adoption processes and diffusion factors The main goal of this study is to defi ne a model to analyze adoption process of electronic health record systems and to understand the diffusion factors

Results of the study indicate that there are different factors affecting the adop-tion process via a literature research and quantitative fi eld survey Model has been tested and constructs have been grouped under intermediary dependent and exter-nal factors


Chapter 8 Adoption Factors of Electronic Health Record Systems

Orhun Mustafa Koumlk Nuri Basoglu and Tugrul U Daim

Todayrsquos rapidly changing regulations increasing healthcare costs and most importantly globalization have made health record keeping an important issue Electronic health record systems are rising as a crucial and unavoidable way of record keeping for healthcare However as other information technology imple-mentations electronic health records also have their own adoption processes and diffusion factors The main goal of this study is to defi ne a model to analyze the adoption process of electronic health record systems and to understand the diffusion factors

Results of the study indicate that there are different factors affecting the adoption process via a literature research and quantitative fi eld survey Models have been tested and constructs have been grouped under intermediary dependent and exter-nal factors

81 Introduction

In Turkey 368 of the people over the age of 15 have health problems affecting their daily activities (Turkstat Health Statistics 2012a 2012b ) Seventy-six percent of the healthcare expenditure in Turkey is conducted via government in 2011

O M Koumlk PwC Strategyamp Ernst and Young Advisory Istanbul Turkey

N Basoglu İzmir Institute of Technology Urla Turkey


190

(Euromonitor 2012 ) In 2020 it is expected that 20 of the Turkish population will be older than 50 years (Euromonitor 2012) The Ministry of Health has started a transformation program in 2003 and offering e-health services is an important part of the program The ministry has created database and data collection standards for all types of healthcare organizations (Ministry of Health Statistics 2012 ) In 2010 there are 16651 patient care institutions and ~123000 physicians in Turkey (Turkstat 2010 ) This proves that effi cient integration and information sharing are required between these institutions and physicians In order to establish this pur-pose the government is planning to integrate all healthcare organizations within a network and in the later steps telehealth and telemedicine applications will go live in the future (Ministry of Health Statistics 2012 )

Healthcare systems are facing with increasing demand rising costs inconsis-tency and lowering interoperability (Lluch 2011 ) As the increasing demand gets combined with the lowering funds of the governments healthcare providers started to look for less costly alternatives (Al-Qirim 2007 )

In our era with the innovations in the telecommunications and information tech-nologies the use of electronic services has increased in many areas Health is one of these areas affected by technologies In the last decades health information systems (HIS) have developed many new technologies Telemedicine telehealth and elec-tronic health records can be counted as the main areas in this industry (Haux 2010 ) Behkami and Daim stated that electronic health records and their adoption are an important research area for technology adoption and medical information research-ers ( 2012 ) Technology is used in many areas in health services Medical informat-ics is a discipline which focuses on data storing processing and information and knowledge management related to healthcare (Haux 2010 )

Health information systems are used by many different types of users such as patients doctors administration employees and application developers So they all have diffi culties in both using and developing these systems This research will focus on the factors that affect users using the electronic health record (EHR) from the technological and organizational perspective

As the healthcare processes are getting more complicated the public expects to move from hard copy of records to electronic-based record keeping (Tavakoli Jahanbakhsh Mokhtari amp Tadayon 2011 ) On the other hand many healthcare IT projects are failing or being abandoned due to lack of understanding of the health-care adoption factors (Kijsanayotin Pannaruthonai amp Speedie 2009 )

Healthcare providers and payers need more collaboration and communication than they ever did (Al-Qirim 2007 ) Electronic health records are an important layer to establish this communication Healthcare providers who try to implement health information systems face with challenging problems in technical social and organizational areas (Ovretveit Scott Rundall Shortell amp Brommels 2007 )

This study has been conducted to bring an understanding to the adoption factors of EHR systems To reach this goal diffusion of information systems diffusion of

OM Koumlk et al

191

health information systems and diffusion of electronic health records have been analyzed This study has researched and sought answers for the following topics

bull The technology diffusion process and factors affecting the technology adoption bull Health information system implementation and main barriers affecting the

implementation process bull Electronic health record evolution and main benefi ts of electronic health record

usage bull Electronic health record diffusion models and factors affecting the electronic

health record adoption process

82 Literature Review

821 Electronic Health Records

The International Organization for Standardization defi nes the electronic health record as a digital information format which contains the health progress of a patient (ISO 2005 ) The electronic health record is also implied as a computerized patient record (CPR) computer-based patient record computerized medical record elec-tronic medical record (EMR) electronic patient record (EPR) electronic healthcare record (EHCR) virtual EHR and digital medical record (DMR) which all have been determined during the last 30 years (Wen Ho Wen-Shan Li amp Hsu 2007 )

Developments in technology and health information systems would result to increase in the quality of healthcare (Tange Hasman Robbe amp Schouten 1997 ) However the developments in technology and telecommunications have not really improved the EHR systems (Brender Nohr amp McNair 2000 )

EHR systems are used by different types of users such as healthcare professionals and upper management Moreover healthcare professionals including physicians nurses radiologists pharmacists laboratory technicians and radiographers use differ-ent modules of EHR systems (Hayrinen et al 2008 ) Early adopters of EHR systems have already started to develop and expand their systems (Collins amp Wagner 2005 )

Transition from old paper-based records to new electronic record systems is a hard and long process which needs to satisfy several stakeholders (Estebaranz amp Castellano 2009 )

As demand of health system stakeholders increases too much healthcare providers cannot serve them until new developments have been taken in (Ludwick amp Doucette 2009 ) EHR 2003 systems are preferred over the paper-based records in the meaning of being portable more accurate and easier to report and also because in some cases they can be used as input for decision support systems (Holbrook et al 2003 )

An electronic healthcare record should include information about patientrsquos con-ditions and situation for doctors administrative data for administrative services and data required for the management of the healthcare organization (Estebaranz amp Castellano 2009 ) Moreover electronic health record systems can be used as a great

8 Adoption Factors of Electronic Health Record Systems

192

input for decision support systems with their long-term storage functionality reliable data structure and exceptional sharing capabilities (Hannan 1999 ) Usage of EHR may lead to reducing costs enhancing higher quality of care increased reli-ability and access to more accurate results (Kierkegaard 2011 ) Changing policies healthcare payers and governments require more accurate standardized and detailed data in order to clearly understand the situation to develop statistics and to segment their customers (Gonzalez-Heydrich et al 2000 ) Electronic health records can play an important role to fulfi ll these requirements (Gonzalez-Heydrich et al 2000 ) Although there are many policies regulating the electronic health record and healthcare information systems they are not totally practiced (Ovretveit et al 2007 ) All countries are changing their system from paper-based records to elec-tronic health records however only some of them could succeed in this operation (Jahanbakhsh Tavakoli amp Mokhtari 2011 ) Health information technologies and electronic health records are rising as a method to increase quality of care produc-tivity and security (Jha Doolan Grandt Scott amp Bates 2008 ) Also EHR offers an easy process for disease management processes with its functionalities and easy sharing (Wright et al 2009 )

822 Technology Adoption Models

Some models have been defi ned to understand the behaviors of people in the adop-tion process The theory of reasoned actions (Fishbein amp Ajzen 1975 ) Technology Acceptance Model (Davis 1989 ) Technology Acceptance Model 2 (Venkatesh amp Davis 2000 ) and unifi ed theory of acceptance and use of technology (Venkatesh Morris Davis amp Davis 2003 ) can be taken as the most signifi cant ones Also most of the researchers are taking these models as base asset and then specify their researches on these

The theory of reasoned action which can be seen in Fig 81 takes subjective norm and attitude toward act as its main constructs Subjective norm refers to ldquothe personrsquos beliefs that specifi c individuals or groups think heshe should or should not perform the behavior and hisher motivation to comply with the specifi c referentsrdquo (Fishbein amp Ajzen 1975 ) on the other hand attitude refers to ldquothe personrsquos beliefs that the behavior leads to certain outcomes and hisher evaluations of these out-comesrdquo (Fishbein amp Ajzen 1975 )

Attitude Toward Act

Subjective Norm

Behaviroal Intention Behavior

Fig 81 Theory of reasoned actions (Fishbein amp Ajzen 1975 )

OM Koumlk et al

193

Davis came up with the idea of the Technology Acceptance Model ( 1989 ) Perceived usefulness and perceived ease of use are taken as the two main drivers In fi nal behavioral intention brings the actual use result (Davis 1989 ) This modelrsquos main purpose is to predict user adoption behavior toward the technological develop-ments Figure 82 explains how the Technology Acceptance Model (TAM) is struc-tured (Davis 1989 ) TAM can be considered a future step for the theory of reasoned actions (Fishbein amp Ajzen 1975 ) and theory of planned behavior (Ajzen 1991 )

Venkatesh and Davis have made some additions to the Technology Acceptance Model and developed a further model with new factors in 2000 Factors such as experience and voluntariness affect the perceived usefulness Also the perceived ease of use has determinants such as subjective norm image job relevance output quality and demonstrability (Venkatesh amp Davis 2000 ) In Fig 83 TAM2 is explained (Venkatesh amp Davis 2000 )

Perceived Ease of Use

Attitude BehavioralIntention


Fig 82 Technology Acceptance Model (Davis 1989 )

Image

Job Relevance

Output Quality

Subjective Norm

Result Demonstability

Experience Voluntariness



Attitude Behavioral Intention

Fig 83 Technology Acceptance Model 2 (Venkatesh amp Davis 2000 )


194

The unifi ed theory of acceptance and use of technology (UTAUT) has been defi ned by Venkatesh et al as a combination of different adoption theories such as the Technology Acceptance Model theory of reasoned actions and theory of planned behavior ( 2003 )

UTAUT (Fig 84 ) has three direct determinants on behavioral intention to use such as expectations from performance expectations from effort and the infl uence of the social environment (Venkatesh et al 2003 ) Intention to use and facilitating conditions affect the use behavior (Venkatesh et al 2003 )

DeLone and McLean have proposed a model for information systems success which correlates system quality and information quality with the actual system use and user satisfaction (1992) Furthermore it is stated that these categories are mul-tidimensional and also affect both individual and organizational impact (DeLone amp McLean 1992 ) (Fig 85 )

In 2003 the information systems success model has been updated and new vari-ables have been added intention to use net benefi ts and service quality (DeLone amp McLean) (Fig 86 )

Performance Expectancy

Effort Expectancy

Social Influence

Facilitating Conditions

Behavioral Intention Use Behavior

Gender Age Experience Voluntariness

Fig 84 UTAUT (Venkatesh et al 2003 )

System Quality

Information Quality

Use

User Satisfaction

Individual Impact

Organizational Impact

Fig 85 Information systems success model (DeLone amp McLean 1992 )

OM Koumlk et al

195

823 Health Information System Adoption

Researchers have developed adoption models specifi cally for health information systems

Yu and Gagnon have extended TAM2 and proposed taxonomy for health IT acceptance factors They have added subjective norm image and computer level as antecedent factors of ease of use Job role and subjective norm are defi ned as sub- factors of usefulness It is expressed that image has a negative effect on behavioral intention (Kargin et al 2009 ) (Fig 87 )

A further step has been taken on UTAUT and it is updated for hospital technol-ogy acceptance It is stated that anxiety has a negative effect on self-effi cacy (Aggelidis amp Chatzoglou 2009 ) Also self-effi cacy has positive effects on perceived ease of use and behavioral intention (Aggelidis amp Chatzoglou 2009 )

Electronic health records have different adoption factors than the other technolo-gies because their focus is mostly on the physicians and hospital administrations unlike the other technologies which mostly focus on citizen workers or students (Gagnon et al 2003 )

In order to increase the adoption effectiveness EHR systems have to be designed to be applicable with the workfl ows of the healthcare employees otherwise practi-cal application of the EHR system would take longer than expected (Hyun Johnson Stetson amp Bakken 2009 )

Another model combines the technology adoption model with new variables for health information adoption factors including computer self-effi cacy and perceived fi nancial cost variables (Tung amp Chang 2008 )

Health information-seeking behavior is related with EHR system usage Availability creditability and comprehensiveness are important factors in health information-seeking behavior (Basoglu et al 2010 ) Improved quality of care is an important adoption factor for EHR systems however privacy concern cost and implementation diffi culties are the main barriers (Greenshup 2012 )

International HL7 standards are defi ned in order to establish communication between healthcare organizations in terms of effi ciency with improved quality of care (Dosswell et al 2010 )

Information Quality

System Quality

Service Quality

Intention to Use

User Satisfaction

Net Benefits

Use

Fig 86 Updated information systems success model (DeLone amp McLean 2003)


196

The dynamically changing healthcare industry requires software which can adapt to new changes and a platform that works effi ciently at a low cost (Daim Basoglu amp Tan 2010 )

Unlike the old times present-day healthcare organizations need to combine tech-nology with information in order to meet the organizationrsquos IT requirements (Blue amp Tan 2010 )

Topacan stated that compatibility quality of support and information quality have a positive impact on usefulness (2011) On the other hand self-effi cacy has a positive effect on the ease of use (Topacan 2009 ) Figure 88 implies Topacanrsquos detailed model

Accesibility

Service Quality

Quality of Sup

Information Qua

Usage Time

Compatibility

Social Influence

Understandibility

Image

Cost

Ease of Use

Usefulness

Attitude

Intention

Self- Efficacy

Fig 88 Topacanrsquos e-health services framework (2009)

Image

Subjective Norm

Job Role

Computer Level

Usefulness

Ease of Use

Behavioral Intention

Fig 87 Health IT acceptance factors (Yu Li amp Gagnon 2009 )

OM Koumlk et al

197

Challenges during the implementation of EHR systems would be divided into two categories structural and infrastructural (Jahanbakhsh et al 2011 ) Infrastructural challenges can be summarized as IT-based problems communi-cation problems between stakeholders cultural problems and lack of require-ment analysis (Jahanbakhsh et al 2011 )

Usage of electronic records brings functionalities such as directly getting the required information through filtering and search capabilities (Wang Chase Markatou Hripcsak amp Friedman 2010 ) Selected information posi-tively affects quality of care and increases the performance of diagnosis (Wang et al 2010 )

Usability of the EHR software depends on many variables Rose et al defi ned the relationship with the usability of EHR systems with the user interface fl exibility and workfl ow of the implemented system ( 2005 ) Also Edwards et al said that fl ex-ibility and workfl ow are the main elements of the usability ( 2008 ) However it is implied that there is a trade-off between the fl exibility and consistency (Edwards Moloney Jacko amp Franccedilois 2008 )

According to Ross et al increasing quality of care effi ciency workfl ow man-agement and different functionalities are the main adoption factors of the health information systems ( 2010 ) It is stated that for each system users need different functionalities which are mainly described as the search ability through patient records report creation and electronic prescribing (Ross Schilling Fernald Davidson amp West 2010 )

A study which has been conducted in Korea has shown that adoption of the EHR systems has been generally blocked by lack of workfl ow-related EHR lack of IT knowledge and concern of privacy and security (Yoon Chang Kang Bae amp Park 2012 )

Vest has categorized EHR adoption factors under three groups technological organizational and environmental context ( 2010 ) Figure 89 implies the grouping of the factors

After the adoption of EHR systems organizations are looking for further benefi -ciary actions and auditing such as warningblocking a healthcare responsible of prescribing penicillin to someone who is already stated as allergic to penicillin (Brown amp Warmington 2002 )

One of the main adoption factors of EHR is standardized guidelines which can direct the user during the healthcare process and turn the processes in a standardized way starting with data entry and at each step of procedures (Vesely Zvarova Peleska Buchtela amp Zdenek 2006 )

Likourezos et al expressed that satisfaction of nurses and physicians mainly depends on computer experience perception regarding the use of EHR and EHRrsquos effects on quality of care ( 2004 )

Lenz and Kuhn implied that organizational structure vendor capabilities and changes in the processes with new software are the main barriers for EHR system adoption ( 2004 )


198

Iakovidis described that standardization effort for certain organizations cultural attitude and technological challenges are the main barriers for EHR implementation ( 1998 )

Sagiroglu stated that integration with other systems and devices is an important success factor of EHR systems ( 2006 ) It is identifi ed that functionalities of elec-tronic health records and its alignment with organizational structure can be taken as a leading adoption factor (Sagiroglu 2006 ) Meyer et al stated that adoption of electronic health record systems through the means of information saving heavily depends on the regulations regarding the privacy of personal records ( 1998 )

To ensure easier adoption health information systems are required to have fl ex-ible architecture which can easily fi t in to the new requirements of the users or technological developments (Toussiant amp Lodder 1998 )

For a successful adoption health information systems need to integrate with other systems or equipment with certain standards (Blazona amp Koncar 2007 ) Moreover electronic health records provide inter-organizational communication which offers a great chance for elderly people that need home care (Helleso amp Lorensen 2005 )

Technological Readiness

Certified EHR

Point-to-point connection technologies

Vertical Integration

Information Needs

Competition

Uncompensated care burden

Horizontal Integration

Control

Environmental Context

Organizational Context

Technological Context

Health information exchange adoption amp implementation

SizeOrganizational complexityNo of potential partnersDays cash on handUrban Rural

Control Variables

Fig 89 Categorization of adoption factors (Vest 2010)

OM Koumlk et al

199

83 Framework

In order to develop a model and taxonomy detailed literature review and semi- structured interviews have been conducted Constructs have been analyzed and then grouped under four categories external intermediary dependent and demographic categories

Table 81 implies the constructs that have been gathered via literature review and semi-structured interviews (L) refers to a construct that has been gathered from literature review (I) refers to a construct that has been gathered from the semi- constructed interviews

Literature has been deeply researched and factors affecting the technology adop-tion health information system adoption and electronic health record adoption have been analyzed Table 82 refers to the subjects and articles of the literature research

Thanks to the expert focus group and semi-structured interviews some of the constructs have been selected for a deeper analysis These constructs have struc-tured the base of our study The list of constructs and their explanations are implied in Table 83

Table 84 lists the major constructs and the literatures that they have been implied before

There are dependent items which are affected by the external factors via the intermediary factors

Table 81 Construct list from interviews and literature

Access validation (L) Disaster recovery (L) Reliability (L)

Accuracy (L) (I) Easy access (I) Reporting (I) Age (L) Ease of learning (I) Response time (L) Attitude (L) Ease of use (L) (I) Search ability (L) (I) Auditing L) Effi ciency (L) Self-confi dence (L) Authorization (L) (I) Flexibility (L) (I) Security (L) Comparison (L) (I) Image (L) Sharing (L) (I) Complexity of treatment (I) Integration (L) (I) Staff anxiety (L) Computer skills (L) Input effort (L) (I) Standardization (L) (I) Completeness (L) (I) Input time (L) Statistics (L) (I) Compatibility (L) Job experience (L) Subjective norm (L) Consistency (L) Job level (L) Support quality (L) (I) Copy (L) Medical assistant (I) Taskndashtechnology fi t (L) (I) Cost (L) Medical history (L) (I) Time saving (L) (I) Customization (L) (I) Normative beliefs (L) Training time (L) Data migration (L) Organization type (L) Usage goal (L) (I) Data preservation (L) (I) Online consultation (I) User interface (L) (I) Decision effectiveness (L) Privacy (L) (I) Usefulness (L) (I) Decision support system (L) Providepatient relations (L) Voluntariness (L) Developer support (I) Quality of care (L) (I)


200

H1 Usefulness of the systems positively affects the quality of care H2 Attitude toward the system use positively affects the quality of care

Quality of care provided by the physicians can be defi ned as rate of successful treatments and rate of successful diagnosis Higher quality of care can be reached with a more useful system and a more positive approach to the EHR usage (Brown amp Warmington 2002 Cho Kim Kim Kim amp Kim 2010 Collins amp Wagner 2005 Ludwick amp Doucette 2009 )

H3 Diffusion is positively affected by usefulness H4 Attitude signifi cantly and positively affects diffusion H5 Infusion is signifi cantly and positively affected by attitude H6 Infusion is signifi cantly and positively affected by ease of use

Usefulness and ease of use are important factors of an individualrsquos acceptance and wide usage of an information system (Davis 1989 Venkatesh amp Davis 2000 )

H7 Usefulness of the system positively affects the attitude toward system use H8 Ease of use of the system signifi cantly and positively affects the attitude toward

the system use

Table 82 Researched literature

Subject Article

Technology adoption models

Holden and Karsh ( 2010 ) Fishbein and Ajzen ( 1975 ) Ajzen and Fishbein ( 1980 ) Kerimoglu ( 2006 ) Davis ( 1989 ) Davis Jr ( 1985 ) Venkatesh and Davis ( 2000 ) Venkatesh et al ( 2003 ) Dishaw and Strong ( 1999 ) Kerimoglu Basoglu and Daim ( 2008 )

Health adoption models

Al-Qirim ( 2007 ) Aggelidis and Chatzoglou ( 2009 ) Basoglu Daim Atesok and Pamuk ( 2010 ) Behkami and Daim ( 2012 ) Blue and Tan ( 2010 ) Brender et al ( 2000 ) Daim et al ( 2010 ) Dossler et al (2010) Gagnon et al ( 2003 ) Greenshup ( 2012 ) Hyun et al ( 2009 ) Jha et al ( 2008 ) Kijsanayotin (2009) Lenz and Kuhn ( 2004 ) Lluch ( 2011 ) Sagiroglu et al ( 2006 ) Stowe and Harding ( 2010 ) Topacan ( 2009 ) Toussiant and Lodder ( 1998 ) Tung and Chang ( 2008 ) Vest ( 2010 )

Electronic health records

Bergman ( 2007 ) Blazona and Koncar ( 2007 ) De-Meyer Lundgren De Moor and Fiers ( 1998 ) Edwards et al ( 2008 ) Haas et al ( 2010 ) Hannan ( 1999 ) Helleso and Lorensen ( 2005 ) Holbrook Keshavjee Troyan Pray and Ford ( 2003 ) International Organization for Standardization ( 2005 ) Kierkegaard ( 2011 ) Scott et al ( 2007 ) Tange et al ( 1997 ) Ueckert et al ( 2003 ) Wen et al ( 2007 ) Wang et al ( 2010 ) Wright et al ( 2009 ) Yoshihara ( 1998 )

Electronic health record adoption

Bernstein Bruun-Rasmussen Vingtoft Andersen and Nohr ( 2005 ) Brown and Warmington ( 2002 ) Cho et al ( 2010 ) Collins and Wagner ( 2005 ) Dobbing ( 2001 ) Estebaranz and Castellano ( 2009 ) Gonzalez-Heydrich et al ( 2000 ) Iakovidis ( 1998 ) Jahanbakhsh et al ( 2011 ) Likourezos et al ( 2004 ) Ludwick and Doucette ( 2009 ) Natarajan et al ( 2010 ) Ovretveit et al ( 2007 ) Rose et al ( 2005 ) Ross et al ( 2010 ) Saitwal et al ( 2010 ) Tavakoli et al ( 2011 ) Vesely et al ( 2006 ) Yoon et al ( 2012 ) Yu Li and Gagnon ( 2009 )

OM Koumlk et al

201

Tabl

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dos

e in

form

atio

n re

gard

ing

the

patie

ntrsquos

med

icat

ion

Ran

ge f

unct

iona

lity

(Fun

cRan

ge)

Syst

emrsquos

fun

ctio

nalit

y of

kee

ping

min

imum

max

imum

val

ues

rega

rdin

g th

e te

st r

esul

ts b

lood

val

ues

etc

M

edic

al in

form

atio

n fu

nctio

nalit

y (F

uncX

Med

) Sy

stem

rsquos f

unct

iona

lity

of p

rovi

ding

req

uire

d ad

ditio

nal m

edic

al in

form

atio

n to

the

user

s in

the

case

of

nece

ssity

Acc

essA

LL

U

serrsquo

s ac

cess

to a

ll re

quir

ed in

form

atio

n in

pat

ient

rec

ords

A

ccur

acy

Syst

emrsquos

cap

abili

ty to

hav

e ac

cura

te a

nd s

ensi

tive

info

rmat

ion

(Hay

rine

n et

al

200

8)

Com

plet

enes

s Sy

stem

rsquos c

apab

ility

to h

ave

com

plet

e in

form

atio

n (O

vret

veit

et a

l 2

007 )

U

p-to

-dat

enes

s Sy

stem

rsquos c

apab

ility

to u

pdat

e in

form

atio

n re

gula

rly

(con

tinue

d)


202

Tabl

e 8

3 (c

ontin

ued)

Con

stru

ct

Exp

lana

tion

Stan

dard

izat

ion

Syst

emrsquo f

unct

iona

lity

to k

eep

info

rmat

ion

alig

ned

with

nat

iona

l and

inte

rnat

iona

l sta

ndar

ds (

Yos

hiha

ra 1

998 )

M

obili

ty

Syst

emrsquos

fun

ctio

nalit

y to

off

er u

ser

acce

ssib

ility

fro

m a

nyw

here

at a

ny ti

me

Sys

tem

rsquos d

egre

e to

the

user

rsquos e

ase

of a

cces

s to

the

info

rmat

ion

(Top

acan

200

9 )

Priv

acy

unau

thor

ized

acc

ess

(Pri

vacy

UA

) Sy

stem

rsquos f

unct

iona

lity

to p

reve

nt u

naut

hori

zed

acce

ss b

ut le

tting

aut

hori

zed

user

s to

acc

ess

requ

ired

info

rmat

ion

(Dob

bing

200

1 )

Med

ical

info

rmat

ion

shar

ing

(Pri

vacy

MD

) U

serrsquo

s at

titud

e to

pat

ient

info

rmat

ion

bein

g se

en b

y ot

her

care

take

rs

Kno

wle

dge

shar

ing

Use

rrsquos

attit

ude

to s

hare

med

ical

info

rmat

ion

with

co-

wor

kers

for

con

sulta

tion

(Uec

kert

et a

l 2

003 )

Su

ppor

t qua

lity

The

qua

lity

of th

e su

ppor

t pro

vide

d by

gui

delin

es s

yste

m h

elp

func

tiona

lity

ven

dor

team

and

co-

wor

kers

Se

lf-c

onfi d

ence

In

divi

dual

rsquos o

wn

skill

s ow

n co

mpu

ter

usag

e (T

anog

lu 2

006 )

E

ase

of le

arni

ng

Syst

emrsquos

rat

e on

how

eas

ily it

can

be

lear

ned

(Hol

broo

k et

al

200

3 )

Eas

e of

use

Sy

stem

rsquos r

ate

on h

ow it

can

be

used

with

leas

t eff

ort (

Dav

is 1

989 )

U

sefu

lnes

s Sy

stem

rsquos p

ositi

ve e

ffec

ts o

n th

e en

hanc

ing

indi

vidu

alrsquos

wor

k (D

avis

198

9 )

Atti

tude

In

divi

dual

rsquos p

ositi

ve o

r ne

gativ

e pe

rcep

tion

abou

t the

sys

tem

(Fi

shbe

in amp

Ajz

en 1

975 )

Q

ualit

y of

car

e R

ate

of th

e pr

oduc

tivity

in th

e he

alth

care

ser

vice

s in

clud

ing

num

ber

of s

ucce

ssfu

l tre

atm

ents

num

ber

of

succ

essf

ul d

iagn

osis

etc

(L

udw

ick

amp D

ouce

tte 2

009 )

E

ffi c

ient

use

R

ate

on h

ow th

e in

divi

dual

effi

cie

ntly

use

s th

e sy

stem

D

iffu

sion

R

ate

on h

ow th

e sy

stem

is s

prea

d w

ithin

the

orga

niza

tion

Infu

sion

R

ate

on h

ow th

e in

divi

dual

use

s th

e of

feri

ngs

of th

e sy

stem

U

se d

ensi

ty

Rat

e on

how

foc

used

the

indi

vidu

al u

sed

the

syst

em

Satis

fact

ion

Rat

e on

how

hap

py th

e in

divi

dual

is o

n us

ing

the

syst

em

OM Koumlk et al

203

Tabl

e 8

4 M

ajor

con

stru

cts

and

thei

r lit

erat

ure

Con

stru

ct

Ana

lyze

d lit

erat

ure

Age

Sh

abbi

r et

al

( 201

0 ) V

enka

tesh

et a

l ( 2

003 )

E

ntity

type

Ja

hanb

akhs

h et

al

( 201

1 ) H

elle

so a

nd L

oren

sen

( 200

5 ) S

agir

oglu

( 20

06 )

Iak

ovid

is (

1998

) Se

curi

ty

Uec

kert

et a

l ( 2

003 )

Dob

bing

( 20

01 )

Ovr

etve

it et

al

( 200

7 ) H

olbr

ook

et a

l ( 2

003 )

Haa

s et

al

( 201

0 )

Jaha

nbak

hsh

et a

l ( 2

011 )

Ta

skndasht

echn

olog

y fi t

N

atar

ajan

et a

l ( 2

010 )

Hol

broo

k et

al

( 200

3 ) C

ayir

( 20

10 )

Dis

haw

and

Str

ong

( 199

9 ) H

yun

et a

l ( 2

009 )

Sa

giro

glu

( 200

6 )

Satis

fact

ion

Hay

rine

n et

al

(200

8) D

eLon

e an

d M

cLea

n (1

992

200

3) L

ikou

rezo

s et

al

( 200

4 )

Eas

e of

use

D

avis

( 19

89 )

Ven

kate

sh e

t al

( 200

3 ) Y

u et

al

( 200

9 ) H

olbr

ook

et a

l ( 2

003 )

Sai

twal

et a

l ( 2

010 )

Top

acan

( 20

09 )

Use

fuln

ess

Yu

et a

l ( 2

009 )

Hol

broo

k et

al

( 200

3 ) S

habb

ir e

t al

( 201

0 ) D

avis

( 19

89 )

Ven

kate

sh e

t al

( 200

3 ) V

enka

tesh

and

D

avis

( 20

00 )

Top

acan

( 20

09 )

Atti

tude

Fi

shbe

in a

nd A

jzen

( 19

75 )

Dav

is (

1989

) V

enka

tesh

and

Dav

is (

2000

) T

opac

an (

2009

) E

ase

of le

arni

ng

Hol

broo

k et

al

( 200

3 ) H

ayri

nen

et a

l (2

008)

DeL

one

and

McL

ean

(200

3)

Info

H

ayri

nen

et a

l (2

008)

Yos

hiha

ra (

1998

) O

vret

veit

et a

l ( 2

007 )

Cay

ir (

2010

) B

asog

lu e

t al

(200

9)

Jaha

nbak

hsh

et a

l ( 2

011 )

Wan

g et

al

( 201

0 )

Qua

lity

of c

are

Lud

wic

k an

d D

ouce

tte (

2009

) H

ayri

nen

et a

l (2

008)

Col

lins

and

Wag

ner

( 200

5 ) B

row

n an

d W

arm

ingt

on (

2002

)

Cho

et a

l ( 2

010 )

Tan

ge e

t al

( 199

7 ) D

ossl

er e

t al

(201

0)

Self

-con

fi den

ce

Tano

glu

( 200

6 ) D

avis

( 19

89 )

Yu

et a

l ( 2

009 )

Agg

elid

is a

nd C

hatz

oglo

u ( 2

009 )

Tun

g an

d C

hang

( 20

08 )

Priv

acy

Dob

bing

( 20

01 )

Lud

wic

k an

d D

ouce

tte (

2009

) H

aas

et a

l ( 2

010 )

Saf

ran

and

Gol

derb

erg

(200

0) B

lobe

l ( 20

06 )

Use

r in

terf

ace

Saitw

al e

t al

( 201

0 ) W

ang

et a

l ( 2

010 )

Dob

bing

( 20

01 )

Pol

at (

2010

) B

row

n an

d W

arm

ingt

on (

2002

)


204

Relationship among usefulness ease of use and attitude is explained in the TAM (Davis 1989 ) and TAM2 (Venkatesh amp Davis 2000 )

H9 Privacy function of the system which avoids unauthorized access to confi den-tial patient data positively affects the attitude

H10 Caretakerrsquos attitude toward information sharing with hisher co-workers has in impact on attitude toward system use

H11 The systemrsquos ease of learning has an impact on attitude toward system use

Holbrook et al stated that provided support on the system and ease of learning of the system have an impact on the implementation of EHR systems ( 2003 )

H12 Ease of use positively affects the satisfaction H13 Usefulness positively impacts the satisfaction H14 Electronic health record systemrsquos integration with medical equipment posi-

tively affects the satisfaction H15 Usefulness signifi cantly and positively impacts use density of the system H16 Attitude toward use signifi cantly impacts the use density of the system

(Table 85 )

In the second aspect the relationship between external factors and intermediary constructs will be analyzed

H1 Ease of use positively affects usefulness H2 Information quality positively and signifi cantly impacts usefulness H3 Flexibility of the system positively affects usefulness H4 Mobility of the system positively affects usefulness H5 Self-confi dence of the user positively affects usefulness

Table 85 Hypothesis list for dependent items

Hypotheses Dependent Independent Relationship

H1 Quality of care Usefulness Positive H2 Quality of care Attitude Positive H3 Diffusion Usefulness Positive H4 Diffusion Attitude Positive H5 Infusion Usefulness Positive H6 Infusion EoU Positive H7 Attitude Usefulness Positive H8 Attitude EoU Positive H9 Attitude PrivacyUA Positive H10 Attitude PrivacyMD Positive H11 Attitude EoL Positive H12 Satisfaction EoU Positive H13 Satisfaction Usefulness Positive H14 Satisfaction IntegrationHW Positive H15 Use density Usefulness Positive H16 Use density Attitude Positive

OM Koumlk et al

205

H6 Ease of learning of the system signifi cantly and positively affects usefulness H7 User interface signifi cantly and positively affects usefulness H8 The systemrsquos functionality related to keeping dose information of the medica-

tion positively affects usefulness H9 The systemrsquos ease of learning positively impacts the systemrsquos ease of use H10 User interface of the system positively and signifi cantly impacts the ease of

use of the system H11 Mobility of the system positively and signifi cantly affects the systemrsquos ease of

use H12 Information quality signifi cantly affects the ease of use H13 Privacy measure for avoiding unauthorized access negatively affects the ease

of use (Table 86 )

In the third model factors affecting userrsquos effi cient use of the system will be analyzed

H1 Taskndashtechnology fi t of the system signifi cantly and positively affects the effi -cient use

H2 User interface signifi cantly and positively impacts the effi cient use of the systems H3 Userrsquos ability to access all required information positively affects the effi cient

use of the system H4 The systemrsquos functionality of offering basic medical information signifi cantly

and positively impacts the effi cient use of the system H5 Information quality in the system positively impacts the effi cient use of the

systems H6 The systemrsquos integration with other software signifi cantly and positively

affects the effi cient use of the system H7 The systemrsquos functionality related to keeping dose information of the medica-

tion positively affects the effi cient use of the system (Table 87 )

Table 86 Hypothesis list for intermediary constructs


H1 Usefulness EoU Positive H2 Usefulness Info Positive H3 Usefulness Flexibility Positive H4 Usefulness Mobility Positive H5 Usefulness Self confi dence Positive H6 Usefulness Ease of learning Positive H7 Usefulness User interface Positive H8 Usefulness FuncDose Positive H9 EoU EoL Positive H10 EoU User interface Positive H11 EoU Mobility Positive H12 EoU Info Positive H13 EoU Privacy Negative


206

84 Methodology

This research study has started in September 2010 From that time many inter-views surveys literature research and observations have been conducted to deeply understand the topic and to develop hypotheses

Firstly literature research has been done between September 2010 and July 2011 Literature related to electronic health records health information systems technology adoption models and health technology adoption has been analyzed and main constructs and variables have been extracted

Furthermore to combine the literature information between September 2010 and December 2010 semi-structured interviews have been conducted with healthcare employees who use electronic health record systems Results of the literature research and semi-structured interviews have been consolidated and published in the PICMET 2011 Conference (Kok Basoglu amp Daim 2011 ) Also these studies have helped us to develop hypotheses

In the second phase of the study we have conducted a focus group study with information systems and medical experts A construct list has been provided to them to select their top preferences

In the third phase a pilot survey has been conducted with 15 participants to check the reliability of the items in the survey

In the fourth phase in order to test our hypotheses quantitative fi eld survey study has been completed with 301 participants (Table 88 )

841 Qualitative Study

Semi-structured face-to-face interviews were conducted to widen electronic health record adoption taxonomy Literature review fi ndings were aimed to be corrected and new fi ndings were expected

Interviewees were doctors who were selected from different hospitals and dif-ferent specialties Questions were prepared in a Word document which have included both factors gained from literature review and questions to discover factors which were not faced yet

Table 87 Hypothesis list for effi cient use


H1 Effi cient use TTF Positive H2 Effi cient use User interface Positive H3 Effi cient use AccessALL Positive H4 Effi cient use FuncXMed Positive H5 Effi cient use Info Positive H6 Effi cient use Integration SW Positive H7 Effi cient use FuncDose Positive

OM Koumlk et al

207

We targeted the doctors as our interview group as they are the main users of EHR systems However there are other users of the systems such as administrations nurses medical assistants etc These groups were not included in the face-to-face interviews

Eight interviews were conducted and the factors have been analyzed with their existence ratio rate of the factorrsquos occurrence in total of the interviews

Questions list can be found in Appendix 1

842 Expert Focus Group Study

After the defi nition of constructs an expert focus group has been conducted in order to prioritize the constructs Figure 810 implies the expert focus group study example

A focus group has been performed with eight experts Participants were experi-enced medical doctors and software development engineers The expert focus group questionnaire was based on Excel which has been sent to the experts and can be found in Appendix 2 Studied constructs are listed in Table 89

843 Pilot Study

Before the quantitative fi eld survey study two pilot studies were conducted to improve the fi eld survey studyrsquos quality and accuracy

The fi rst pilot study was conducted with three people with a survey of 65 ques-tions Participants have completed the survey with us and shared their comments regarding the quality or wording of the questions that we have prepared Also one of the participants requested a question to be added

Table 88 Steps of the study

Step Date Explanation

Semi-structured interviews

September 2010 Interviews were conducted with eight participants from our main target group doctors Results of the study have been published in PICMET-2011 conference

Expert focus group study

August 2011 A focus group study has been conducted with eight participants including doctors and software developers Participants were asked to choose 20 most important constructs from the construct list that we have provided

Pilot study January 2012 In order to test the research instrument a pilot study has been conducted with 15 participants Sixty-fi ve questions survey has been conducted with participants Then reliability analysis and factor analysis have been conducted

Quantitative fi eld survey study

February 2012 Quantitative fi eld survey study has been conducted with 301 participants Reliability analysis factor analysis regression modeling ANOVA analysis and clustering have been done with the results


208

The second pilot study was shared via a web survey system Fifteen people have participated in the second pilot study Results of the pilot study have been used as an input for the reliability and factor analysis test in the Statistical Package for Social Sciences (SPSS)

844 Quantitative Field Survey

After the pilot study the survey has been prepared in a web-based tool and shared via e-mail through different channels Initially three hospitals were targeted Then with efforts of the Manisa City Health Department the survey is shared with the

Fig 810 Expert focus group construct list

Table 89 Constructs studied in focus group

Accessibility Guidelines Quality of support

Accuracy Habit Successful treatment Adequate resources Hospital size Successful decision Age Image Successful diagnosis Behavioral control Income Response time Clinical specialty Information quality Risk Compatibility Job experience Satisfaction Computer experience Job relevance Security Computer literacy Managerial support Social infl uence Ease of learning Marital status Standardization Ease of use Medical Taskndashtechnology fi t Educational level Occupation Tool experience Facilitating conditions Other clinical variables Trust Flexibility Peer support Usefulness Functionality characteristics Place of residence User interface Gender Population serviced Vendor support Geographic area Professional support Voluntariness

OM Koumlk et al

209

family practitioners of the city of Manisa They have shown great participation and the quantitative fi eld survey study has been applied to 301 people in total Mostly the participants were family health practitioners in the city of Manisa

85 Findings

851 Qualitative Study Findings

Semi-structured face-to-face interviews have been conducted with eight participants

bull 375 + of the participants were females bull 50 of the employees had more than 15 years of work experience bull Only one participant had his own clinic the remaining ones were working at a

hospital bull Average age of the interviewees was 41

General characteristics of the interviewees can be found in Table 810 Constructs which two or more interviewees have implied are listed in Table 811

with their frequency and frequency rate during the interviews (in total eight interviews)

Several important factors have been defi ned via combination of literature review and qualitative research

8511 Sharing and Privacy

Easy sharing is the one of the other important factors It is implied that unlike the paper records medical records can be shared easier and faster without making phys-ical transaction such as photocopying (Safran amp Golderberg 2000 )

Also interviewers told that sometimes they are exchanging information about patients with their colleagues Moreover interviewers working in government

Table 810 Profi le of the interviewees

Specialty Age Organization Gender Experience

Brain surgeon 49 Hospital A Male 20+ Internist 50 Hospital B Male 20+ Pediatrician 46 Own clinic Male 20+ Earndashnosendashthroat 32 Hospital A Male 6 Earndashnosendashthroat 36 Hospital C Male 10 Pediatrician 38 Hospital C Female 12 Dermatologist 35 Hospital C Female 11 Pediatrician 40 Hospital C Female 15


210

hospitals explained that some of the government hospitals have been using a com-mon system and they can easily share fi les through them This also brings out that systems can be used for consultation and some EHR system can be developed with this functionality This can also be related with the doctorrsquos title and work experience One of the interviewers stated that

For some specifi c cases I request consultation over the system from more experienced doc-tors Even for some cases I share the fi le over the system with other departments to consult their opinion (Brain Surgeon 49)

Moreover it stated that many organizations started to look for exchanging healthcare data and patient data faster through networks as a result of the development in commu-nications technologies (Ueckert Maximilian Goerz Tessmann amp Prokosch 2003 )

So easier and accurate sharing is an important adoption factor of EHR systems It brings more fl exibility than paper-based records

8512 User Interface

User interface highly affects the usage of EHR systems It defi nes the mental opera-tions needed to be done and also the physical steps to take for completing a task (Saitwal Xuan Walji Patel amp Zhang 2010 )

In the in-depth interview we made we gained the feedback that most of the users have complaints about the UIs of the EHR systems Some of the doctors stated that they have diffi culties to compare the results of the tests that they requested with their pre-diagnoses and the patient complaints Because all of these are kept in different places in the system and from one UI they canrsquot view them all

Also one of the interviewers has stated that for some tasks she needs to deal with many steps

For some simple tasks even I need to go to 2ndash3 different UIs and have to click a few buttons (Female 35)

User interface affects the ease of use positively

Table 811 Frequency of the constructs

Construct Frequency Frequency rate ()

User interface 8 100 Archiving 7 88 Quality of care 6 75 Sharing 4 50 Data preservation 4 50 Search criteria 4 50 Accuracy 3 38 Time saving 2 25 Medical assistant 2 25 Standardization 2 25 Search ability 2 25

OM Koumlk et al

211


Davis defi ned the perceived ease of use as ldquothe degree to which a person believes that using a particular system would be free of effortrdquo ( 1989 )


Perceived usefulness is defi ned as ldquoextent to which a person believes that using the system will enhance his or her job performancerdquo (Davis 1989 )

It is modeled that if users believe that a system has high usefulness users will gain high performance when the system is used (Davis 1989 )

8515 Information Quality

Use of EHR brings standardization of the medical terms in the use of medical records Even though standardization of the terms may cause problems in the begin-ning of the adoption process such as requiring assistance to enter standardized names in the long term users will start to use it more effi ciently Also for effective statistics standardized records are the main base asset (Yoshihara 1998 )

One of the interviewers stated that

Electronic health records provide us to the chance to compare them with other patients and to be able to get statistics The data that I get is more qualifi ed (Male Internist 50)

Also standardization of the procedures might have a positive impact on the qual-ity of the processes (Nowinski et al 2007 ) Usage of EMR has distinctive changes on the way that physicians keep their records (Bergman 2007 ) From this stand-point we can say that getting easier statistics with standardized information is one of the important adoption factors of electronic health records We can assume that it has positive interaction with the perceived usefulness

8516 Quality of Care

Most of our interviewees have stated that EHR usage has many effects on the qual-ity of care provided EHR lets the user see the medical history of the patient consis-tently Physicians have access to see the past injuries of the patient and the treatments that have been applied to himher

If physicians do not have the enough information about the medical history of the patient they would not be able to give the right decisions The patient care process also includes the process of getting data turning it to information and then using it in the decision-making (Collins amp Wagner 2005 ) Keeping accurate and correct information is important otherwise with wrong data wrong clinician actions can be taken on the patients (Brown amp Warmington 2002 ) It has been proven in many studies that EHR has a positive effect on the quality of care


212

To be able to offer better healthcare diagnostics and treatments healthcare pro-viders should have good information about the patientrsquos situation Nowadays EHR is upcoming as the most preferred way to keep up with patient data (Haas Wohlgemuth Echizen Sonehara amp Muumlller 2010 ) Also some studies have shown that with EHR input to decision support systems for some specifi c cases like chronic illnesses quality of care has signifi cantly increased (Cho et al 2010 )

So we can assume that quality of care is an important factor on the usage of the EHR system Quality of care affects the usefulness of the systems positively

8517 Job Relevance TaskndashTechnology Fit (TTF)

As gathered from both interviews and literature EHR usage reduces the time spent in the healthcare Input time does not really decrease with the EHR usage but time spent for gathering the information and viewing the patientrsquos medical history occurs much faster (Dobbing 2001 ) Also it is stated that sometimes data entry takes a little more time than the data entry on paper-based records (Shabbir et al 2010 ) The more customized the workfl ows of the system can be the faster the user can adapt to the system (Dishaw amp Strong 1999 )

Our interviewees did not really give specifi c responses about the time that they saved during the data entry However they specifi ed that EHR usage really reduces the time spent during the search of the records and also they spend less time when they want to look for some specifi c information

8518 Functionality

Interviewees had a general opinion about EHR having many advantages with search abilities than paper-based records Users can easily and quickly search health records over the system In the old-fashioned way doctors needed to search the fi les manually between folders However our interviewees have stated that the EHR sys-tem is not fully functional about search now

If my patients have two names itrsquos hard to fi nd and identify them I need another criteria to be able to search (Earndashnosendashthroat 32)

Also another interviewee stated that

I can search with the name or identity number of the patient It could be more useful if I have some other criteria (Pediatrician 38)

With the increasing data in the EHR systems search abilities will play a very critical role to fi nd the accurate and required information (Natarajan Stein Jain amp Elhadad 2010 )

We can say that search abilities are an important factor in adoption of EHR As the search abilities are developed more it would have more effect on the use of

OM Koumlk et al

213

EHR EHR systems can offer different functionalities such as integration with other required software (IntegrationSW) integration with medical devices eg ultra-sound (IntegrationHW) keeping limit dosage values for medicines (FuncDose) containing basic health and diagnosis information to assist healthcare responsible (FuncXMed) and critical ranges for lab results (FuncRange)

8519 Archiving and Data Preservation

Medical records are essential for healthcare Thus archiving plays a critical role

With EHR system we gained a better archiving We are the master of the data now 10ndash15 years ago I was giving my patients the reports lab results and etc about them They needed to archive them in their house by themselves However mostly they were not able to keep the records They generally lost them and for next appointments they came to me without any records So this was limiting my knowledge about the patientsrsquo background and the treatments have been applied Now I keep all the records in my computer and the data is preserved (Neurobiologist 49)

One of the interviewees stated that

Papers can always get lost even if they are stored by me or the patient itself Archiving the records in computers are more reliable (Pediatrician 40)

Paper-based records bring high costs to save keep and then use again Sometimes they are transferred to different departments and sometimes they are not returned thus the data get lost (Safran amp Golderberg 2001)

Keeping the medical data is very important also for healthcare At least the health information which can be used as input for clinical decision-making should be kept and archived in systems (Estebaranz amp Castellano 2009 ) EHR history should be recorded with its updates and also should be aimed to be kept long term as required (Toyoda 1998)

85110 Medical Assistant

We found another specifi c item which is the medical assistant Medical assistants are the clerks in the hospital who are occupied for up to 2ndash3 doctors They handle the offi ce work of the doctors Some doctors stated that they let their medical assistants keep their medical records

852 Expert Focus Group Findings

Constructs gained from literature review and qualitative study have been com-piled in Excel Then the Excel file has been sent to the expert via e-mail Experts were asked to determine the 20 most favorable constructs out of 51


214

The list had the Turkish meaning English meaning and explanation of the construct

bull 125 of the participants were female bull 50 of the participants had work experience over 20 years bull Half of the participants were software experts and the other half were medical

experts (Table 812 )

Participants had consistent responses Age and ease of use constructs were selected by all participants Satisfaction compatibility usefulness and accuracy were the other signifi cant constructs

These results have been analyzed by us and the responses are used as an input to the pilot and quantitative fi eld survey studies

Detailed results can be viewed in Table 813 The selection of constructs has been done and items for the pilot study have been

chosen

853 Pilot Study Findings

8531 Participant Characteristics

Fifteen participants were involved in pilot study

bull 733 of the participants were aged between 18 and 25 bull 50 of the participants had at least a university degree bull 733 of the participants were from government hospitals

Characteristics of the pilot study participants can be viewed in Table 814

8532 Reliability and Factor Analysis

After conducting reliability analysis and factor analysis redundant items were elim-inated Table 815 shows the constructs and their related items for the quantitative fi eld survey study

Table 812 Characteristics of participants


Brain surgeon 40+ Hospital A Male 20+ Brain surgeon 40+ Hospital B Male 20+ Brain surgeon 40+ Hospital B Female 20+ Doctor 40+ Hospital C Male 20+ Software project manager 30+ Organization A Male 15+ Software architect 30 Organization B Male 10+ Software designer 20+ Organization C Male 5+ Software expert 20+ Organization D Male 5+

OM Koumlk et al

215

Tabl

e 8

13

Exp

ert f

ocus

stu

dy r

esul

ts

Con

cept

Fr

eque

ncy

Con

cept

Fr

eque

ncy

Con

cept

Fr

eque

ncy

Age

8

Occ

upat

ion

4 G

uide

lines

3

Eas

e of

use

8

Eas

e of

lear

ning

4

Com

pute

r lit

erac

y 3

Com

patib

ility

7

Geo

grap

hic

area

4

Gen

der

2 Sa

tisfa

ctio

n 7

Popu

latio

n se

rvic

ed

4 C

linic

al s

peci

alty

2

Use

fuln

ess

6 H

ospi

tal s

ize

4 Pr

ofes

sion

al s

uppo

rt

2 A

ccur

acy

6 V

endo

r su

ppor

t 4

Tool

exp

erie

nce

2 Se

curi

ty

6 So

cial

infl u

ence

4

Rat

e of

suc

cess

ful t

reat

men

ts

2 Q

ualit

y of

sup

port

5

Func

tiona

l cha

ract

eris

tics

4 H

abit

2 St

anda

rdiz

atio

n 5

Acc

essi

bilit

y 4

Tru

st

2 In

form

atio

n qu

ality

5

Rat

e of

suc

cess

ful d

iagn

osis

4

Mar

ital s

tatu

s 1

Secu

rity

5

Edu

catio

nal l

evel

3

Job

expe

rien

ce

1 Fa

cilit

atin

g co

nditi

ons

5 Ta

skndasht

echn

olog

y fi t

3

Ade

quat

e re

sour

ces

1 Jo

b re

leva

nce

5 R

isk

3 Fl

exib

ility

1

Rat

e of

dec

isio

n ef

fi cie

ncy

5 Pl

ace

of r

esid

ence

3

Beh

avio

ral c

ontr

ol

1 R

espo

nse

time

5 M

anag

eria

l sup

port

3

Com

pute

r ex

peri

ence

1

Use

r in

terf

ace

5 V

olun

tari

ness

3


216

Table 815 Reliability analysis of pilot study

Construct c Alpha Items before deletion Items after deletion

User interface 0736 8 8 Usefulness 0773 7 7 Info 0613 5 5 EoU 0429 4 4 Satisfaction 0851 4 2 Flexibility 0694 3 3 Sharing 0328 3 0 TTF 0596 3 3 Mobility 0474 3 3 Quality of care 0714 3 3 Security 0254 2 2 Support quality 0691 2 2 Attitude toward use 0851 2 2

Table 814 Participant characteristics of pilot study

Item Range Frequency Percentage

Age 18ndash25 11 733 26ndash35 1 67 35ndash45 0 00 45ndash55 3 200 55+ 0 00

Education High school 7 500 University 5 357 Masters 0 00 PhD 2 143

Goal Medical 13 867 Management 2 133 Financial 0 00

Entity type Family treatment 0 00 Government 4 277 Private hospital 11 733

Reliability analysis has been conducted between the constructs Generally reli-ability results were over 0600 and items were considerably reliable However con-structs such as mobility security and sharing had lower reliabilities The main reason for this situation is related to the low number of observations and low num-ber of items in the test These results have been ignored and constructs have been kept same Detailed results of the reliability analysis can be seen in Table 815

OM Koumlk et al

217

Table 816 Profi le of the respondents





Entity type Family treatment center 251 839 Government 4 13 Private hospital 44 147

Seven components have been extracted with the factor analysis for all items Detailed results for factor analysis of the pilot study can be found in Appendix 3 Factor analysis results have also supported our hypotheses

854 Quantitative Field Survey Study Findings

A study aimed to explore and understand factors affecting the adoption of electronic health record systems A web-based data collection tool has been used to gather data via questionnaire from healthcare employees from different organizations with different purposes

8541 Profi le of the Respondents

Most of the respondents were university graduates (432 ) and majority of the respondents were in the age between 36 and 45 (632 ) Systems in the respon-dentrsquos work locations were mainly used for medical purposes Doctors employed in the family treatment centers constituted the majority of the respondents with 854 (Tables 816 ndash 818 )


218

Table 818 Respondent profi le by entity goal and centrality

Entity type Central

Goal

Medical Admin Finance Total

Family HC No 1 1 Government Yes 215 32 3 250 Private Yes 2 2 4 Blank No 3 3 Family HC Yes 34 5 2 41 Government Yes 2 2 Total 257 39 5 301

Table 817 Respondent profi le by entity and education

Entity type Education High S Uni Masters PhD Blank Total

Family HC 6 176 40 28 1 251 Government 1 2 1 4 Private 15 11 4 13 1 44 Blank 1 1 2 Total 23 189 45 42 2 301


Responses from the survey have been evaluated with reliability analysis and factor analysis Validity of the constructs and reliability of the items have been investi-gated with these studies For multi-item constructs lowest c alpha value was calcu-lated as 0676 In general c alpha values were over 0800 which show that the consistencies of the items were relatively signifi cant However constructs such as support quality and fl exibility have lower consistencies compared to the others (Table 819 )

Factor analysis has been conducted on all constructs Ten main components have been extracted For intermediary construct group one component was extracted with 70 variance For dependent construct group one component was iterated with a variance of 67 Finally for external constructs four components have been devel-oped with a 57 variance Detailed factor analysis results can be seen in Appendix 3

8543 Descriptives

Descriptive statistics show us that participants do not have a certain decision about information sharing with our colleagues In average they all fi nd the electronic health records software easy to learn easy to use and useful They generally have a positive attitude to the electronic health record software usage They are mostly satisfi ed with the software and they believe that they are effi ciently using the soft-ware Descriptive results of the summated constructs can be found in Table 820

OM Koumlk et al

219

Table 819 Reliability analysis results

Construct of items c Alpha

Satisfaction 3 0943 Info 5 0915 Usefulness 7 0914 Attitude 2 0905 TTF 3 0863 EoU 4 0854 Security 2 0826 QualityofCare 3 0819 Mobility 3 0804 User interface 8 0770 Flexibility 3 0696 SupportQuality 2 0676

Table 820 Descriptive statistics for all constructs

Construct Mean Median Mode Min Max SD

IntegrationHW 174 1 1 1 5 155 IntegrationSW 055 1 1 0 1 050 FuncDose 057 1 1 0 1 050 FuncRange 050 1 1 0 1 050 FuncXMed 051 1 1 0 1 050 AccessALL 082 1 1 0 1 039 PrivacyUA 345 4 4 1 5 117 PrivacyMD 354 4 4 1 5 118 KnowledgeShare 323 3 4 1 5 122 SelfConfi dence 401 4 4 1 5 096 EoL 379 4 4 1 5 106 Effi cientUse 761 8 8 1 10 181 Diffusion 389 4 4 1 5 090 Infusion 371 4 4 1 5 103 UseDensity 405 4 4 1 5 089 Attitude 407 4 4 1 5 074 Security 379 4 4 1 5 092 SupportQuality 340 350 4 1 5 098 EoU 403 4 4 1 5 073 Flexibility 367 360 4 1 5 086 Mobility 368 4 4 1 5 095 QualityofCare 361 360 4 1 5 084 Satisfaction 395 4 4 1 5 090 TTF 389 4 4 1 5 088 Info 385 4 4 1 5 078 Usefulness 390 4 4 1 5 073 UserInterface 369 370 370 110 5 062


220

8544 Regression Model Results

Obtained data has been analyzed using the IBM SPSS v20 software Linear regres-sion modeling has been chosen as the applied methodology Results of the executed regression model for dependent items are listed in Tables 821 and 822

Based on the regression results two models have been developed One shows the relationship between the external factors intermediary factors and dependent fac-tors The second model shows the relationship between the external factors and effi cient use First model is implied in Fig 811 and second model is implied in Fig 812 (Table 823 )

Regression results show that usefulness and attitude are direct determinants of quality of care with coeffi cients 055 ( p lt 0001) and 024 ( p lt 0001) Usefulness ( p lt 0001) and attitude ( p lt 001) explains 0568 of the diffusion respectively On the other hand infusion is dependent on usefulness ( p lt 0001) and EoU ( p lt 0010) Our hypothesis that attitude is dependent on PrivacyUA PrivacyMD and EoL was not supported in the regression analysis However results showed that 0710 of attitude is dependent on usefulness with a coeffi cient of 068 ( p lt 0001) and on EoU with a coeffi cient of 020 ( p lt 0001) The relationship between attitude EoU and usefulness was also supported in Davisrsquos TAM model (Davis 1989 ) Although EoU ( p lt 0001) and usefulness ( p lt 0001) explain the 0710 of satisfaction analysis did not imply that hardware integration (IntegrationHW) affects satisfaction Usefulness ( p lt 0001) and attitude ( p lt 0100) explain the 0417 of use density (Table 824 )

Information quality ( b 030 p lt 0001) ease of use ( b 020 p lt 0010) fl exibility of the software ( b 014 p lt 0010) mobility of the software ( b 014 p lt 0010) selfconfi dence of the individual( b 011 p lt 0010) user interface of the software ( b 015 p lt 0100) and dose functionality of the software ( b 007 p lt 0100) explain the 0752 of usefulness factor Results also show similarities with other models An unsupported hypothesis was that privacy negatively affects ease of use and ease of learning affects usefulness (Table 825 )

Effi cient use of the system is explained mainly with taskndashtechnology fi t ( b 027 p lt 0001) and user interface ( b 028 p lt 0001) is then affected with AccessALL ( b 014 p lt 0002) medical information functionality of the software ( b 009 p lt 0100) information quality ( b 017 p lt 0010) integration of the system with other software ( b 011 p lt 0100) and dose functionality of the system ( b 009 p lt 0100)

8545 ANOVA Results

ANOVA analysis has been conducted on demographic values including age entity goal and education

Signifi cant results for ANOVA analysis based on age construct can be found in Table 826 Participants are grouped under fi ve different age categories 18ndash25 26ndash35 36ndash45 46ndash55 and 55+ It can be seen that participants in the age of 55+ are more satisfi ed with their EHR system and use the system more densely People in

OM Koumlk et al

221

Tabl

e 8

21

Reg

ress

ion

resu

lts f

or d

epen

dent

fac

tors

Dep

ende

nt

Inde

pend

ent

Coe

ffi c

ient

bet

a St

anda

rdiz

ed c

oeffi

cie

nt

Sign

ifi ca

nce

R 2

Adj

uste

d R

2

Qua

lity

of c

are

(Con

stan

t)

005

0

786

057

8 0

575

Use

fuln

ess

063

0

55

000

0 A

ttitu

de

027

0

24

000

0 E

ffi c

ient

use

(C

onst

ant)

minus

020

0

697

054

2 0

529

TT

F 0

57

027

0

000

Use

rInt

erfa

ce

079

0

28

000

0 A

cces

sAL

L

068

0

14

000

2 Fu

ncX

Med

0

33

009

0

049

Info

0

39

017

0

009

Inte

grat

ionS

W

039

0

11

001

8 Fu

ncD

ose

034

0

09

004

4 D

iffu

sion

(C

onst

ant)

0

10

061

1 0

572

056

9 U

sefu

lnes

s 0

67

054

0

000

Atti

tude

0

29

024

0

001

Infu

sion

(C

onst

ant)

minus

024

0

346

046

4 0

460

Use

fuln

ess

069

0

49

000

0 E

oU

031

0

22

000

1 U

se d

ensi

ty

(Con

stan

t)

085

0

000

042

1 0

417

Use

fuln

ess

062

0

51

000

0 A

ttitu

de

019

0

16

004

4 Sa

tisfa

ctio

n (C

onst

ant)

minus

043

0

009

071

2 0

710

EoU

0

56

045

0

000

Use

fuln

ess

054

0

44

000

0


222

Table 822 Regression results for intermediary factors

Dependent Independent Coeffi cient beta

Standardized coeffi cient Signifi cance R 2 Adjusted R 2

Attitude (Constant) 056 0000 0712 0710 Usefulness 069 068 0000 EoU 020 020 0000

Usefulness (Constant) 011 0464 0759 0752 Info 028 030 0000 EoU 019 020 0002 Flexibility 012 014 0002 Mobility 011 014 0003 SelfConfi dence 009 011 0006 UserInterface 017 015 0010 FuncDose 011 007 0027

EoU (Constant) 017 0238 0775 0771 UserInterface 046 038 0000 Info 025 027 0000 EoL 019 024 0000 Mobility 013 017 0000

020

044

045

051

054

055

Diffusion

Infusion

Attitude

Use Density

Satisfaction

Quality of Care

EoU

Usefulness

Use Density

EoL

Self Confidence

Func Dose

User Int

Mobility

Info

Flexibility

p lt 0100 p lt 0010 p lt 0001

Fig 811 Factors affecting the EHR adoption

OM Koumlk et al

223

027 Efficient Use

FuncXMed

Func Dose

User Interface

TTF

Access All

Info

IntegrationSW

p lt 0100 p lt 0010 p lt 0001

Fig 812 Factors affecting the effi cient use of EHR

Table 823 Results for dependent items

Hypotheses Dependent Independent Supported Signifi cance

H1 Quality of care Usefulness Yes 0000 H2 Quality of care Attitude Yes 0000 H3 Diffusion Usefulness Yes 0000 H4 Diffusion Attitude Yes 0001 H6 Infusion Usefulness Yes 0000 H7 Infusion EoU Yes 0001 H8 Attitude Usefulness Yes 0000 H9 Attitude EoU Yes 0000 H10 Attitude PrivacyUA No ndash H11 Attitude PrivacyMD No ndash H12 Attitude EoL No ndash H13 Satisfaction EoU Yes 0000 H14 Satisfaction Usefulness Yes 0000 H15 Satisfaction IntegrationHW No ndash H16 Use density Usefulness Yes 0000 H17 Use density Attitude Yes 0044


Table 824 Results of intermediary items


H1 Usefulness EoU Yes 0000 H2 Usefulness Info Yes 0002 H3 Usefulness Flexibility Yes 0002 H4 Usefulness Mobility Yes 0003 H5 Usefulness Self-confi dence Yes 0006 H6 Usefulness Ease of learning No ndash H7 Usefulness User interface Yes 0010 H8 Usefulness FuncDose Yes 0027 H9 EoU EoL Yes 0000 H10 EoU User interface Yes 0000 H11 EoU Mobility Yes 0000 H12 EoU Info Yes 0000 H13 EoU PrivacyUA No ndash

Table 825 Results of effi cient use


H1 Effi cient use TTF Yes 0000 H2 Effi cient use User interface Yes 0000 H3 Effi cient use AccessALL Yes 0002 H4 Effi cient use FuncXMed Yes 0049 H5 Effi cient use Info Yes 0009 H6 Effi cient use IntegrationSW Yes 0018 H7 Effi cient use FuncDose Yes 0044

Table 826 ANOVA results for age

Construct F Sig 18ndash25 26ndash35 36ndash45 46ndash55 55+

23 24 130 110 14 IntegrationHW 1561 0000 386 252 153 151 100 Satisfaction 720 0000 307 381 410 397 417 SelfConfi dence 676 0000 313 413 411 410 357 UserInterface 590 0000 313 360 378 374 366 EoL 567 0000 291 358 396 385 350 UseDensity 541 0000 330 383 415 410 429 SupportQuality 520 0000 265 313 353 342 379 TTF 484 0001 317 382 401 388 410 Info 438 0002 326 375 394 387 410 Flexibility 438 0002 306 338 373 377 376 Mobility 423 0002 312 336 382 364 407 Attitude 408 0003 352 410 417 404 421 Usefulness 381 0005 336 389 398 389 403 EoU 363 0007 350 397 410 407 414 IntegrationSW 333 0011 057 077 060 042 062 PrivacyMD 324 0013 296 346 377 346 314 Diffusion 293 0021 335 392 402 385 386 FuncRange 283 0025 065 058 040 059 043

225

the age between 26 and 36 have more self-confi dence than other participants Participants in the age of 36ndash45 fi nd their system easier to learn

Signifi cant ANOVA results for education (Table 827 ) show that participants with a PhD have higher self-confi dence than other participants and also they care less about privacy issues

ANOVA results for entity types show that (Table 828 ) participants from family treatment centers are more satisfi ed with their system and they believe that their system is aligned with their workfl ow On the other hand government and private hospital participants stated that their systems are effectively integrated with diag-nostic healthcare devices

ANOVA results for software usage goal show that participants who use the sys-tem for medical purposes fi nd the system more useful and show a more positive attitude to the usage of the system On the other hand participants who use the system for management and fi nance purposes are more self-confi dent and keen on information sharing Whole results are implied in Table 829

8546 Cluster Analysis

Sample clustering has been applied to the participants with two different construct sets Two- three- and four-group cluster analysis have been applied and the four- group analysis has given the most signifi cant results in both sets Case numbers have been shown for each group in Table 830 for the fi rst analysis

The fi rst cluster is the moderately satisfi ed cluster They have an average attitude and average satisfaction with most of the constructs The second cluster is the least satisfi ed cluster with low satisfaction rates The third cluster is the totally satisfi ed one with high satisfaction rates and positive attitude They are also pleasant about the general functionalities and specifi cations The last cluster is the partially adopted group They are not pleasant about all the functionalities or specifi cations of the system Thus they are partially satisfi ed

Table 827 ANOVA results for education

Construct F Sig High S Uni Masters PhD

23 189 45 42 IntegrationHW 1521 0000 389 149 173 186 EoL 565 0001 296 385 398 381 SelfConfi dence 561 0001 330 408 387 419 FuncDose 433 0005 070 062 045 037 IntegrationSW 366 0013 085 054 041 060 UserInterface 323 0023 340 374 379 355 Satisfaction 320 0024 346 402 406 381 EoU 288 0036 375 409 417 385 Mobility 279 0041 317 375 374 358 PrivacyMD 273 0044 330 357 387 319


226

Table 828 ANOVA results for entity

Construct F Sig FHC Gov- Pri

251 48 IntegrationHW 11306 0000 139 367 Satisfaction 8033 0000 414 300 UserInterface 7707 0000 382 306 TTF 5837 0000 404 308 Infusion 5676 0000 389 277 EoU 4272 0000 415 345 Diffusion 3947 0000 403 319 Flexibility 3924 0000 380 301 SupportQuality 3584 0000 355 268 Mobility 3580 0000 382 298 Usefulness 3516 0000 401 337 UseDensity 3118 0000 416 342 EoL 2629 0000 393 310 Info 2383 0000 395 338 QualityofCare 2014 0000 371 314 Attitude 1675 0000 415 369 Effi cientUse 1561 0000 779 669 SelfConfi dence 1336 0000 410 356 Security 1183 0001 387 339 PrivacyUA 887 0003 354 300 PrivacyMD 593 0015 362 317 FuncRange 535 0021 047 066

Results of the fi rst clustering can be seen in Fig 813 and Table 831 Second clustering has been done related to characteristics of the systems and

user behavior (Table 832 ) The fi rst group was the average systems Their characteristics were fulfi lling the

user expectations somehow The second cluster was the least functional systems The third cluster was the moderate systems They had similar performance to the average system cluster however their performance was shown on different charac-teristics The fourth cluster was the capable systems They had high-performance characteristics in each area Detailed results of the clustering can be seen in Table 833 and Fig 814

8547 Participant Comments

At the end of the questionnaire two open-ended questions were asked to the participants regarding their requests for modifi cations and extra functionalities related to the systems The following quotes include selected responses from the participants

OM Koumlk et al

227

Table 829 ANOVA results for goal

F Sig Medical MngmtmdashFin

257 44 SupportQuality 834 0004 347 301 Satisfaction 809 0005 401 360 Usefulness 692 0009 394 363 Flexibility 620 0013 372 337 Security 560 0019 384 349 EoU 556 0019 408 380 FuncDose 519 0024 059 040 QualityofCare 511 0024 366 335 Mobility 483 0029 373 339 Infusion 462 0032 377 341 Attitude 395 0048 410 386 AccessALL 355 0060 084 071 Diffusion 354 0061 393 366 PrivacyUA 325 0072 350 316 Info 227 0133 388 369 UserInterface 190 0169 371 357 Effi cientUse 181 0180 767 727 UseDensity 167 0197 407 389 TTF 122 0270 391 375 SelfConfi dence 095 0331 398 414 IntegrationSW 079 0374 054 062 FuncRange 078 0377 051 044 EoL 034 0562 381 370 PrivacyMD 028 0599 356 345 IntegrationHW 021 0648 172 184 KnowledgeShare 015 0696 322 330 FuncXMed 000 0973 051 051

Table 830 Cluster distribution

Cluster of cases in each cluster Percentage

Moderate 103 342 Least satisfi ed 17 56 Totally satisfi ed 161 535 Partially adopted

20 66

Currently we only have access to the patient records related to the family health centers In order to make a full assessment we need to see the whole medical history of the individual (Healthcare Practitioner)

We should be able to request laboratory tests x-ray diagnosis and etc for patient via online channel from other institutions Also the results should be delivered via same mod-ule quickly and effectively (Healthcare Practitioner)

The system should be integrated with the MEDULA (Social Insurance Medicine System) Otherwise we canrsquot be able to see which medicines the patient has been prescribed


228

0123456789

EoU

EoL

Usefulness

Attitude

Satisfaction

QualityofCare

EfficientUse

UseDensity

Diffusion

Infusion

1

2

3

Fig 813 Cluster analysis 1

Table 831 Cluster analysis 1 results

1 2 3 4

EoU 383 263 442 320 EoL 346 276 414 355 Usefulness 366 278 432 271 Attitude 391 315 442 280 Satisfaction 367 202 448 280 QualityofCare 344 245 401 230 Effi cientUse 641 335 880 790 UseDensity 386 229 448 295 Diffusion 376 229 435 225 Infusion 346 171 426 235

Table 832 Cluster analysis 2 distribution


Average systems 65 223 Least functional systems 29 100 Moderate systems 125 430 High-performance systems 72 247

OM Koumlk et al

229

to and their dosages This creates problems when we need to prescribe to the patient (Healthcare Practitioner)

These three quotes defi nitely show that caretakers require integration with other healthcare institutions Integration with other institutions will provide access to the full medical history of the patients and also the whole medical examination and testing process will be kept in a common environment

System has low response times This creates delays in our caretaking process (Healthcare Practitioner)

In the user interface warnings should come up about the patientrsquos allergies vaccine deadline and etc (Healthcare Practitioner)


1 2 3 4

Flexibility 372 247 352 442 Info 390 250 370 465 AccessALL 083 079 075 093 KnowledgeShare 263 259 334 382 Mobility 370 226 346 462 PrivacyMD 198 328 398 424 PrivacyUA 314 266 320 449 Security 388 233 359 467 SelfConfi dence 398 303 383 476 SupportQuality 356 198 320 417 TTF 369 263 382 469 UserInterface 373 266 360 428

000

100

200

300

400

500

600Flexibility

Info

AccessALL

KnowledgeShare

Mobility

PrivacyMD

PrivacyUA

Security

SelfConfidence

SupportQuality

TTF

UserInterface

1

2

3

4



230

I canrsquot make changes in the past information sometimes mistakes or mistypes exist in the recorded data (Healthcare Practitioner)

These three comments raise the caretakersrsquo main problems regarding the sys-temrsquos performance or user interface The last one discusses the data update mecha-nism However that request needs a detailed and secure process map in order to be successful since there are certain privacy data quality and security issues

Sometimes properly working modulesfunctions of the systems are being altered due to testing new functions This creates problems as they also break the properly working mod-ules (Healthcare Practitioner)

This request is related with the updates in the system and their effects Developers should consider the ongoing work of the caretakers and system updates should not go live without a proper testing period that does not affect the live system

A mobile version of this system should be developed since we often conduct on-site visits to patient homes or villages out of the city center (Healthcare Practitioner)

This quote is mainly aligned with the requirements of our era Many software offer mobile applications and mobile versions After the main developments are complete in the system developers should consider the mobile version of the appli-cations as the next step

86 Conclusion

As the usage of electronic health record systems increases developers systems architects and project managers will focus on them more Adoption process and diffusion factors will be the main input for the implementation and development of electronic health record systems This study has focused on the adoption factors and developed a model implying the interaction of intermediary dependent and exter-nal factors and their effects on the use and attitude

Main determinants for EHR adoption process have been defi ned as attitude ease of use and usefulness These results also align with TAM TAM2 and UTAUT It is also found that attitude ease of use usefulness and ease of learning have effects on satisfaction infusion diffusion and use density processes

Effi cient use of the electronic health record systems is mainly affected by the functionalities of the systems user interface integration taskndashtechnology fi t infor-mation quality and accessibility Taskndashtechnology fi t was also investigated by Hyun et al in 2009 and it was stated that the system should fi t with workfl ows of the healthcare employees

In conclusion this study provided a model in light of a quantitative fi eld survey study and is supported by the prior literature The relationship among dependent factors intermediary factors and external factors has been analyzed

OM Koumlk et al

231

861 Limitations

This study had some limitations First of all it has been applied among three hospi-tals and Manisa family health practitioners Results may differ when the quantitative fi eld survey study has been applied in different geographic regions and among differ-ent professionals Secondly all participants of the survey were using centralized record systems Ones that have their own individual systems for record keeping might have different adoption factors It would be sounder if we could recruit strati-fi ed representative health professional samples from different health units of the country such as state hospitals university hospitals private hospitals primary health-care facilities and those who use specialized record systems such as a cancer regis-try As another restriction the majority of our data come from the primary healthcare facilities of Manisa in which the data were collected via an announcement from the province health directorate of Manisa This might positively bias the results

862 Implications

During this study main adoption factors of EHR system usage have been analyzed

Effi cient use of the EHR system is found to be mainly related with the alignment between the systemrsquos workfl ow and the individualrsquos daily tasks It can be stated that the more the developers adapt their systemsrsquo workfl ows to the individualsrsquo tasks the more effi ciently their system will be used or this can be considered vice versa Also effi cient use of the system is found to be mainly dependent on the functionalities of the system and its integration with other required software Developers should focus on offering more functionality with their system such as dose functionality and medical critical value range Other factors that developers or software architects should take into account are information quality user interface and accessibility

The information quality factor is considered a multi-construct factor in our study We defi ned information quality from completeness accuracy and up-to-dateness aspects Future studies may also include other aspects and take into account differ-ent factors

Quality of care was found to be an important factor during the whole research since caretakers aim to offer the best care The relationship between quality of care and EHR systems is found to be usefulness of the system and the individualrsquos attitude

Infusion rate is found to be dependent on usefulness and ease of use of the sys-tem So developers should try to focus on creating systems which are found to be more useful and easy to use

Usefulness of the system is defi ned with information quality fl exibility mobility user interface and ease of use factors in the developed model Moreover the individualrsquos


232

self-confi dence is taken into account as an important factor This shows that individuals who have more computer experience will fi nd the system more useful

Ease of use of the system is found to be correlated with information quality ease of learning mobility and user interface of the system We can say that software developers should focus on the user interface of their product and make it easier to learn with guidelines Also this study proves that mobility is an important adoption factor and should be considered with priority

Outputs of this study and the developed model can be a really useful input for further researches More comprehensive or more detailed frameworks can be devel-oped from this research

87 Appendices

871 1 Interview Questions

1 Adınız 2 Yaşınız 3 Medikal Kayıt Sistemlerini daha oumlnce kullandınız mı 4 Medikal Kayıt Sistemlerini kullanmanın gerekli olduğunu duumlşuumlnuumlyor musunuz

Nedenleri nelerdir 5 Medikal Kayıt Sistemlerinin kullanım kolaylığı hakkında ne duumlşuumlnuumlyorsunuz 6 Medikal Kayıt Sistemlerinin sizce sağladığı faydalar neledir 7 Medikal Kayıt Sistemleri kullanmanız gerektiği durumlarda kayıtları kendiniz

mi tutuyorsunuz yoksa bu konuda daha yetkin kişilerden yardım mı alıyorsunuz 8 Medikal Kayıt Sistemleri geliştirilirken hangi konulara dikkat edilmesi

gerektiğini duumlşuumlnuumlyorsunuz 9 Medikal Kayıt Sistemleri kullanırken aradığınız bilgiye ulaşmakta ne gibi zor-

luklar ccedilekmektesiniz 10 Hastalarınız medikal kayıtlarının dijital ortamda tutulduğundan haberdarlar mı 11 Meslektaşlarınızla medikal kayıtları paylaşarak bilgi aktarımında bulunmakta

mısınız 12 Medikal Kayıt sistemleri kullanırken teknolojik zorluklarla karşılaştınız mı 13 Medikal Kayıt Sistemlerinde size goumlre bulunması zorunlu fonksiyonaliteler

nelerdir 14 Medikal kayıtlarınızı kendiniz mi tutmaktasınız yoksa bu konuda medikal

sekreterlerasistanlarınızdan yardım aldığınız olmakta mıdır 15 Medikal kayıtlarınızı başkalarına tutturdugunuz durumlarda kayıtların oumlnem

derecesi (ilgili hasta operasyon hastalık) bu kararı vermenizde etken oluyor mu

OM Koumlk et al

233

(con

tinue

d)

Oumlze

llikl

er

Anl

am

Accedilı

klam

a D

emog

raph

ics

Dem

ogra

fi k

Kul

lanı

cını

n de

mog

rafi k

oumlze

llikl

eri

1 A

ge

Yaş

K

ulla

nıcı

nın

yaşı

2

Edu

catio

nal L

evel

E

ğitim

Duumlz

eyi

Kul

lanı

cını

n eğ

itim

duumlz

eyi

3 G

ende

r C

insi

yet

Kul

lanı

cını

n ci

nsiy

eti

4 In

com

e G

elir

K

ulla

nıcı

nın

aylık

gel

iri

5 M

arita

l sta

tus

Evl

ilik

Dur

umu

Kul

lanı

cını

n ev

lilik

dur

umu

6 Jo

b ex

peri

ence

İş

Den

eyim

i K

ulla

nıcı

nın

iş d

eney

imi

7 Pl

ace

of r

esid

ence

İk

amet

Yer

i K

ulla

nıcı

nın

ikam

et y

erin

in ouml

zelli

liği (

koumly

ilccedile

şeh

ir m

erke

zi)

8 O

ccup

atio

n M

esle

k K

ulla

nıcı

nın

mes

leği

In

term

edia

ry

Ara

cı Ouml

zelli

kler

K

ulla

nıcı

yaz

ılım

la e

tkile

şim

e ge

ccediltiğ

i sır

ada

orta

ya ccedil

ıkan

oumlz

ellik

ler

kiş

inin

yaz

ılım

ı kul

land

ığın

da k

azan

dığı

fay

da

kulla

nım

ın k

olay

olm

ası g

ibi

9 E

ase

of u

se

Kol

ay K

ulla

nım

Y

azılı

mın

kol

ay k

ulla

nım

ı 10

U

sefu

lnes

s Fa

yda

Yaz

ılım

ın k

ulla

nım

dan

doğa

n fa

yda

11

Eas

e of

lear

ning

K

olay

Oumlğr

enm

e Y

azılı

mı k

ulla

nmay

ı oumlğr

enm

enin

kol

aylığ

ı C

linic

al v

aria

bles

K

linik

Oumlze

llikl

eri

Has

tane

ile

ilgili

değ

işke

nler

12

G

eogr

aphi

c ar

ea

Coğ

rafi

Kon

um

Has

tane

nin

coğr

afi k

onum

u (ş

ehir

mer

kezi

ilccedil

e k

oumly g

ibi)

13

Po

pula

tion

serv

iced

H

izm

et E

ttiği

Nuumlf

us

Has

tane

nin

hizm

et v

erdi

ği k

işi s

ayıs

ı 14

H

ospi

tal s

ize

Has

taha

ne B

uumlyuumlk

luumlğuuml

H

asta

neni

n fi z

ikse

l buumly

uumlkluuml

ğuuml

15

Oth

er c

linic

al v

aria

bles

D

iğer

Değ

işke

nler

H

asta

ne il

e ilg

ili d

iğer

değ

işke

nler

16

A

dequ

ate

reso

urce

s K

ayna

klar

H

asta

neni

n se

rvis

icin

ayi

rabi

lece

gi k

ayna

klar

17

C

linic

al s

peci

alty

U

zman

lık A

lanı

H

asta

neni

n ge

nel u

zman

lık a

lanı

Su

ppor

t D

este

k Y

azılı

mı k

ulla

nanl

ara

veri

len

tekn

ik d

este

k

87

2 2

Exp

ert F

ocus

Gro

up Q

uest

ionn

aire


234

18

Man

ager

ial s

uppo

rt

Youmln

etim

Des

teği

Y

oumlnet

icile

rin

serv

isin

kul

lanı

lmas

ı iccedili

n ve

rdiğ

i des

tek

19

Peer

sup

port

A

rkad

aş D

este

ği

Yaz

ılım

kul

lanı

mı s

ıras

ında

yaş

ıtlar

ının

dan

veya

ark

adaş

ları

ndan

al

dığı

des

tek

20

Prof

essi

onal

sup

port

Pr

ofes

yone

l Des

tek

Yaz

ılım

kul

lanı

mı s

ıras

ında

pro

fesy

onel

lerd

en a

lınan

des

tek

21

Ven

dor

supp

ort

Satıc

ı Des

teği

Sa

tıcı fi

rm

anın

sağ

ladı

ğı y

ardı

m v

e de

stek

22

Q

ualit

y of

sup

port

D

este

ğin

Kal

itesi

V

erile

n ya

rdım

ve

dest

eğin

kal

itesi

23

So

cial

infl u

ence

So

syal

Etk

enle

r Y

azılı

mı k

ulla

nan

kişi

nin

ccedilevr

esin

deki

lerd

en

aldı

ğı e

tki

24

Com

patib

ility

U

yum

lulu

k Y

azılı

mı ouml

ncek

i suumlr

uumlmle

ri v

eya

ccedilalış

tırıld

ığı o

rtam

daki

diğ

er

sist

emle

re u

yum

u C

onte

nt

Serv

is İ

ccedileri

ği

Yaz

ılım

ın s

undu

ğu b

ilgin

in iccedil

eriğ

i 25

A

ccur

acy

Doğ

rulu

k Su

nula

n bi

lgin

in d

oğru

luğu

26

St

anda

rdiz

atio

n St

anda

rd

Bilg

inin

sta

ndar

t bir

şek

ilde

sunu

lmas

ı 27

In

form

atio

n qu

ality

B

ilgi K

alite

si

Sunu

lan

iccediler

iğin

kal

itesi

28

Se

curi

ty

Bilg

inin

Guumlv

enliğ

i İccedil

eriğ

in b

aşka

ları

nın

eriş

emey

eceğ

i bir

ort

amda

sak

lanm

ası

29

Tool

exp

erie

nce

Den

eyim

K

ulla

nıcı

nın

benz

er s

ervi

s ya

uumlruuml

n ile

ilgi

li ge

ccedilmiş

den

eyim

leri

30

Im

age

İmaj

K

ulla

nıcı

ları

n et

rafl a

rınd

aki i

nsan

lara

ken

dile

rini

far

klı

ayrı

calık

lı ve

oumlnc

uuml gouml

ster

me

iste

ği

31

Satis

fact

ion

Mem

nuni

yet

Kul

lanı

cını

n ya

zılım

dan

mem

nun

kalm

ası

32

Vol

unta

rine

ss

Goumln

uumllluuml

luumlk

Kul

lanı

cını

n yuuml

kuumlm

luumlluuml

ğuuml o

lmad

an is

teye

rek

yazı

lımı k

ulla

nmas

ı 33

Fa

cilit

atin

g co

nditi

ons

Kol

ayla

ştır

ıcı

Koş

ulla

r Y

azılı

mın

kul

lanı

mın

ı kol

ayla

ştır

acak

koş

ulla

r

34

Func

tiona

l cha

ract

eris

tics

Fonk

siyo

nel

Oumlze

llikl

er

Yaz

ılım

ın f

onks

iyon

el ouml

zelli

kler

i

35

Flex

ibili

ty

Kiş

isel

leşt

irile

bilir

lik

Yaz

ılım

ın f

onks

iyon

ları

nı is

teğe

goumlr

e de

ğişt

ireb

ilmek

Oumlrn

eğin

m

enuumln

uumln s

ıras

ı uumlze

rind

e de

ğişi

klik

yap

abilm

esi

Oumlze

llikl

erA

nlam

Accedilı

klam

aD

emog

raph

ics

Dem

ogra

fi kK

ulla

nıcı

nın

dem

ogra

fi k ouml

zelli

kler

i

(con

tinue

d)

OM Koumlk et al

235

36

Acc

essi

bilit

y U

laşa

bilir

lik

Yaz

ılım

ın k

ulla

nıcı

lar

tara

fınd

an k

olay

ula

şala

bilir

olm

ası

37

Beh

avio

ral c

ontr

ol

Kul

lanı

cını

n ya

zılım

ı kul

lanm

ak iccedil

in y

eter

li ye

tene

kler

inin

ka

ynağ

ının

ve

fırs

atın

ın o

lup

olm

adığ

ı alg

ısı

38

Job

rele

vanc

e Iş

e U

ygun

luk

Yaz

ılım

ın d

okto

run

işin

e uy

gunl

uğu

Med

ical

M

edik

al

Yaz

ılım

med

ikal

ala

ndak

i etk

ileri

39

R

ate

of s

ucce

ssfu

l tre

atm

ents

B

aşar

ılı T

edav

ileri

n O

ranı

Y

azılı

mın

kul

lanı

cını

n uy

gula

dığı

teda

vile

rin

oran

ını a

rtır

mas

ı 40

R

ate

of s

ucce

ssfu

l dia

gnos

is

Baş

arılı

Teş

hisl

erin

O

ranı

Y

azılı

mın

kul

lanı

cını

n ko

yduğ

u te

şhis

leri

n or

anın

ı art

ırm

ası

41

Rat

e of

dec

isio

n ef

fi cie

ncy

Kar

ar v

erm

e ve

rim

liliğ

inin

ar

tırılm

ası

Yaz

ılım

ın k

ulla

nıcı

nın

kara

r ve

rme

doğr

uluğ

unu

artır

mas

ı

42

Res

pons

e tim

e Si

stem

in Ccedil

alış

ma

Hız

ı Y

azılı

mın

kul

lanı

m z

aman

ı Y

azılı

mın

kul

lanı

mas

ı ccedilok

zam

an a

labi

lir v

e ku

llanı

cıla

rın

yete

rinc

e va

kti o

lmay

abili

r 43

G

uide

lines

D

oumlkuumlm

anta

syon

Y

azılı

mın

doumlk

uumlman

tasy

onu

44

Hab

it A

lışka

nlık

K

ulla

nıcı

nın

mev

cut a

lışka

nlar

ı 45

T

rust

G

uumlven

ilirl

ik

Kul

lanı

cını

n ya

zılım

a du

yduğ

u guuml

veni

C

ompu

ter

liter

acy

Bilg

isay

ar

Oku

ryaz

arlığ

ı K

ulla

nıcı

nın

bilg

isay

ar b

ilgis

i ve

okur

yaza

rlığ

ı

46

Com

pute

r ex

peri

ence

B

ilgis

ayar

Den

eyim

i K

ulla

nıcı

nın

kaccedil

yıld

ır b

ilgis

ayar

kul

land

ığı

47

Com

pute

r lit

erac

y B

ilgis

ayar

O

kury

azar

lığı

Kul

lanı

cını

n bi

lgis

ayar

kul

lanı

mın

ı ne

kad

ar iy

i bild

iği

48

Use

r in

terf

ace

Ekr

an G

oumlruumln

tuumlsuuml

Y

azılı

mın

kul

lanı

cı e

kran

ları

nın

oumlzel

likle

ri

49

Task

ndashtec

hnol

ogy

fi t

Tekn

oloj

iGoumlr

ev U

ygun

luğu

Y

azılı

mın

kul

lanı

cını

n ya

ptığ

ı goumlr

evle

re u

ygun

luğu

50

R

isk

Ris

k Y

azılı

mın

kul

lanı

lmas

ında

n do

ğabi

lece

k ol

an r

iskl

er

51

Secu

rity

G

uumlven

lik

Yaz

ılım

ın k

ulla

nılm

ası i

le o

luşa

n bi

lgik

ulla

nıcı

has

ta g

uumlven

liği


236

873 3 Factor Analysis Results for Pilot

1 2 3 4 5 6 7 Usef6 0967 0087 minus0151 0147 minus0059 minus0096 minus0017 UserInterface1 0943 0183 0036 0205 0062 0139 0107 EoU2 0931 minus0120 0001 0091 0075 0080 minus0314 Usef4 0918 0071 0152 0059 minus0292 0182 minus0082 EoU3 0902 minus0001 0033 0230 minus0293 minus0159 minus0146 FuncXMed 0868 0294 0041 0064 minus0073 minus0303 0238 EoU1 0868 0052 minus0148 minus0057 0027 0464 minus0058 UserInterface8 0855 minus0250 0273 0291 minus0185 minus0112 0019 UseDensity 0826 0209 minus0276 0346 minus0251 minus0115 minus0038 Effi cientUse 0824 minus0506 minus0116 0092 0081 0084 0173 SupportQ1 0789 minus0142 0335 0260 0204 0195 0312 Usef1 0774 0012 minus0399 0350 minus0035 0344 minus0011 Infusion 0765 minus0110 0120 0121 0539 0078 minus0276 Satisfaction2 0750 minus0018 0483 0408 minus0065 minus0053 minus0175 Diffusion 0710 minus0400 0054 0265 minus0249 0448 0016 TTF2 0710 minus0369 minus0237 0142 0426 0308 0091 Completeness 0670 0257 minus0399 0488 minus0255 0129 0078 UserInterface5 0668 0239 minus0116 0566 0327 0233 minus0042 UserInterface6 0595 minus0536 0078 0549 0002 0208 0092 UserInterface2 0543 minus0423 0413 0417 minus0211 0341 0144 Usef3 0027 0943 0157 0137 minus0247 minus0077 minus0016 QoCare1 0249 minus0915 0200 0033 0012 minus0038 minus0240 Attitude1 minus0065 0913 minus0005 0262 0195 minus0134 0194 TTF3 0237 0859 minus0166 0368 0201 minus0034 0038 UptoDate 0354 0769 minus0283 0294 minus0341 minus0032 0006 SupportQ2 0396 minus0684 0442 minus0041 minus0401 0098 minus0094 Attitude2 0271 0683 0163 minus0027 0111 minus0389 0519 Flexibility2 0374 minus0619 0040 0355 0388 0444 minus0018 SelfConfi dence 0128 0605 0145 0506 minus0489 minus0320 minus0004 PrivacyUA minus0346 minus0581 0385 0011 0383 0393 0304 IntegrationSW 0327 minus0561 minus0456 minus0242 minus0295 minus0147 0450 QoCare2 minus0120 minus0086 0965 0013 minus0118 0171 0058

(continued)

OM Koumlk et al

237

Usef7 0041 minus0035 0965 0004 0187 minus0149 0095 Consistency 0317 0247 0826 0265 minus0252 minus0065 minus0135 Mobility2 0033 minus0375 0822 minus0106 minus0276 0113 0289 Mobility3 0254 0487 0727 0041 minus0079 minus0395 0074 FuncDose minus0148 minus0259 0698 minus0266 0350 0174 minus0447 AccessALL minus0148 minus0259 0698 minus0266 0350 0174 minus0447 UserInterface3 minus0184 minus0398 0656 minus0257 minus0550 0092 minus0017 Usef5 minus0264 minus0139 0548 minus0358 0536 0389 0210 Security1 0244 0232 0086 0833 0094 minus0209 0364 Satisfaction3 0456 0250 0034 0812 0185 0068 minus0175 EoL 0258 0388 minus0256 0809 0075 0230 minus0067 Satisfaction1 0584 0102 0160 0771 0037 minus0031 minus0159 Accuracy 0634 0008 minus0174 0750 0061 0005 0021 Standardization 0127 minus0251 minus0433 0543 0363 0396 0388 FuncRange minus0251 0010 0238 0068 0934 0002 0050 PrivacyMD minus0044 0313 minus0258 0162 0830 minus0286 0193 TTF1 0467 minus0147 minus0336 0325 0693 0176 minus0176 Usef2 0360 0403 0471 minus0005 minus0612 minus0333 0033 Flexibility3 0210 minus0164 0310 0087 minus0147 0854 minus0273 Flexibility1 0500 minus0007 minus0008 minus0004 0180 0844 minus0073 IntegrationHW 0181 minus0623 0269 0130 minus0081 0645 minus0260 UserInterface4 0341 0408 0349 0218 minus0001 minus0584 0456 UserInterface7 0435 minus0430 minus0497 0064 0084 0568 0210 QoCare3 0506 0102 0431 minus0134 minus0299 minus0524 0407 Mobility1 0270 minus0041 0141 minus0096 minus0029 minus0003 minus0946 KnowledgeShare minus0011 0042 0181 minus0374 0069 minus0191 0886 EoU4 minus0195 0488 0319 0256 0021 minus0313 0677 Security2 0182 0388 minus0476 0401 0216 0017 0618

(continued)


238

Tabl

e 8

34

Fact

or a

naly

sis

for

all i

tem

s

1 2

3 4

5 6

7 8

9 10

U

sef3

0

822

012

4 0

147

028

1 0

135

001

1 0

104

minus0

096

minus0

116

003

4 Q

oCar

e3

079

4 0

177

014

2 0

240

003

3 0

105

021

1 0

031

minus0

006

005

6 U

sef2

0

793

018

8 0

146

025

3 0

128

minus0

001

011

6 minus

004

6 minus

011

8 0

038

Atti

tude

2 0

793

030

0 0

132

013

2 0

115

005

9 minus

014

6 0

011

001

8 0

098

Atti

tude

1 0

781

024

9 0

209

025

0 0

121

008

5 minus

012

5 minus

001

3 minus

001

1 0

055

Use

f1

077

4 0

233

019

8 0

249

008

3 minus

009

8 0

004

minus0

021

000

5 0

027

Dif

fusi

on

074

9 0

279

022

9 0

192

minus0

030

001

2 0

146

010

5 minus

002

3 0

036

QoC

are2

0

702

023

9 minus

001

2 0

037

010

9 0

087

020

8 0

132

020

4 0

048

Use

f6

070

1 0

389

028

3 0

186

007

8 0

031

minus0

137

006

5 minus

010

1 0

045

Use

f4

063

0 0

487

031

1 0

289

008

3 minus

004

5 0

038

009

4 0

038

002

9 Sa

tisfa

ctio

n3

055

9 0

482

043

1 0

255

004

2 minus

001

4 0

127

011

3 minus

006

5 0

018

Infu

sion

0

532

036

9 0

287

026

3 minus

001

5 0

056

021

0 0

253

minus0

103

011

1 U

seD

ensi

ty

052

2 0

359

032

3 0

397

minus0

041

minus0

093

002

1 0

082

minus0

047

008

6 Q

oCar

e1

052

0 0

179

008

2 0

075

004

9 0

104

050

4 0

068

020

6 0

140

Satis

fact

ion1

0

484

044

6 0

476

034

9 0

094

minus0

030

012

1 0

163

minus0

006

minus0

019

EoU

2 0

480

043

2 0

378

036

3 0

105

002

2 minus

016

4 0

193

004

6 minus

004

3 Sa

tisfa

ctio

n2

046

7 0

461

043

3 0

371

010

3 minus

003

2 0

098

017

0 minus

000

6 minus

002

2 U

sef7

0

448

031

9 0

145

033

0 0

212

022

9 0

162

029

9 0

114

004

1 Se

lfC

onfi d

ence

0

427

015

6 0

169

036

9 0

147

minus0

113

minus0

317

021

4 0

048

014

5 U

sef5

0

407

016

6 0

174

033

0 0

309

017

8 0

169

018

9 0

174

006

1 U

serI

nter

face

6 0

284

071

5 0

158

016

5 0

171

minus0

081

015

1 minus

005

0 0

085

001

8

87

4 4

Fac

tor

Ana

lysi

s R

esul

ts

OM Koumlk et al

239

Use

rInt

erfa

ce1

032

3 0

711

033

1 0

164

minus0

002

minus0

003

minus0

052

003

8 minus

005

0 0

068

Use

rInt

erfa

ce5

036

3 0

681

028

5 0

333

004

9 minus

001

5 minus

006

8 minus

001

9 0

038

010

8 E

oU4

043

2 0

616

017

0 0

245

017

2 0

132

minus0

086

001

5 0

048

014

2 U

serI

nter

face

2 0

208

061

5 0

357

024

8 0

115

minus0

010

021

4 0

079

010

3 minus

005

2 U

serI

nter

face

4 0

317

058

9 0

128

035

4 0

146

004

0 minus

014

5 minus

004

5 0

019

010

7 Fl

exib

ility

3 0

359

053

7 0

211

032

0 0

244

016

6 0

213

014

4 minus

003

9 minus

001

7 Fl

exib

ility

1 0

327

051

0 0

099

014

7 0

054

027

8 0

176

minus0

030

minus0

197

minus0

031

Use

rInt

erfa

ce8

035

5 0

509

018

9 0

212

015

4 0

007

011

9 0

158

020

4 minus

009

5 E

oU1

046

5 0

485

041

0 0

207

002

4 minus

006

7 minus

011

6 0

129

minus0

043

003

4 M

obili

ty1

033

3 0

458

031

2 0

097

012

6 minus

012

3 0

179

031

5 minus

016

3 0

126

TT

F2

014

7 0

200

074

5 0

262

012

8 0

052

027

8 0

042

008

8 0

029

TT

F3

031

9 0

233

067

5 0

211

021

8 0

079

006

0 0

019

minus0

085

002

9 E

oU3

033

7 0

289

065

5 0

171

009

5 minus

003

1 minus

013

9 0

141

003

9 minus

003

2 E

oL

012

1 0

287

065

0 0

178

005

1 0

083

minus0

228

014

4 minus

002

3 0

029

TT

F1

010

1 0

190

064

9 0

341

008

3 0

087

029

1 minus

001

4 minus

003

8 0

037

Use

rInt

erfa

ce7

028

7 0

221

063

2 minus

007

5 0

180

minus0

050

001

5 minus

003

6 0

077

006

8 E

ffi c

ient

Use

0

237

032

4 0

454

030

5 0

123

016

0 0

163

021

6 0

250

003

6 Pr

ivac

yUA

0

059

004

6 0

356

019

9 0

318

007

4 0

256

minus0

093

003

7 0

288

Acc

urac

y 0

396

025

8 0

186

066

6 0

124

minus0

049

001

8 0

014

007

3 0

004

Con

sist

ency

0

440

030

7 0

180

062

0 0

107

minus0

017

minus0

059

013

2 0

018

minus0

068

Stan

dard

izat

ion

040

7 0

319

024

9 0

614

016

4 minus

001

9 minus

014

5 0

153

minus0

008

007

4 Se

curi

ty1

026

3 0

262

015

6 0

610

005

2 0

098

014

9 0

021

minus0

020

035

2 U

ptoD

ate

047

0 0

222

018

2 0

596

019

3 0

061

minus0

033

005

2 0

021

minus0

016

(con

tinue

d)


240

Com

plet

enes

s 0

416

034

6 0

231

056

6 0

078

001

7 0

186

005

0 0

153

minus0

038

Secu

rity

2 0

300

030

9 0

180

055

1 0

139

007

5 minus

000

1 minus

009

1 minus

008

3 0

344

Supp

ortQ

1 0

351

040

6 0

202

050

1 0

208

007

5 0

117

007

4 minus

003

6 minus

005

0 M

obili

ty2

030

7 0

300

009

0 0

159

071

5 minus

001

9 0

043

013

0 0

058

010

2 U

serI

nter

face

3 0

066

000

7 minus

036

0 minus

013

7 minus

061

5 0

100

minus0

037

minus0

024

minus0

024

minus0

059

Mob

ility

3 0

366

033

8 0

158

025

8 0

593

013

5 minus

003

8 0

185

002

4 0

015

Func

Ran

ge

minus0

023

013

5 minus

002

9 0

023

008

2 0

751

minus0

036

007

6 0

054

minus0

097

Func

Dos

e 0

141

minus0

126

014

5 0

008

minus0

139

063

0 0

148

015

7 0

126

014

4 Fl

exib

ility

2 0

192

013

6 0

370

minus0

023

038

4 0

119

047

2 0

042

001

6 minus

002

9 A

cces

sAL

L

minus0

015

minus0

007

008

4 0

032

010

5 0

235

minus0

051

078

1 0

128

minus0

035

Supp

ortQ

2 0

298

025

0 0

054

035

5 0

124

minus0

084

019

9 0

420

minus0

034

minus0

002

Inte

grat

ionS

W

minus0

019

007

3 0

043

003

4 0

040

minus0

050

002

0 0

149

073

7 0

146

Inte

grat

ionH

W

minus0

172

minus0

141

minus0

106

minus0

132

minus0

022

036

9 minus

005

5 minus

001

2 0

547

minus0

143

Func

XM

ed

010

0 0

041

012

8 0

168

008

6 0

344

013

6 minus

011

7 0

503

minus0

222

Kno

wle

dgeS

hare

0

070

005

6 minus

005

6 0

162

004

8 minus

006

8 0

057

006

7 minus

001

4 0

792

Priv

acyM

D

012

6 minus

004

4 0

428

minus0

183

011

2 0

024

minus0

125

minus0

196

003

9 0

536

Ext

ract

ion

met

hod

pri

ncip

al c

ompo

nent

ana

lysi

s R

otat

ion

met

hod

var

imax

with

Kai

ser

norm

aliz

atio

n a R

otat

ion

conv

erge

d in

13

itera

tions

Tabl

e 8

34

(con

tinue

d)

12

34

56

78

910

OM Koumlk et al

241

Component 1

Satisfaction 0881 Diffusion 0879 Infusion 0860 UseDensity 0822 QualityofCare 0791 Effi cientUse 0697

Extraction method principal component analysis a One component extracted

Table 835 Factor analysis for dependent constructs

Table 837 Factor analysis for external constructs

1 2 3 4 Info 0875 0003 0033 0105 UserInterface 0839 0050 0041 minus0024 Mobility 0795 0036 0096 0103 SupportQuality 0789 0049 minus0041 0108 Flexibility 0765 0226 0106 minus0075 Security 0738 minus0058 0230 0102 TTF 0702 0203 0308 minus0088 SelfConfi dence 0607 minus0201 minus0013 0302 FuncXMed 0203 0630 minus0007 0060 FuncRange 0084 0622 minus0147 0117 IntegrationHW minus0345 0585 minus0038 0169 FuncDose 0045 0577 0218 0090 PrivacyMD 0045 minus0028 0792 minus0032 PrivacyUA 0355 0209 0555 minus0102 KnowledgeShare 0127 minus0341 0550 0456 IntegrationSW 0028 0259 0102 0656 AccessALL 0156 0268 minus0221 0582

Component 1

EoU 0925 Usefulness 0911 Attitude 0883 EoL 0582


Table 836 Factor analysis for intermediary constructs


242

875 5 Regression Results

Table 838 All regression analysis

EN Dependent variable

Independent variables B

Standardized beta Signifi cance R 2 Adj R 2

11 Quality of care (Constant) 009 0659 0613 0605 Usefulness 059 052 0000 FuncDose 021 012 0003 Attitude 023 020 0005 Flexibility 014 014 0012 EoL minus009 minus011 0019

12 Quality of care (Constant) 027 0659 0596 0592 Usefulness 068 052 0000 EoL minus011 012 0003 Attitude 028 020 0005

13 Quality of care (Constant) 005 0786 0578 0575 Usefulness 063 055 0000 Attitude 027 024 0000

21 Effi cient use (Constant) minus020 0697 0542 0529 TTF 057 027 0000 UserInterface 079 028 0000 AccessALL 068 014 0002 FuncXMed 033 009 0049 Info 039 017 0009 IntegrationSW 039 011 0018 FuncDose 034 009 0044

22 Effi cient use (Constant) 181 0000 0354 0347 EoU 115 047 0000 Usefulness 079 032 0001 Attitude minus047 minus019 0027

23 Effi cient use (Constant) 260 0000 0270 0267 Usefulness 129 052 0000

24 Effi cient use (Constant) 154 0002 0343 0339 EoU 105 043 0000 Usefulness 047 019 0012

25 Effi cient use (Constant) 353 0000 0169 0167 Attitude 100 041 0000

31 Diffusion (Constant) 010 0611 0572 0569 Usefulness 067 054 0000 Attitude 029 024 0001

32 Diffusion (Constant) 010 0611 0572 0569 Usefulness 067 054 0000

(continued)

OM Koumlk et al

243




Attitude 029 024 0001 41 Infusion (Constant) minus024 0346 0464 0460

Usefulness 069 049 0000 EoU 031 022 0001

Infusion (Constant) 007 0765 0444 0442 Usefulness 093 067 0000

42 Infusion (Constant) 051 0062 0326 0324 Attitude 079 057 0000

51 Use density (Constant) 055 0013 0468 0464 EoU 045 037 0000 Usefulness 043 036 0000

52 Use density (Constant) 085 0000 0421 0417 Usefulness 062 051 0000 Attitude 019 016 0044

61 Satisfaction (Constant) minus086 0000 0827 0822 EoU 028 023 0000 Usefulness 036 028 0000 TTF 022 020 0000 UserInterface 031 021 0000 SupportQuality 010 011 0004 IntegrationHW minus005 minus008 0006

62 Satisfaction (Constant) minus043 0009 0712 0710 EoU 056 045 0000 Usefulness 054 044 0000


64 Satisfaction (Constant) 052 0014 0480 0478 Attitude 084 069 0000

71 Attitude (Constant) 056 0000 0742 0737 Usefulness 073 072 0000 EoU 027 027 0000 PrivacyUA minus007 minus011 0002 PrivacyMD 007 010 0003 TTF minus011 minus012 0006

72 Attitude (Constant) 056 0000 0740 0735 Usefulness 069 067 0000 EoU 030 030 0000 PrivacyUA minus009 minus014 0000 PrivacyMD 007 010 0003 EoL minus008 minus010 0021


(continued)


244




73 Attitude (Constant) 061 0000 0717 0714 Usefulness 067 066 0000 EoU 026 026 0000 EoL minus006 minus008 0032

74 Attitude (Constant) 056 0000 0712 0710 Usefulness 069 068 0000 EoU 020 020 0000

81 Usefulness (Constant) 015 0311 0772 0764 Info 027 028 0000 EoU 028 028 0000 Flexibility 013 015 0001 Mobility 010 013 0004 EoL minus011 minus014 0000 SelfConfi dence 010 013 0001 UserInterface 018 015 0007 FuncDose 012 008 0014

82 Usefulness (Constant) 011 0464 0759 0752 Info 028 030 0000 EoU 019 020 0002 Flexibility 012 014 0002 Mobility 011 014 0003 SelfConfi dence 009 011 0006 UserInterface 017 015 0010 FuncDose 011 007 0027

83 Usefulness (Constant) 085 0000 0615 0613 EoU 085 086 0000 EoL minus010 minus015 0001


85 Usefulness (Constant) 016 0290 0770 0763 Info 027 028 0000 EoU 027 028 0000 Flexibility 013 015 0001

(continued)


OM Koumlk et al

245




Mobility 010 013 0004 EoL minus011 minus014 0001 SelfConfi dence 010 013 0001 UserInterface 018 015 0007 FuncDose 012 008 0013


91 EoU (Constant) 015 0296 0772 0769 UserInterface 047 039 0000 Info 026 027 0000 EoL 018 024 0000 Mobility 013 016 0000

92 EoU (Constant) 259 0000 0306 0303 EoL 038 055 0000




References

Aggelidis V P amp Chatzoglou P D (2009) Using a modifi ed Technology Acceptance Model in hospitals International Journal of Medical Informatics 78 115ndash126

Ajzen I amp Fishbein M (1980) Understanding attitudes and predicting social behavior Englewood Cliffs NJ Prentice Hall

Ajzen Icek (1991) ldquoThe theory of planned behaviorrdquo Organizational Behavior and Human Decision Processes 50(2) 179ndash211

Al-Qirim N (2007) Championing telemedicine adoption and utilizations in healthcare organiza-tions in New Zealand International Journal of Medical Informatics 76 42ndash54


246

Basoglu N Daim T U Atesok H C amp Pamuk M (2010) Exploring the impact of information technology on health information-seeking behaviour International Journal of Business Information Systems 5 (3) 291ndash308

Behkami A N amp Daim T U (2012) Research Forecasting for Health Information Technology (HIT) using technology intelligence Technological Forecasting amp Social Change 79 498ndash508

Bergman M J (2007) Integrating patient questionnaire data into electronic medical records Best Practice amp Research Clinical Rheumatology 21 (4) 649ndash652

Bernstein K Bruun-Rasmussen M Vingtoft S Andersen S K amp Nohr C (2005) Modelling and implementing electronic health records in Denmark International Journal of Medical Informatics 74 213ndash220

Blazona B amp Koncar M (2007) HL7 and DICOM based integration of radiology departments with healthcare enterprise information systems International Journal of Medical Informatics 76S S425ndashS432

Blobel B (2006) Advanced and secure architectural EHR approaches International Journal of Medical Informatics 75 185ndash190

Blue J amp Tan J (2010) Health management strategic information system planninginformation requirements (pp 95ndash108) London Jones and Bartlet Publishers

Brender J Nohr C amp McNair P (2000) Research needs and priorities in Health Informatics International Journal of Medical Informatics 58ndash59 257ndash289

Brown P J B amp Warmington V (2002) Data quality probesmdashExploiting and improving the quality of electronic patient record data and patient care International Journal of Medical Informatics 68 91ndash98

Cayir S (2010) Development of a task information fi t model A study of relationship between task information and individual performance Unpublished masterrsquos thesis Bogazici University Istanbul Turkey

Cho I Kim J Kim J H Kim H Y amp Kim Y (2010) Design and implementation of a standards- based interoperable clinical decision support architecture in the context of the Korean EHR International Journal of Medical Informatics 79 611ndash622

Collins B amp Wagner M (2005) Early experiences in using computerized patient record data for monitoring charting compliance International Journal of Medical Informatics 74 917ndash925

Daim T U Basoglu N amp Tan J (2010) Health management information system innovation Managing innovation diffusion in healthcare services organizations (pp 95ndash108) London Jones and Bartlet Publishers

Davis F D (1989) Perceived usefulness perceived ease of use and user acceptance of informa-tion technology MIS Quarterly 13 (3) 319ndash340

Davis F D Jr (1985) A technology acceptance model for empirically testing new end-user systems theory and results Unpublished doctoral dissertation Massachusetts Institute of Technology

DeLone W H and McLean ER (1992) Information systems success the quest for the depen-dent variable Information Systems Research 3(1) 60ndash95

De-Meyer F Lundgren P-A De Moor G amp Fiers T (1998) Determination of user require-ments for the secure communication of electronic medical information International Journal of Medical Informatics 49 125ndash130

Dishaw M T amp Strong D M (1999) Extending the technology acceptance model with task- technology fi t constructs Information and Management A 36 9ndash21

Dobbing C (2001) Paperless practicemdashElectronic medical records at island health Computer Methods and Programs in Biomedicine 64 197ndash199

Dosswell J T Gibbs S R amp Duncanson K M (2010) Community health information net-works building virtual communities and networking health provider organizations In J Tan amp F C Payton (Eds) Adaptive health management information systems (pp 95ndash108) London Jones and Bartlet Publishers

Edwards P J Moloney K P Jacko J A amp Franccedilois S (2008) Evaluating usability of a com-mercial electronic health record A case study International Journal of Human-Computer Studies 66 718ndash728

OM Koumlk et al

247

Euromonitor (2012) Euromonitor 01042012 httpwwweuromonitorcom Estebaranz J L L amp Castellano C V (2009) Electronic medical history Experience in a der-

matology department Actas Dermosifi liogr 100 374ndash385 Fishbein M amp Ajzen I (1975) Belief attitude intention and behavior An introduction to theory

and research Reading MA Addison and Wesley Gagnon M-P Godin G Gagne C Fortin J-P Lamothe L Reinharz D et al (2003) An

adaptation of theory of interpersonal behaviour to the study of telemedicine adoption by physi-cians International Journal of Medical Informatics 71 103ndash115

Gonzalez-Heydrich J DeMaso D R Irwin C Steingard R J Kohane I S amp Beardslee W R (2000) Implementation of an electronic medical record system in a pediatric psycho-pharmacology program International Journal of Medical Informatics 57 109ndash116

Greenshup H (2012) Physician perspective about health information technology Deloitte Center for Health Solutions

Haas S Wohlgemuth S Echizen I Sonehara N amp Muumlller G (2010) Aspects of privacy for electronic health records International Journal of Medical Informatics 80 (2) e26ndash31

Hannan T (1999) Variation in health caremdashThe roles of electronic medical record International Journal of Medical Informatics 54 127ndash136

Haux R (2010) Medical informatics Past present and future International Journal of Medical Informatics 79 599ndash610

Hayrinen K Saranto K Nykanen P (2008) Defi nition structure content use and impacts of elec-tronic health records A review of the research literature International Journal of Medical Informatics 77(5)291ndash304

Helleso R amp Lorensen M (2005) Inter-organizational continuity of care and the electronic patient record A concept development International Journal of Nursing Studies 42 807ndash822

Holden R J amp Karsh B (2010) The technology acceptance model Its past and its future in healthcare Journal of Biomedical Informatics 43 159ndash172

Holbrook A Keshavjee K Troyan S Pray M amp Ford P T (2003) Applying methodology to electronic medical record selection International Journal of Medical Informatics 70 43ndash50

Hyun S Johnson S B Stetson P D amp Bakken S (2009) Development and evaluation of nursing user interface screens using multiple methods Journal of Biomedical Informatics 42 1004ndash1012

Iakovidis I (1998) Towards personal health record Current situation obstacles and trends in implementation of electronic healthcare record in Europe International Journal of Medical Informatics 52 105ndash115

International Standards Organization (2005) Health informaticsmdashElectronic health recordmdashDefi nition scope and context

Jahanbakhsh M Tavakoli N amp Mokhtari H (2011) Challenges of EHR implementation and related guidelines in Isfahan Procedia Computer Science 3 1199ndash1204

Jha A Doolan D Grandt D Scott T amp Bates D W (2008) The use of health information technology in seven nations International Journal of Medical Informatics 77 848ndash854

Kargin B Basoglu AN Daim TU (2009) Factors Affecting the Adoption of Mobile Services International Journal of Services Sciences 2(1)29ndash52

Kerimoglu O (2006) Organizational adoption of enterprise resource planning systems Unpublished masterrsquos thesis Bogazici University Istanbul Turkey

Kerimoglu O Basoglu N amp Daim T (2008) Organizational adoption of information technolo-gies Case of enterprise resource systems Journal of High Technology Management Research 19 21ndash35

Kierkegaard P (2011) Electronic health record Wiring Europersquos healthcare Computer Law amp Security Review 70 503ndash515

Kijsanayotin B Pannaruthonai S amp Speedie S (2009) Factors infl uencing health information technology adoption in Thailandrsquos community centers Applying the UTAUT model International Journal of Medical Informatics 70 404ndash416


248

Kok O M Basoglu N Daim T (2011) Exploring the success factors of Electronic Health Records adoption Picmet Conference 2011 Portland Oregon

Lenz R amp Kuhn K A (2004) Towards a continuous evolution and adaptation of information systems in healthcare International Journal of Medical Informatics 73 75ndash89

Likourezos A Chalfi n D B Murphy D G Sommer B Darcy K amp Davidson S J (2004) Physician and nurse satisfaction with and electronic medical record system Computer in Emergency Medicine 27 419ndash424

Lluch M (2011) Healthcare professionalsrsquo organisational barriers to health information tech-nologiesmdashA literature review International Journal of Medical Informatics 80 849ndash862

Ludwick D A amp Doucette J (2009) Adopting electronic medical records in primary care Lessons learned from health information systems implementation experience in seven coun-tries International Journal of Medical Informatics 78 22ndash31

Ministry of Health Statistics (2012) Ministry of Health 01042012 wwwsaglikgovtr Natarajan K Stein D Jain S amp Elhadad N (2010) An analysis of clinical queries in an elec-

tronic health record search utility International Journal of Medical Informatics 79 515ndash522 Nowinski C J Becker S M Reynolds K S Beaumont J L Caprini C A Hahn E A et al

(2007) The impact of converting to an electronic health record on organizational culture and quality improvement International Journal of Medical Informatics 76(1)174ndash183

Ovretveit J Scott T Rundall T G Shortell S M amp Brommels M (2007) Implementation of electronic medical record in hospitals Two case studies Health Policy 87 181ndash190

Rose F A Schnipper J L Park E R Poon E G Li Q amp Middleton B (2005) Using quali-tative studies to improve the usability of an EMR Journal of Biomedical Informatics 38 51ndash60

Ross E R Schilling L M Fernald D H Davidson A J amp West D R (2010) Health infor-mation exchange in small-to-medium sized family medicine practices Motivators barriers and potential facilitators of adoption Journal of Medical Informatics 79 123ndash129

Sagiroglu O Y (2006) Implementation diffi culties of health information systems A case study in private hospital in Turkey Unpublished masterrsquos thesis Bogazici University Istanbul Turkey

Saitwal H Xuan F Walji M Patel V amp Zhang J (2010) Assessing performance of an Electronic Health Records (EHR) using cognitive task analysis International Journal of Medical Informatics 79 501ndash506

Safran C amp Goldberg H (2000) Electronic patient records and impact of the internet International Journal of Medical Informatics 60 77ndash83

Shabbir A S Ahmet L A Sudhir R R Scholl J Li Y-C amp Liou D-M (2010) Comparison of documentation time between an electronic and a paper-based record system by optometrists at an eye hospital in south India A timendashmotion study Computer Methods and Programs in BioMedicine 100 283ndash288

Stowe S amp Harding S (2010) Telecare telehealth telemedicine European Geriatric Medicine 1 193ndash197

Tange H J Hasman A Robbe P F amp Schouten H C (1997) Medical narrative in electronic medical records International Journal of Medical Informatics 46 7ndash29

Tanoglu I (2006) Information technology diffusion and managerial decision making Unpublished masterrsquos thesis Bogazici University Istanbul Turkey

Tavakoli N Jahanbakhsh M Mokhtari H amp Tadayon H R (2011) Opportunities of electronic health record implementation in Isfahan Procedia Computer Science 3 1195ndash1198

Topacan U (2009) Exploring the adoption of technology assisted services in the healthcare industry Unpublished masterrsquos thesis Bogazici University Istanbul Turkey

Toussiant P J amp Lodder H (1998) Component based development for supporting workfl ows in hospitals International Journal of Medical Informatics 52 53ndash60

Tung F C amp Chang S C (2008) A new hybrid model for exploring the adoption of online nurs-ing courses Nurse Education Today 28 293ndash300

Turkstat (2010) Turkstat Healthcare Statistics 01032012 httpwwwtuikgovtrPreTablodoalt_id=1095

OM Koumlk et al

249

Turkstat Health Statistics (2012) Turkstat 01032012 httpwwwtuikgovtrjsphbhb_arama_temjspkomut=preAramaampd-5442-p=1

Turkstat Health Statistics (2012) Turkstat 01032012 httpwwwtuikgovtr Ueckert F Maximilian A Goerz M Tessmann S amp Prokosch H U (2003) Empowerment

of patients and communication with health care professionals through an electronic health record International Journal of Medical Informatics 70 99ndash108

Venkatesh V amp Davis F D (2000) A theoretical extension of the technology acceptance model Four longitudinal fi eld studies Management Science 46 (2) 186ndash204

Venkatesh V Morris M G Davis G B amp Davis F (2003) User acceptance of information technology A unifi ed view MIS Quarterly 27 425ndash478

Vesely A Zvarova J Peleska J Buchtela D amp Zdenek A (2006) Medical guidelines presen-tation and comparing with Electronic Health Record International Journal of Medical Informatics 75 240ndash245

Vest J R (2010) More than just a question of technology Factors related to hospitalsrsquo adoption and implementation of health information exchange International Journal of Medical Informatics 79 797ndash806

Wang X Chase H Markatou M Hripcsak G amp Friedman C (2010) Selecting information in electronic health records for knowledge acquisition Journal of Biomedical Informatics 43 595ndash601

Wen H-C Ho Y-S Wen-Shan J Li H-C amp Hsu Y-H E (2007) Scientifi c production of electronic health record research 1991-2005 Computer Methods and Programs in Biomedicine 86 191ndash196

Wright M-O Fisher A John M Reynold K Peterson L R amp Robiscek A (2009) The electronic medical record as a tool for infection surveillance Successful automation of device- days American Journal of Infection Control 37 364ndash370

Yoon D Chang B Kang S W Bae H amp Park R W (2012) Adoption of electronic health record in Korean tertiary teaching and general hospitals International Journal of Medical Informatics 81 53ndash58

Yoshihara H (1998) Development of the electronic health record in Japan International Journal of Medical Informatics 49 53ndash58

Yu P Li H amp Gagnon M-P (2009) Health IT acceptance factors in long-term care facilities A cross-sectional survey International Journal of Medical Informatics 78 219ndash229


Series Foreword13

Preface

Contents

Part I A Dynamic Capabilities Theory-Based Innovation Diffusion Model for Spread of Health Information Technology in the USA





References





231 Care Providers



232 Government


234 Payers


2351 HIT Vendors




251 Barriers and Influences


253 Policy Making





258 Workflow and Knowledge Management



2511 Infusion






References


31 Proposed Model Overview and Justification


33 Diffusion Theory

331 An Innovation


3312 Compatibility

3313 Complexity

3314 Trialability

3315 Observability







344 The Unified Theory of Acceptance and Use of Technology



347 Lazy User Model




3512 Penrose 1959




355 Typology and Classification of Factors of Production


361 Model

362 Diagram

363 View

364 Domain


366 Tool

367 Simulation









376 Unified Modeling Language



377 SysML




392 Grounded Theory and Case Study Method Definitions







3981 Open Coding

3982 Axial Coding


References




421 Significance of the National Healthcare Problem


43 Research Design

431 Overview

432 Objectives





4334 Study Sampling



434 Analysis









4353 Classification of Capabilities

4354 Limitations




4363 Assumptions

4364 Role of Feedback (Fig 419)

4365 Model Verification


Outside Doubters

Walkthroughs

Hypothesis Testing

Tornado Diagram


Conceptual Validity


Believability


4368 Limitations

References




References

Part II Evaluating Electronic Health Record Technology Models and Approaches13Liliya Hogaboam and Tugrul U Daim





References

Chapter 7 Decision Models Regarding Electronic Health Records






721 Criteria








7218 Methodology


References

Part III Adoption Factors of Electronic Health Record Systems


81 Introduction





83 Framework

84 Methodology



843 Pilot Study


85 Findings



8512 User Interface






8518 Functionality








8541 Profile of the Respondents


8543 Descriptives


8545 ANOVA Results



86 Conclusion

861 Limitations

862 Implications

87 Appendices


872 2 Expert Focus Group Questionnaire


874 4 Factor Analysis Results


References


Series Editor Elias G Carayannis George Washington University Washington DC USA

More information about this series at httpwwwspringercomseries8124















v

Series Foreword





vi






Innovationnetworks



Local

Creativemilieus

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Knowledgeclusters






Series Foreword

vii








Series Foreword

viii




Elias G Carayannis

Series Foreword

ix

Pref ace




xi








Contents

xii













Contents

xiii












Contents

xiv













Contents

xv




References 245

Contents






2














4







5











6






technology




7

References




400

300

200

100

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1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

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Policy Making





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10







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231 Care Providers









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232 Government




13



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15










16








(continued)


17









18







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et al 2001 )




29




Perspectives











30


References



















31






















32
























33






















34
























35



























38


+

+

+

+

+

+

Awareness

Awareness


Adoption N


Condition

Capabilities

Capabilities

Capabilities






39







DiffusionAdopters

Confirmation




40


33 Diffusion Theory




41

331 An Innovation





3312 Compatibility


3313 Complexity



42

3314 Trialability


3315 Observability








43









44










perspectives


45




Name



Level of analysis




Information systems

Individual




Social psychology

Individual




Social psychology

Individual




Information systems

Individual





Firmorganization






46







BehavioralIntention

Behavior

Subjective Norm




47




Classic TAM Model

PeroeivedUsefulness




Actual SystemUse

PeroeivedUsefulness




Actual SystemUse



48








Source Ajzen (1991)

PerceivedBehavioral

Control



49






UseBehavior

BehavioralIntention

Voluntarinessof Use

ExperienceAgeGender


EffortExpectancy

SocialInfluence




50


347 Lazy User Model







51









52


3512 Penrose 1959






53








54




































55







56

361 Model


362 Diagram


363 View


364 Domain





57



366 Tool


367 Simulation







58



Qualitative methods






59




(continued)


60









61













62












63

PotentialAdopters

P


N

AdoptionFraction

i

Contact Ratec

MarketSaturation


a


B

B

R

MarketSaturation

Adoption RateAR

Word ofMouth

AdoptersA


Mouth

+

+

+

+ +

+

-

+

+


20

10100

00

0 10 20 30 40 50


60 70 80 90 100





64





65
















66







377 SysML






67









68










research study



69



















70













71








72





design phases






73










74


3981 Open Coding


3982 Axial Coding




75

Subsystem

-Student record

Management system




Users


Student


Classescourses

-Courses Name-

Data Item

-Name-Type


-Database language

-VisualBasic

User Interfaces









76

References




77























future directions


78


























Sociology Press


79

























80



























81
































84







85






Care Management

Care Manager

Technology






4 Field Test

86


43 Research Design

431 Overview


432 Objectives




87







4 Field Test

88









product







89

4334 Study Sampling






See Table 41

Table 41 Sampling


EHR- adoption level








4 Field Test

90

434 Analysis




91





4 Field Test

92






93







Provider

Physician





4 Field Test

94







95


4 Field Test

96






Government

Supplier

Care Seeker

Adoption

Rejection

Dissemenation

DevelopCapabilities

Manage Capabilities

Reconfirmation

Usage

Implementation

Provider

Payer



97




4 Field Test

98





4354 Limitations



NoNo

No


(CMP Software)


(Receive Payments)




AdoptInnovation

RejectInnovation




Yes Yes Yes



99






4 Field Test

100








Technology

Work Flow

CMPSoftware

EHRIntegration


Training


PhysicianTraining






101






Sterman 2000 )





4 Field Test

102
















Total adopters






103

4363 Assumptions



















8



02

4 Field Test

104










PotentialAdopters

Large PracticeLP


SP


N

AdoptionFraction

Contact Ratec

MarketSaturation


a


B

B

B

R

MarketSaturation

Adoption RateLAR

Word ofMouth

AdoptersA


Mouth

Adoption RateSAR

AdoptionFraction

j


$40k

+

+

+

+ +

+

-

+

+

+

i



105




Outside Doubters


Walkthroughs








Correct



4 Field Test

106

Hypothesis Testing


Tornado Diagram






incentive only Pass


practices only Pass



107




Conceptual Validity





ndash20 ndash10 +10 +20260010001600003001

802

Base


4 Field Test

108





Believability





109

Adopters A

4000

4000

1000

00 6 12 18 24 30


36 42 48 54 60

3000

Fig 421 Adopters

Adoption Rates

40

2020040

000

0 6 12 18 24 30

Time (Month)




36 42 48 54 60

80400


Selected Variables

4000

2000500

1000

000

0 6 12 18 24 30




36 42 48 54 60

20001000


4 Field Test

110


4368 Limitations


References













111








4 Field Test









114








115





5 Conclusions

116





117







Technology

Work Flow

CMPSoftware

EHRIntegration


Training


PhysicianTraining





5 Conclusions

118




119


5 Conclusions

120


Government

Supplier

Care Seeker

Adoption

Rejection

Dissemenation

DevelopCapabilities

Manage Capabilities

Reconfirmation

Usage

Implementation

Provider

Payer




121

NoNo

No


(CMP Software)


(Receive Payments)




AdoptInnovation

RejectInnovation




Yes Yes Yes





PotentialAdopters

Large PracticeLP


SP


N

AdoptionFraction

Contact Ratec

MarketSaturation


a


B

B

B

R

MarketSaturation

Adoption RateLAR

Word ofMouth

AdoptersA


Mouth

Adoption RateSAR

AdoptionFraction

j


$40k

+

+

+

+ +

+

-

+

+

+

i


5 Conclusions

122

Adopters A

4000

4000

1000

00 6 12 18 24 30


36 42 48 54 60

3000

Fig 514 Adopters

Adoption Rates

40

2020040

000

0 6 12 18 24 30

Time (Month)




36 42 48 54 60

80400


Selected Variables

4000

2000500

1000

000

0 6 12 18 24 30




36 42 48 54 60

20001000



123











References



5 Conclusions

























128









resources







hardware








records






129










Number of studies



130
















131
















Moores ( 2012 )


(continued)


132




New Zealand





Hong Kong









133










134













135






Feature

Infrastructure type















Easier Harder




Lower Higher


136




















137



















Financial negatives






138













139








locaon










of care






140


























141









142




1





































144

References

















145


















146

















147


















148


















149












152



Subjec13ve norm

Inten13on Behavior






153




i i( ) = +=aring1

b e

(71)


i n( ) = + +=

+aring1

1

b b e

(72)


(73)


(74)


Users Mo13va13on

to Use System

Actual System Use



x1

x2


Atude Toward Using

Actual System Use


User Movaon

Design Features

Cognive Response

Affecve Response

Behavioral Response



154

where








i i( )= +=

aring1 5

b e

(75)


i n( ) = + +=

+aring1 5

1

b b e

(76)


(77)


155

Atude toward the

behavior

Subjec13ve norm

Inten13on Behavior

Perceived

behavioral

control


Perceived

usefulness

Perceived

ease of use

Atude toward

using EHR

Intention to

use EHR

system

EHR system use

Technical

factors

Financial

factors

Subjective

Norm

Interpersonal

Influence

Social

(organizatio

nal) factors

Personal

factors



156

Tabl

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t(G

reen

halg

h et

al

200

9 P

apat

heod

orou

199

0 J

ancz

ewsk

i amp

Shi

20

02)

bull S

ecur

ity

confi

dent

iali

typ

riva

cy

conc

erns

(Alp

er amp

Ols

on 2

010

Ang

st e

t al

20

10 A

sh amp

Bat

es

2005

B

oons

tra

amp B

roek

huis

20

10

Bow

ens

et a

l 2

010

D

uumlnne

beil

et

al

2012

M

orto

n amp

Wie

denb

eck

200

9 P

ilio

uras

et

al

2011

R

ind

amp S

afra

n 1

993

R

osem

ann

et a

l

2010

T

yler

200

1)

bull In

cent

ives

(Ash

amp B

ates

200

5 B

ates

et

al

2003

B

ecke

tt e

t al

20

11

Boo

nstr

a amp

Bro

ekhu

is

2010

C

ress

wel

l amp

She

ikh

201

2

For

d et

al

200

6 G

oldz

wei

g et

al

200

9 G

reen

halg

h et

al

200

9 K

umar

amp A

ldri

ch 2

010

Ros

eman

n et

al

20

10

Sch

oen

et a

l 2

006)

bull P

olic

y dr

awba

cks

and

supp

orts

(And

reacute e

t al

20

08

C

hen

amp H

siao

20

12

Chu

mbl

er e

t al

20

11

Gor

oll

et a

l 2

008

Mil

ler

amp S

im

2004

S

choe

n et

al

200

6 S

imon

et

al

2008

V

ishw

anat

h et

al

200

9 W

itte

r 2

009)

bull T

rans

ienc

e of

ven

dors

(Bat

es e

t al

20

03

For

d et

al

200

6 R

ande

ree

200

7)

bull W

orkfl

ow r

edes

ign

(Boo

nstr

a amp

Bro

ekhu

is 2

010

Bow

ens

et a

l

2010

C

haud

hry

et a

l 2

006

Dix

on e

t al

20

10

Fur

ukaw

a 2

011

G

orol

l et

al

200

8 L

oren

zi e

t al

20

09

Men

ache

mi

amp B

rook

s 2

006

Mil

ler

amp S

im 2

004

Zan

dieh

et

al

2008

Z

arou

kian

20

06)

Per

sona

l

bull A

ge s

peci

alty

pos

itio

n

fam

ilia

rity

(Ang

st e

t al

20

10

Ber

gman

-Eva

ns e

t al

20

08

Che

n amp

Hsi

ao

2012

E

gea

amp G

onza

lez

201

1 H

andy

et

al

2001

Je

ng amp

Tze

ng 2

012

Kim

amp H

an 2

008

Mil

ler

amp S

im 2

004

Mor

ton

amp W

iede

nbec

k 2

010

Pai

amp H

uang

20

11

Pol

ice

et a

l 2

011

Rah

impo

ur e

t al

20

08

Ros

eman

n et

al

201

0 V

ishw

anat

h et

al

200

9 W

u et

al

20

07)

bull M

otiv

atio

n(B

ecke

tt e

t al

20

11

Cre

ssw

ell

amp S

heik

h 2

012

Dix

on 1

999

Fra

mba

ch amp

Sch

ille

wae

rt 2

002

Gre

enha

lgh

et a

l

2009

P

ilio

uras

et

al

2011

W

u et

al

20

07 Y

arbr

ough

amp S

mit

h 2

007

Yu

et a

l 2

009)

bull P

rodu

ctiv

ity

(Bow

ens

et a

l 2

010

D

eLia

et

al

2004

M

orto

n amp

Wie

denb

eck

200

9 Y

oon-

Fla

nner

y et

al

200

8)

bull P

erso

nal

inno

vati

vene

ss(F

ram

bach

amp S

chil

lew

aert

200

2 H

ung

et a

l 2

012

Jen

g amp

Tze

ng

2012

M

oore

s 2

012

Vis

hwan

ath

et a

l 2

009

Y

i et

al

200

6)

bull S

elf-

effi

cacy

(Cha

u amp

Hu

200

2 C

hen

amp H

siao

20

12

Cho

w e

t al

20

12a

201

2b

Cre

ssw

ell

amp S

heik

h 2

012

Dix

on 1

999

Kuk

afka

et

al

200

3 L

egri

s et

al

200

3 M

cFar

land

amp H

amil

ton

200

6 R

ahim

pour

et

al

2008

W

u et

al

20

07

Wu

et a

l

2009

Yu

et a

l 2

009)

bull A

nxie

ty(A

ggel

idis

amp C

hatz

oglo

u 2

009

Che

ng 2

012

Kuk

afka

et

al

2003

L

udw

ick

amp D

ouch

ette

2

009

Sto

rey

amp B

ucha

nan

20

08

Wu

et a

l 2

007

Yar

brou

gh amp

Sm

ith

200

7)

Inte

rper

sona

l(C

hang

201

2 C

hen

amp H

siao

20

12

Chi

asso

n et

al

200

7 D

uumlnne

beil

et

al

2012

F

ram

bach

amp S

chil

lew

aert

20

02

L

iu a

nd M

a 2

005

Wu

et a

l 2

007

Yan

g 2

004

Yar

brou

gh amp

Sm

ith

200

7 Y

u an

d G

agno

n 2

009

Yus

of e

t al

20

08)

bull D

octo

r-do

ctor

bull D

octo

r-nu

rse

bull D

octo

r-pa

tien

t


158


(78)


(79)

where






Impactfactors


doctor-doctor

doctor-nurse

doctor-patient

start-upcosts

ongoingcosts


lack offinancial

resources

information

quality

intensit of

IT utilization

data securty

documentation

technical support

complexity

customization

reliability

interconnectivity

interoperability

hardware issues

accuracy

content

format

timeliness



process orientation

standardization

staff reallocation

employment


incentives



workflow redesign

age

specialty

position

familiarity

motivation

productivity

personalinnovative-

ness

self-efficacy

anxiety



159









160





721 Criteria



Human factors

ergonomics


social psychology


change management

Information systems



161

















Barriers













Other 147 231




162







163






164








165







166







167







168







169






170








171










7218 Methodology




172

















173












174




















175




NETBENEFITS

USERSATISFACTION

INTENTIONTO USE

INFORMATIONQUALITY

SYSTEM QUALITY

SERVICEQUALITY

USE


Structure

Strategy


ManagementProcess




176





Information quality




User satisfaction






177

TECHNOLOGY

HUMAN

ORGANIZATION

SystemQuality

InformationQuality

ServiceQuality

System Use

Net Benefits

Fit

Influence

User Satisfaction

Structure

Environment





(Fig 711)


Technological

Sys

tem

Qua

lity

Info

rmat

ion

Qua

lity

Ser

vice

Qua

lity

Human

Sys

tem

Use

Use

r S

atis

fact

ion

Organizational

Str

uctu

re

Env

ironm

ent

Net BenefitsC

linic

al

Fin

anci

ial



178





References














179


















180



















181


















182

















183


















184



















185




















Record Systems









81 Introduction





190








OM Koumlk et al

191















192





Attitude Toward Act

Subjective Norm



OM Koumlk et al

193







Image

Job Relevance

Output Quality

Subjective Norm








194






Effort Expectancy

Social Influence





System Quality

Information Quality

Use

User Satisfaction

Individual Impact



OM Koumlk et al

195










Information Quality

System Quality

Service Quality

Intention to Use

User Satisfaction

Net Benefits

Use



196




Accesibility

Service Quality

Quality of Sup

Information Qua

Usage Time

Compatibility

Social Influence

Understandibility

Image

Cost

Ease of Use

Usefulness

Attitude

Intention

Self- Efficacy


Image

Subjective Norm

Job Role

Computer Level

Usefulness

Ease of Use



OM Koumlk et al

197












198






Certified EHR



Information Needs

Competition



Control






Control Variables


OM Koumlk et al

199

83 Framework











200






the system use


Subject Article









OM Koumlk et al

201

Tabl

e 8

3 E

xpla

natio

n of

the

cons

truc

ts

Con

stru

ct

Exp

lana

tion

Age

A

ge o

f th

e us

er

Ent

ity ty

pe

The

org

aniz

atio

n th

at th

e pa

rtic

ipan

t is

empl

oyed

at (

eg

hos

pita

l cl

inic

fam

ily h

ealth

cen

ter

Goa

l O

rgan

izat

ionrsquo

s go

al f

or u

sing

the

elec

tron

ic h

ealth

rec

ord

syst

em s

uch

as fi

nanc

ial

med

ical

and

adm

inis

trat

ive

Flex

ibili

ty

Syst

emrsquos

abi

lity

of a

dapt

ing

the

inte

rfac

e an

d w

orkfl

ow

acc

ordi

ng to

use

r re

quir

emen

ts (

Pola

t 20

10)

Bog

azic

i U

nive

rsity

MIS

Dep

artm

ent M

aste

r T

hesi

s U

ser

inte

rfac

e A

ll us

er-f

acin

g gr

aphi

cal i

nter

face

incl

udin

g bu

ttons

men

us o

ptio

ns v

isua

lizat

ion

and

use

r-fr

iend

lines

s Se

curi

ty

The

arc

hite

ctur

e th

at k

eeps

the

reco

rds

from

una

utho

rize

d ac

cess

dat

a lo

ss a

nd d

ata

man

ipul

atio

n (B

lobe

l 20

06 )

Task

ndashtec

hnol

ogy

fi t

Info

rmat

ion

syst

em w

hich

hav

e a

fl exi

ble

wor

kfl o

w a

nd a

cle

ar g

raph

ical

inte

rfac

e ca

n ea

sily

ada

pt to

the

task

s of

an

indi

vidu

al (

Dis

haw

amp S

tron

g 1

999 )

In

tegr

atio

n ha

rdw

are

Syst

emrsquos

inte

grat

ion

capa

bilit

y w

ith m

edic

al d

evic

es s

uch

as u

ltras

ound

lab

equ

ipm

ent

etc

In

tegr

atio

n so

ftw

are

Syst

emrsquos

org

aniz

atio

n ca

pabi

lity

with

oth

er s

oftw

are

syst

ems

such

as

acco

untin

g n

atio

nal i

dent

ity d

atab

ase

an

d in

sura

nce

com

pani

es (

Med

ula

Mer

nis)

Thi

s fu

nctio

nalit

y pr

ovid

es d

ata

cons

iste

ncy

amon

g sy

stem

s an

d al

so s

ave

criti

cal t

ime

for

the

user

s D

ose

func

tiona

lity

(Fun

cDos

e)

Syst

emrsquos

fun

ctio

nalit

y of

kee

ping

dos

e in

form

atio

n re

gard

ing

the

patie

ntrsquos

med

icat

ion

Ran

ge f

unct

iona

lity

(Fun

cRan

ge)

Syst

emrsquos

fun

ctio

nalit

y of

kee

ping

min

imum

max

imum

val

ues

rega

rdin

g th

e te

st r

esul

ts b

lood

val

ues

etc

M

edic

al in

form

atio

n fu

nctio

nalit

y (F

uncX

Med

) Sy

stem

rsquos f

unct

iona

lity

of p

rovi

ding

req

uire

d ad

ditio

nal m

edic

al in

form

atio

n to

the

user

s in

the

case

of

nece

ssity

Acc

essA

LL

U

serrsquo

s ac

cess

to a

ll re

quir

ed in

form

atio

n in

pat

ient

rec

ords

A

ccur

acy

Syst

emrsquos

cap

abili

ty to

hav

e ac

cura

te a

nd s

ensi

tive

info

rmat

ion

(Hay

rine

n et

al

200

8)

Com

plet

enes

s Sy

stem

rsquos c

apab

ility

to h

ave

com

plet

e in

form

atio

n (O

vret

veit

et a

l 2

007 )

U

p-to

-dat

enes

s Sy

stem

rsquos c

apab

ility

to u

pdat

e in

form

atio

n re

gula

rly

(con

tinue

d)


202

Tabl

e 8

3 (c

ontin

ued)

Con

stru

ct

Exp

lana

tion

Stan

dard

izat

ion

Syst

emrsquo f

unct

iona

lity

to k

eep

info

rmat

ion

alig

ned

with

nat

iona

l and

inte

rnat

iona

l sta

ndar

ds (

Yos

hiha

ra 1

998 )

M

obili

ty

Syst

emrsquos

fun

ctio

nalit

y to

off

er u

ser

acce

ssib

ility

fro

m a

nyw

here

at a

ny ti

me

Sys

tem

rsquos d

egre

e to

the

user

rsquos e

ase

of a

cces

s to

the

info

rmat

ion

(Top

acan

200

9 )

Priv

acy

unau

thor

ized

acc

ess

(Pri

vacy

UA

) Sy

stem

rsquos f

unct

iona

lity

to p

reve

nt u

naut

hori

zed

acce

ss b

ut le

tting

aut

hori

zed

user

s to

acc

ess

requ

ired

info

rmat

ion

(Dob

bing

200

1 )

Med

ical

info

rmat

ion

shar

ing

(Pri

vacy

MD

) U

serrsquo

s at

titud

e to

pat

ient

info

rmat

ion

bein

g se

en b

y ot

her

care

take

rs

Kno

wle

dge

shar

ing

Use

rrsquos

attit

ude

to s

hare

med

ical

info

rmat

ion

with

co-

wor

kers

for

con

sulta

tion

(Uec

kert

et a

l 2

003 )

Su

ppor

t qua

lity

The

qua

lity

of th

e su

ppor

t pro

vide

d by

gui

delin

es s

yste

m h

elp

func

tiona

lity

ven

dor

team

and

co-

wor

kers

Se

lf-c

onfi d

ence

In

divi

dual

rsquos o

wn

skill

s ow

n co

mpu

ter

usag

e (T

anog

lu 2

006 )

E

ase

of le

arni

ng

Syst

emrsquos

rat

e on

how

eas

ily it

can

be

lear

ned

(Hol

broo

k et

al

200

3 )

Eas

e of

use

Sy

stem

rsquos r

ate

on h

ow it

can

be

used

with

leas

t eff

ort (

Dav

is 1

989 )

U

sefu

lnes

s Sy

stem

rsquos p

ositi

ve e

ffec

ts o

n th

e en

hanc

ing

indi

vidu

alrsquos

wor

k (D

avis

198

9 )

Atti

tude

In

divi

dual

rsquos p

ositi

ve o

r ne

gativ

e pe

rcep

tion

abou

t the

sys

tem

(Fi

shbe

in amp

Ajz

en 1

975 )

Q

ualit

y of

car

e R

ate

of th

e pr

oduc

tivity

in th

e he

alth

care

ser

vice

s in

clud

ing

num

ber

of s

ucce

ssfu

l tre

atm

ents

num

ber

of

succ

essf

ul d

iagn

osis

etc

(L

udw

ick

amp D

ouce

tte 2

009 )

E

ffi c

ient

use

R

ate

on h

ow th

e in

divi

dual

effi

cie

ntly

use

s th

e sy

stem

D

iffu

sion

R

ate

on h

ow th

e sy

stem

is s

prea

d w

ithin

the

orga

niza

tion

Infu

sion

R

ate

on h

ow th

e in

divi

dual

use

s th

e of

feri

ngs

of th

e sy

stem

U

se d

ensi

ty

Rat

e on

how

foc

used

the

indi

vidu

al u

sed

the

syst

em

Satis

fact

ion

Rat

e on

how

hap

py th

e in

divi

dual

is o

n us

ing

the

syst

em

OM Koumlk et al

203

Tabl

e 8

4 M

ajor

con

stru

cts

and

thei

r lit

erat

ure

Con

stru

ct

Ana

lyze

d lit

erat

ure

Age

Sh

abbi

r et

al

( 201

0 ) V

enka

tesh

et a

l ( 2

003 )

E

ntity

type

Ja

hanb

akhs

h et

al

( 201

1 ) H

elle

so a

nd L

oren

sen

( 200

5 ) S

agir

oglu

( 20

06 )

Iak

ovid

is (

1998

) Se

curi

ty

Uec

kert

et a

l ( 2

003 )

Dob

bing

( 20

01 )

Ovr

etve

it et

al

( 200

7 ) H

olbr

ook

et a

l ( 2

003 )

Haa

s et

al

( 201

0 )

Jaha

nbak

hsh

et a

l ( 2

011 )

Ta

skndasht

echn

olog

y fi t

N

atar

ajan

et a

l ( 2

010 )

Hol

broo

k et

al

( 200

3 ) C

ayir

( 20

10 )

Dis

haw

and

Str

ong

( 199

9 ) H

yun

et a

l ( 2

009 )

Sa

giro

glu

( 200

6 )

Satis

fact

ion

Hay

rine

n et

al

(200

8) D

eLon

e an

d M

cLea

n (1

992

200

3) L

ikou

rezo

s et

al

( 200

4 )

Eas

e of

use

D

avis

( 19

89 )

Ven

kate

sh e

t al

( 200

3 ) Y

u et

al

( 200

9 ) H

olbr

ook

et a

l ( 2

003 )

Sai

twal

et a

l ( 2

010 )

Top

acan

( 20

09 )

Use

fuln

ess

Yu

et a

l ( 2

009 )

Hol

broo

k et

al

( 200

3 ) S

habb

ir e

t al

( 201

0 ) D

avis

( 19

89 )

Ven

kate

sh e

t al

( 200

3 ) V

enka

tesh

and

D

avis

( 20

00 )

Top

acan

( 20

09 )

Atti

tude

Fi

shbe

in a

nd A

jzen

( 19

75 )

Dav

is (

1989

) V

enka

tesh

and

Dav

is (

2000

) T

opac

an (

2009

) E

ase

of le

arni

ng

Hol

broo

k et

al

( 200

3 ) H

ayri

nen

et a

l (2

008)

DeL

one

and

McL

ean

(200

3)

Info

H

ayri

nen

et a

l (2

008)

Yos

hiha

ra (

1998

) O

vret

veit

et a

l ( 2

007 )

Cay

ir (

2010

) B

asog

lu e

t al

(200

9)

Jaha

nbak

hsh

et a

l ( 2

011 )

Wan

g et

al

( 201

0 )

Qua

lity

of c

are

Lud

wic

k an

d D

ouce

tte (

2009

) H

ayri

nen

et a

l (2

008)

Col

lins

and

Wag

ner

( 200

5 ) B

row

n an

d W

arm

ingt

on (

2002

)

Cho

et a

l ( 2

010 )

Tan

ge e

t al

( 199

7 ) D

ossl

er e

t al

(201

0)

Self

-con

fi den

ce

Tano

glu

( 200

6 ) D

avis

( 19

89 )

Yu

et a

l ( 2

009 )

Agg

elid

is a

nd C

hatz

oglo

u ( 2

009 )

Tun

g an

d C

hang

( 20

08 )

Priv

acy

Dob

bing

( 20

01 )

Lud

wic

k an

d D

ouce

tte (

2009

) H

aas

et a

l ( 2

010 )

Saf

ran

and

Gol

derb

erg

(200

0) B

lobe

l ( 20

06 )

Use

r in

terf

ace

Saitw

al e

t al

( 201

0 ) W

ang

et a

l ( 2

010 )

Dob

bing

( 20

01 )

Pol

at (

2010

) B

row

n an

d W

arm

ingt

on (

2002

)


204








(Table 85 )






OM Koumlk et al

205





of use (Table 86 )













206

84 Methodology













OM Koumlk et al

207







843 Pilot Study













208








OM Koumlk et al

209


85 Findings















210





8512 User Interface









OM Koumlk et al

211















212






8518 Functionality







OM Koumlk et al

213














214







chosen











OM Koumlk et al

215

Tabl

e 8

13

Exp

ert f

ocus

stu

dy r

esul

ts

Con

cept

Fr

eque

ncy

Con

cept

Fr

eque

ncy

Con

cept

Fr

eque

ncy

Age

8

Occ

upat

ion

4 G

uide

lines

3

Eas

e of

use

8

Eas

e of

lear

ning

4

Com

pute

r lit

erac

y 3

Com

patib

ility

7

Geo

grap

hic

area

4

Gen

der

2 Sa

tisfa

ctio

n 7

Popu

latio

n se

rvic

ed

4 C

linic

al s

peci

alty

2

Use

fuln

ess

6 H

ospi

tal s

ize

4 Pr

ofes

sion

al s

uppo

rt

2 A

ccur

acy

6 V

endo

r su

ppor

t 4

Tool

exp

erie

nce

2 Se

curi

ty

6 So

cial

infl u

ence

4

Rat

e of

suc

cess

ful t

reat

men

ts

2 Q

ualit

y of

sup

port

5

Func

tiona

l cha

ract

eris

tics

4 H

abit

2 St

anda

rdiz

atio

n 5

Acc

essi

bilit

y 4

Tru

st

2 In

form

atio

n qu

ality

5

Rat

e of

suc

cess

ful d

iagn

osis

4

Mar

ital s

tatu

s 1

Secu

rity

5

Edu

catio

nal l

evel

3

Job

expe

rien

ce

1 Fa

cilit

atin

g co

nditi

ons

5 Ta

skndasht

echn

olog

y fi t

3

Ade

quat

e re

sour

ces

1 Jo

b re

leva

nce

5 R

isk

3 Fl

exib

ility

1

Rat

e of

dec

isio

n ef

fi cie

ncy

5 Pl

ace

of r

esid

ence

3

Beh

avio

ral c

ontr

ol

1 R

espo

nse

time

5 M

anag

eria

l sup

port

3

Com

pute

r ex

peri

ence

1

Use

r in

terf

ace

5 V

olun

tari

ness

3


216











OM Koumlk et al

217













218


Entity type Central

Goal









8543 Descriptives


OM Koumlk et al

219








220







8545 ANOVA Results



OM Koumlk et al

221

Tabl

e 8

21

Reg

ress

ion

resu

lts f

or d

epen

dent

fac

tors

Dep

ende

nt

Inde

pend

ent

Coe

ffi c

ient

bet

a St

anda

rdiz

ed c

oeffi

cie

nt

Sign

ifi ca

nce

R 2

Adj

uste

d R

2

Qua

lity

of c

are

(Con

stan

t)

005

0

786

057

8 0

575

Use

fuln

ess

063

0

55

000

0 A

ttitu

de

027

0

24

000

0 E

ffi c

ient

use

(C

onst

ant)

minus

020

0

697

054

2 0

529

TT

F 0

57

027

0

000

Use

rInt

erfa

ce

079

0

28

000

0 A

cces

sAL

L

068

0

14

000

2 Fu

ncX

Med

0

33

009

0

049

Info

0

39

017

0

009

Inte

grat

ionS

W

039

0

11

001

8 Fu

ncD

ose

034

0

09

004

4 D

iffu

sion

(C

onst

ant)

0

10

061

1 0

572

056

9 U

sefu

lnes

s 0

67

054

0

000

Atti

tude

0

29

024

0

001

Infu

sion

(C

onst

ant)

minus

024

0

346

046

4 0

460

Use

fuln

ess

069

0

49

000

0 E

oU

031

0

22

000

1 U

se d

ensi

ty

(Con

stan

t)

085

0

000

042

1 0

417

Use

fuln

ess

062

0

51

000

0 A

ttitu

de

019

0

16

004

4 Sa

tisfa

ctio

n (C

onst

ant)

minus

043

0

009

071

2 0

710

EoU

0

56

045

0

000

Use

fuln

ess

054

0

44

000

0


222







020

044

045

051

054

055

Diffusion

Infusion

Attitude

Use Density

Satisfaction

Quality of Care

EoU

Usefulness

Use Density

EoL

Self Confidence

Func Dose

User Int

Mobility

Info

Flexibility

p lt 0100 p lt 0010 p lt 0001


OM Koumlk et al

223

027 Efficient Use

FuncXMed

Func Dose

User Interface

TTF

Access All

Info

IntegrationSW

p lt 0100 p lt 0010 p lt 0001















225












226









OM Koumlk et al

227







20 66





228

0123456789

EoU

EoL

Usefulness

Attitude

Satisfaction

QualityofCare

EfficientUse

UseDensity

Diffusion

Infusion

1

2

3



1 2 3 4





OM Koumlk et al

229






1 2 3 4


000

100

200

300

400

500

600Flexibility

Info

AccessALL

KnowledgeShare

Mobility

PrivacyMD

PrivacyUA

Security

SelfConfidence

SupportQuality

TTF

UserInterface

1

2

3

4



230







86 Conclusion





OM Koumlk et al

231

861 Limitations


862 Implications








232




87 Appendices











OM Koumlk et al

233

(con

tinue

d)

Oumlze

llikl

er

Anl

am

Accedilı

klam

a D

emog

raph

ics

Dem

ogra

fi k

Kul

lanı

cını

n de

mog

rafi k

oumlze

llikl

eri

1 A

ge

Yaş

K

ulla

nıcı

nın

yaşı

2

Edu

catio

nal L

evel

E

ğitim

Duumlz

eyi

Kul

lanı

cını

n eğ

itim

duumlz

eyi

3 G

ende

r C

insi

yet

Kul

lanı

cını

n ci

nsiy

eti

4 In

com

e G

elir

K

ulla

nıcı

nın

aylık

gel

iri

5 M

arita

l sta

tus

Evl

ilik

Dur

umu

Kul

lanı

cını

n ev

lilik

dur

umu

6 Jo

b ex

peri

ence

İş

Den

eyim

i K

ulla

nıcı

nın

iş d

eney

imi

7 Pl

ace

of r

esid

ence

İk

amet

Yer

i K

ulla

nıcı

nın

ikam

et y

erin

in ouml

zelli

liği (

koumly

ilccedile

şeh

ir m

erke

zi)

8 O

ccup

atio

n M

esle

k K

ulla

nıcı

nın

mes

leği

In

term

edia

ry

Ara

cı Ouml

zelli

kler

K

ulla

nıcı

yaz

ılım

la e

tkile

şim

e ge

ccediltiğ

i sır

ada

orta

ya ccedil

ıkan

oumlz

ellik

ler

kiş

inin

yaz

ılım

ı kul

land

ığın

da k

azan

dığı

fay

da

kulla

nım

ın k

olay

olm

ası g

ibi

9 E

ase

of u

se

Kol

ay K

ulla

nım

Y

azılı

mın

kol

ay k

ulla

nım

ı 10

U

sefu

lnes

s Fa

yda

Yaz

ılım

ın k

ulla

nım

dan

doğa

n fa

yda

11

Eas

e of

lear

ning

K

olay

Oumlğr

enm

e Y

azılı

mı k

ulla

nmay

ı oumlğr

enm

enin

kol

aylığ

ı C

linic

al v

aria

bles

K

linik

Oumlze

llikl

eri

Has

tane

ile

ilgili

değ

işke

nler

12

G

eogr

aphi

c ar

ea

Coğ

rafi

Kon

um

Has

tane

nin

coğr

afi k

onum

u (ş

ehir

mer

kezi

ilccedil

e k

oumly g

ibi)

13

Po

pula

tion

serv

iced

H

izm

et E

ttiği

Nuumlf

us

Has

tane

nin

hizm

et v

erdi

ği k

işi s

ayıs

ı 14

H

ospi

tal s

ize

Has

taha

ne B

uumlyuumlk

luumlğuuml

H

asta

neni

n fi z

ikse

l buumly

uumlkluuml

ğuuml

15

Oth

er c

linic

al v

aria

bles

D

iğer

Değ

işke

nler

H

asta

ne il

e ilg

ili d

iğer

değ

işke

nler

16

A

dequ

ate

reso

urce

s K

ayna

klar

H

asta

neni

n se

rvis

icin

ayi

rabi

lece

gi k

ayna

klar

17

C

linic

al s

peci

alty

U

zman

lık A

lanı

H

asta

neni

n ge

nel u

zman

lık a

lanı

Su

ppor

t D

este

k Y

azılı

mı k

ulla

nanl

ara

veri

len

tekn

ik d

este

k

87

2 2

Exp

ert F

ocus

Gro

up Q

uest

ionn

aire


234

18

Man

ager

ial s

uppo

rt

Youmln

etim

Des

teği

Y

oumlnet

icile

rin

serv

isin

kul

lanı

lmas

ı iccedili

n ve

rdiğ

i des

tek

19

Peer

sup

port

A

rkad

aş D

este

ği

Yaz

ılım

kul

lanı

mı s

ıras

ında

yaş

ıtlar

ının

dan

veya

ark

adaş

ları

ndan

al

dığı

des

tek

20

Prof

essi

onal

sup

port

Pr

ofes

yone

l Des

tek

Yaz

ılım

kul

lanı

mı s

ıras

ında

pro

fesy

onel

lerd

en a

lınan

des

tek

21

Ven

dor

supp

ort

Satıc

ı Des

teği

Sa

tıcı fi

rm

anın

sağ

ladı

ğı y

ardı

m v

e de

stek

22

Q

ualit

y of

sup

port

D

este

ğin

Kal

itesi

V

erile

n ya

rdım

ve

dest

eğin

kal

itesi

23

So

cial

infl u

ence

So

syal

Etk

enle

r Y

azılı

mı k

ulla

nan

kişi

nin

ccedilevr

esin

deki

lerd

en

aldı

ğı e

tki

24

Com

patib

ility

U

yum

lulu

k Y

azılı

mı ouml

ncek

i suumlr

uumlmle

ri v

eya

ccedilalış

tırıld

ığı o

rtam

daki

diğ

er

sist

emle

re u

yum

u C

onte

nt

Serv

is İ

ccedileri

ği

Yaz

ılım

ın s

undu

ğu b

ilgin

in iccedil

eriğ

i 25

A

ccur

acy

Doğ

rulu

k Su

nula

n bi

lgin

in d

oğru

luğu

26

St

anda

rdiz

atio

n St

anda

rd

Bilg

inin

sta

ndar

t bir

şek

ilde

sunu

lmas

ı 27

In

form

atio

n qu

ality

B

ilgi K

alite

si

Sunu

lan

iccediler

iğin

kal

itesi

28

Se

curi

ty

Bilg

inin

Guumlv

enliğ

i İccedil

eriğ

in b

aşka

ları

nın

eriş

emey

eceğ

i bir

ort

amda

sak

lanm

ası

29

Tool

exp

erie

nce

Den

eyim

K

ulla

nıcı

nın

benz

er s

ervi

s ya

uumlruuml

n ile

ilgi

li ge

ccedilmiş

den

eyim

leri

30

Im

age

İmaj

K

ulla

nıcı

ları

n et

rafl a

rınd

aki i

nsan

lara

ken

dile

rini

far

klı

ayrı

calık

lı ve

oumlnc

uuml gouml

ster

me

iste

ği

31

Satis

fact

ion

Mem

nuni

yet

Kul

lanı

cını

n ya

zılım

dan

mem

nun

kalm

ası

32

Vol

unta

rine

ss

Goumln

uumllluuml

luumlk

Kul

lanı

cını

n yuuml

kuumlm

luumlluuml

ğuuml o

lmad

an is

teye

rek

yazı

lımı k

ulla

nmas

ı 33

Fa

cilit

atin

g co

nditi

ons

Kol

ayla

ştır

ıcı

Koş

ulla

r Y

azılı

mın

kul

lanı

mın

ı kol

ayla

ştır

acak

koş

ulla

r

34

Func

tiona

l cha

ract

eris

tics

Fonk

siyo

nel

Oumlze

llikl

er

Yaz

ılım

ın f

onks

iyon

el ouml

zelli

kler

i

35

Flex

ibili

ty

Kiş

isel

leşt

irile

bilir

lik

Yaz

ılım

ın f

onks

iyon

ları

nı is

teğe

goumlr

e de

ğişt

ireb

ilmek

Oumlrn

eğin

m

enuumln

uumln s

ıras

ı uumlze

rind

e de

ğişi

klik

yap

abilm

esi

Oumlze

llikl

erA

nlam

Accedilı

klam

aD

emog

raph

ics

Dem

ogra

fi kK

ulla

nıcı

nın

dem

ogra

fi k ouml

zelli

kler

i

(con

tinue

d)

OM Koumlk et al

235

36

Acc

essi

bilit

y U

laşa

bilir

lik

Yaz

ılım

ın k

ulla

nıcı

lar

tara

fınd

an k

olay

ula

şala

bilir

olm

ası

37

Beh

avio

ral c

ontr

ol

Kul

lanı

cını

n ya

zılım

ı kul

lanm

ak iccedil

in y

eter

li ye

tene

kler

inin

ka

ynağ

ının

ve

fırs

atın

ın o

lup

olm

adığ

ı alg

ısı

38

Job

rele

vanc

e Iş

e U

ygun

luk

Yaz

ılım

ın d

okto

run

işin

e uy

gunl

uğu

Med

ical

M

edik

al

Yaz

ılım

med

ikal

ala

ndak

i etk

ileri

39

R

ate

of s

ucce

ssfu

l tre

atm

ents

B

aşar

ılı T

edav

ileri

n O

ranı

Y

azılı

mın

kul

lanı

cını

n uy

gula

dığı

teda

vile

rin

oran

ını a

rtır

mas

ı 40

R

ate

of s

ucce

ssfu

l dia

gnos

is

Baş

arılı

Teş

hisl

erin

O

ranı

Y

azılı

mın

kul

lanı

cını

n ko

yduğ

u te

şhis

leri

n or

anın

ı art

ırm

ası

41

Rat

e of

dec

isio

n ef

fi cie

ncy

Kar

ar v

erm

e ve

rim

liliğ

inin

ar

tırılm

ası

Yaz

ılım

ın k

ulla

nıcı

nın

kara

r ve

rme

doğr

uluğ

unu

artır

mas

ı

42

Res

pons

e tim

e Si

stem

in Ccedil

alış

ma

Hız

ı Y

azılı

mın

kul

lanı

m z

aman

ı Y

azılı

mın

kul

lanı

mas

ı ccedilok

zam

an a

labi

lir v

e ku

llanı

cıla

rın

yete

rinc

e va

kti o

lmay

abili

r 43

G

uide

lines

D

oumlkuumlm

anta

syon

Y

azılı

mın

doumlk

uumlman

tasy

onu

44

Hab

it A

lışka

nlık

K

ulla

nıcı

nın

mev

cut a

lışka

nlar

ı 45

T

rust

G

uumlven

ilirl

ik

Kul

lanı

cını

n ya

zılım

a du

yduğ

u guuml

veni

C

ompu

ter

liter

acy

Bilg

isay

ar

Oku

ryaz

arlığ

ı K

ulla

nıcı

nın

bilg

isay

ar b

ilgis

i ve

okur

yaza

rlığ

ı

46

Com

pute

r ex

peri

ence

B

ilgis

ayar

Den

eyim

i K

ulla

nıcı

nın

kaccedil

yıld

ır b

ilgis

ayar

kul

land

ığı

47

Com

pute

r lit

erac

y B

ilgis

ayar

O

kury

azar

lığı

Kul

lanı

cını

n bi

lgis

ayar

kul

lanı

mın

ı ne

kad

ar iy

i bild

iği

48

Use

r in

terf

ace

Ekr

an G

oumlruumln

tuumlsuuml

Y

azılı

mın

kul

lanı

cı e

kran

ları

nın

oumlzel

likle

ri

49

Task

ndashtec

hnol

ogy

fi t

Tekn

oloj

iGoumlr

ev U

ygun

luğu

Y

azılı

mın

kul

lanı

cını

n ya

ptığ

ı goumlr

evle

re u

ygun

luğu

50

R

isk

Ris

k Y

azılı

mın

kul

lanı

lmas

ında

n do

ğabi

lece

k ol

an r

iskl

er

51

Secu

rity

G

uumlven

lik

Yaz

ılım

ın k

ulla

nılm

ası i

le o

luşa

n bi

lgik

ulla

nıcı

has

ta g

uumlven

liği


236



(continued)

OM Koumlk et al

237


(continued)


238

Tabl

e 8

34

Fact

or a

naly

sis

for

all i

tem

s

1 2

3 4

5 6

7 8

9 10

U

sef3

0

822

012

4 0

147

028

1 0

135

001

1 0

104

minus0

096

minus0

116

003

4 Q

oCar

e3

079

4 0

177

014

2 0

240

003

3 0

105

021

1 0

031

minus0

006

005

6 U

sef2

0

793

018

8 0

146

025

3 0

128

minus0

001

011

6 minus

004

6 minus

011

8 0

038

Atti

tude

2 0

793

030

0 0

132

013

2 0

115

005

9 minus

014

6 0

011

001

8 0

098

Atti

tude

1 0

781

024

9 0

209

025

0 0

121

008

5 minus

012

5 minus

001

3 minus

001

1 0

055

Use

f1

077

4 0

233

019

8 0

249

008

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34

(con

tinue

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12

34

56

78

910

OM Koumlk et al

241

Component 1






Component 1





242
















(continued)

OM Koumlk et al

243





Usefulness 069 049 0000 EoU 031 022 0001












(continued)


244











(continued)


OM Koumlk et al

245







92 EoU (Constant) 259 0000 0306 0303 EoL 038 055 0000




References






246






















OM Koumlk et al

247

























248
























OM Koumlk et al

249















Series Foreword13

Preface

Contents






References





231 Care Providers



232 Government


234 Payers


2351 HIT Vendors






253 Policy Making








2511 Infusion






References




33 Diffusion Theory

331 An Innovation


3312 Compatibility

3313 Complexity

3314 Trialability

3315 Observability










347 Lazy User Model




3512 Penrose 1959






361 Model

362 Diagram

363 View

364 Domain


366 Tool

367 Simulation












377 SysML











3981 Open Coding

3982 Axial Coding


References






43 Research Design

431 Overview

432 Objectives





4334 Study Sampling



434 Analysis










4354 Limitations




4363 Assumptions




Outside Doubters

Walkthroughs

Hypothesis Testing

Tornado Diagram


Conceptual Validity


Believability


4368 Limitations

References




References






References







721 Criteria








7218 Methodology


References



81 Introduction





83 Framework

84 Methodology



843 Pilot Study


85 Findings



8512 User Interface






8518 Functionality










8543 Descriptives


8545 ANOVA Results



86 Conclusion

861 Limitations

862 Implications

87 Appendices






References















v

Series Foreword





vi






Innovationnetworks



Local

Creativemilieus

Academicfirm



Knowledgeclusters






Series Foreword

vii








Series Foreword

viii




Elias G Carayannis

Series Foreword

ix

Pref ace




xi








Contents

xii













Contents

xiii












Contents

xiv













Contents

xv




References 245

Contents






2














4







5











6






technology




7

References




400

300

200

100

0

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008



600

700


0 5 10 15 20 25 30



Infusion







Policy Making





8




























10







11

231 Care Providers









12



232 Government




13



234 Payers





14




2351 HIT Vendors




15










16








(continued)


17









18







19







20




253 Policy Making




21








22








23










24








25



2511 Infusion





26










27

Tabl

e 2

2 Su

mm

ary

of L

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ston

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onal

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rabl

e


28





et al 2001 )




29




Perspectives











30


References



















31






















32
























33






















34
























35



























38


+

+

+

+

+

+

Awareness

Awareness


Adoption N


Condition

Capabilities

Capabilities

Capabilities






39







DiffusionAdopters

Confirmation




40


33 Diffusion Theory




41

331 An Innovation





3312 Compatibility


3313 Complexity



42

3314 Trialability


3315 Observability








43









44










perspectives


45




Name



Level of analysis




Information systems

Individual




Social psychology

Individual




Social psychology

Individual




Information systems

Individual





Firmorganization






46







BehavioralIntention

Behavior

Subjective Norm




47




Classic TAM Model

PeroeivedUsefulness




Actual SystemUse

PeroeivedUsefulness




Actual SystemUse



48








Source Ajzen (1991)

PerceivedBehavioral

Control



49






UseBehavior

BehavioralIntention

Voluntarinessof Use

ExperienceAgeGender


EffortExpectancy

SocialInfluence




50


347 Lazy User Model







51









52


3512 Penrose 1959






53








54




































55







56

361 Model


362 Diagram


363 View


364 Domain





57



366 Tool


367 Simulation







58



Qualitative methods






59




(continued)


60









61













62












63

PotentialAdopters

P


N

AdoptionFraction

i

Contact Ratec

MarketSaturation


a


B

B

R

MarketSaturation

Adoption RateAR

Word ofMouth

AdoptersA


Mouth

+

+

+

+ +

+

-

+

+


20

10100

00

0 10 20 30 40 50


60 70 80 90 100





64





65
















66







377 SysML






67









68










research study



69



















70













71








72





design phases






73










74


3981 Open Coding


3982 Axial Coding




75

Subsystem

-Student record

Management system




Users


Student


Classescourses

-Courses Name-

Data Item

-Name-Type


-Database language

-VisualBasic

User Interfaces









76

References




77























future directions


78


























Sociology Press


79

























80



























81
































84







85






Care Management

Care Manager

Technology






4 Field Test

86


43 Research Design

431 Overview


432 Objectives




87







4 Field Test

88









product







89

4334 Study Sampling






See Table 41

Table 41 Sampling


EHR- adoption level








4 Field Test

90

434 Analysis




91





4 Field Test

92






93







Provider

Physician





4 Field Test

94







95


4 Field Test

96






Government

Supplier

Care Seeker

Adoption

Rejection

Dissemenation

DevelopCapabilities

Manage Capabilities

Reconfirmation

Usage

Implementation

Provider

Payer



97




4 Field Test

98





4354 Limitations



NoNo

No


(CMP Software)


(Receive Payments)




AdoptInnovation

RejectInnovation




Yes Yes Yes



99






4 Field Test

100








Technology

Work Flow

CMPSoftware

EHRIntegration


Training


PhysicianTraining






101






Sterman 2000 )





4 Field Test

102
















Total adopters






103

4363 Assumptions



















8



02

4 Field Test

104










PotentialAdopters

Large PracticeLP


SP


N

AdoptionFraction

Contact Ratec

MarketSaturation


a


B

B

B

R

MarketSaturation

Adoption RateLAR

Word ofMouth

AdoptersA


Mouth

Adoption RateSAR

AdoptionFraction

j


$40k

+

+

+

+ +

+

-

+

+

+

i



105




Outside Doubters


Walkthroughs








Correct



4 Field Test

106

Hypothesis Testing


Tornado Diagram






incentive only Pass


practices only Pass



107




Conceptual Validity





ndash20 ndash10 +10 +20260010001600003001

802

Base


4 Field Test

108





Believability





109

Adopters A

4000

4000

1000

00 6 12 18 24 30


36 42 48 54 60

3000

Fig 421 Adopters

Adoption Rates

40

2020040

000

0 6 12 18 24 30

Time (Month)




36 42 48 54 60

80400


Selected Variables

4000

2000500

1000

000

0 6 12 18 24 30




36 42 48 54 60

20001000


4 Field Test

110


4368 Limitations


References













111








4 Field Test









114








115





5 Conclusions

116





117







Technology

Work Flow

CMPSoftware

EHRIntegration


Training


PhysicianTraining





5 Conclusions

118




119


5 Conclusions

120


Government

Supplier

Care Seeker

Adoption

Rejection

Dissemenation

DevelopCapabilities

Manage Capabilities

Reconfirmation

Usage

Implementation

Provider

Payer




121

NoNo

No


(CMP Software)


(Receive Payments)




AdoptInnovation

RejectInnovation




Yes Yes Yes





PotentialAdopters

Large PracticeLP


SP


N

AdoptionFraction

Contact Ratec

MarketSaturation


a


B

B

B

R

MarketSaturation

Adoption RateLAR

Word ofMouth

AdoptersA


Mouth

Adoption RateSAR

AdoptionFraction

j


$40k

+

+

+

+ +

+

-

+

+

+

i


5 Conclusions

122

Adopters A

4000

4000

1000

00 6 12 18 24 30


36 42 48 54 60

3000

Fig 514 Adopters

Adoption Rates

40

2020040

000

0 6 12 18 24 30

Time (Month)




36 42 48 54 60

80400


Selected Variables

4000

2000500

1000

000

0 6 12 18 24 30




36 42 48 54 60

20001000



123











References



5 Conclusions

























128









resources







hardware








records






129










Number of studies



130
















131
















Moores ( 2012 )


(continued)


132




New Zealand





Hong Kong









133










134













135






Feature

Infrastructure type















Easier Harder




Lower Higher


136




















137



















Financial negatives






138













139








locaon










of care






140


























141









142




1





































144

References

















145


















146

















147


















148


















149












152



Subjec13ve norm

Inten13on Behavior






153




i i( ) = +=aring1

b e

(71)


i n( ) = + +=

+aring1

1

b b e

(72)


(73)


(74)


Users Mo13va13on

to Use System

Actual System Use



x1

x2


Atude Toward Using

Actual System Use


User Movaon

Design Features

Cognive Response

Affecve Response

Behavioral Response



154

where








i i( )= +=

aring1 5

b e

(75)


i n( ) = + +=

+aring1 5

1

b b e

(76)


(77)


155

Atude toward the

behavior

Subjec13ve norm

Inten13on Behavior

Perceived

behavioral

control


Perceived

usefulness

Perceived

ease of use

Atude toward

using EHR

Intention to

use EHR

system

EHR system use

Technical

factors

Financial

factors

Subjective

Norm

Interpersonal

Influence

Social

(organizatio

nal) factors

Personal

factors



156

Tabl

e 7

1 E

xten

ded

taxo

nom

y of

ext

erna

l fa

ctor

s

Fin

anci

al

bull S

tart

-up

cost

s(B

oons

tra

amp B

roek

huis

201

0 C

ress

wel

l amp

She

ikh

201

2 F

onky

ch amp

Tay

lor

200

5 M

cGin

n et

al

201

1

Men

ache

mi

amp B

rook

s 2

006

Pal

acio

et

al

2009

S

hoen

amp O

sbor

n 2

006

Sim

on e

t al

20

07

Val

des

et a

l 2

004

Zar

ouki

an

2006

)

bull O

ngoi

ng c

osts

Ash

amp B

ates

200

5 B

oons

tra

amp B

roek

huis

20

10

DeP

hill

ips

200

7 M

arti

ch amp

Cer

vena

k 2

007

Pol

ice

et a

l 2

011

W

itte

r 2

009)

bull F

inan

cial

unc

erta

inti

es (

lack

of

tang

ible

ben

efits

la

ck o

f fi

nanc

ial

retu

rn r

eim

burs

emen

t)

(Blu

men

thal

200

9 C

haud

hry

et a

l 2

006

Gol

dzw

eig

et a

l 2

009

Men

ache

mi

et a

l 2

008)

bull L

ack

of fi

nanc

ial

reso

urce

s (i

n so

me

sour

ces

refe

rred

to

as l

ack

of c

apit

al l

ack

of f

undi

ng e

tc)

(Ash

amp B

ates

200

5 B

oons

tra

amp B

roek

huis

201

0 B

owen

s et

al

201

0 F

onky

ch amp

Tay

lor

200

5 G

orol

l et

al

200

8

Lor

enzi

et

al

2009

P

alac

io e

t al

20

09

Rob

ert

Woo

d Jo

hnso

n F

ound

atio

n 2

010

Shi

elds

et

al

2007

S

imon

et

al

2008

S

hen

amp G

inn

201

2 S

imon

et

al

2007

)

Tec

hnic

al f

acto

rs

bull In

form

atio

n qu

alit

y (a

ccur

acy

co

nten

t f

orm

at t

imel

ines

s)(B

oden

heim

er amp

Gru

mba

ch 2

003

Che

n amp

Hsi

ao

2012

C

ress

wel

l amp

She

ikh

201

2 K

im amp

Cha

ng 2

006

Lia

ng e

t al

20

11

Mor

es 2

012

Wu

et a

l

2007

)

bull In

tens

ity

of I

T u

tili

zati

on

inte

nsit

y of

IT

uti

liza

tion

(da

ta

secu

rity

doc

umen

tati

on te

chni

cal

supp

ort

com

plex

ity

cu

stom

izat

ion

rel

iabi

lity

in

terc

onne

ctiv

ity

int

erop

erab

ilit

y

hard

war

e is

sues

)

(Ang

st e

t al

20

10

Bat

es e

t al

20

03

Blu

men

thal

200

9 B

oons

tra

amp B

roek

huis

20

10

Bow

ens

et a

l

2010

C

hen

et a

l 2

010

Che

n amp

Hsi

ao 2

012

Duumln

nebe

il e

t al

20

12

Gla

ser

et a

l 2

008

Gor

oll

et a

l 2

008

Gre

enha

lgh

et a

l

2009

H

andy

et

al

2001

Ji

an e

t al

20

12

Lor

ence

amp C

hurc

hill

200

5 L

udw

ick

amp D

ouce

tte

200

9 M

enac

hem

i amp

B

rook

s 2

006

Mil

ler

amp S

im 2

004

Ort

ega

Ege

a amp

Rom

aacuten G

onzaacute

lez

201

1

Pal

acio

et

al

2009

P

olic

e et

al

20

11

Rah

impo

ur e

t al

20

08

Rin

d amp

Saf

ran

199

3 R

osem

ann

et a

l 2

010

Rob

ert

Woo

d Jo

hnso

n F

ound

atio

n 2

010

Sim

on

et a

l 2

007

Tsi

knak

is e

t al

20

02

Tyl

er 2

001

Val

des

et a

l 2

004

Ved

vik

et a

l 2

009

Yoo

n-F

lann

ery

et a

l 2

008

Z

hang

amp L

iu 2

010)

Soc

ial

orga

niza

tion

al

bull T

op m

anag

emen

t su

ppor

t(A

ndreacute

et

al

2008

C

hen

amp H

siao

20

12

Kim

amp C

hang

200

6 L

egri

s et

al

200

3 M

orto

n amp

Wie

denb

eck

200

9 Y

usof

et

al

200

8)

bull P

roje

ctt

eam

com

pete

ncy

(Car

ayon

et

al

2011

C

hen

amp H

siao

20

12

Cho

w e

t al

20

12a

201

2b Y

arbr

ough

amp S

mit

h 2

007

Zar

ouki

an

2006

)

bull P

roce

ss o

rien

tati

on(C

hias

son

et a

l 2

007

Duumln

nebe

il e

t al

20

12)

bull S

tand

ardi

zati

on(B

oons

tra

amp B

roek

huis

201

0 C

ress

wel

l amp

She

ikh

201

2 G

lase

r et

al

200

8 G

reen

halg

h et

al

20

09

Hel

ms

amp

Wil

liam

s 2

011

Hol

den

amp K

arsh

201

0 K

azle

y amp

Ozc

an

2008

K

umar

amp A

ldri

ch 2

010

Lan

ham

et

al

2012

L

apin

sky

et a

l 2

008

Leu

et

al

2008

L

oren

zi e

t al

20

09

Lud

wic

k amp

Dou

cett

e 2

009

Mat

ysie

wic

z amp

Sm

ycze

k

2009

R

ande

ree

200

7 T

sikn

akis

et

al

2002

T

yler

200

1 W

agne

r amp

Wei

bel

200

5 Z

arou

kian

200

6)


157

bull S

taff

rea

lloc

atio

nem

ploy

men

t(G

reen

halg

h et

al

200

9 P

apat

heod

orou

199

0 J

ancz

ewsk

i amp

Shi

20

02)

bull S

ecur

ity

confi

dent

iali

typ

riva

cy

conc

erns

(Alp

er amp

Ols

on 2

010

Ang

st e

t al

20

10 A

sh amp

Bat

es

2005

B

oons

tra

amp B

roek

huis

20

10

Bow

ens

et a

l 2

010

D

uumlnne

beil

et

al

2012

M

orto

n amp

Wie

denb

eck

200

9 P

ilio

uras

et

al

2011

R

ind

amp S

afra

n 1

993

R

osem

ann

et a

l

2010

T

yler

200

1)

bull In

cent

ives

(Ash

amp B

ates

200

5 B

ates

et

al

2003

B

ecke

tt e

t al

20

11

Boo

nstr

a amp

Bro

ekhu

is

2010

C

ress

wel

l amp

She

ikh

201

2

For

d et

al

200

6 G

oldz

wei

g et

al

200

9 G

reen

halg

h et

al

200

9 K

umar

amp A

ldri

ch 2

010

Ros

eman

n et

al

20

10

Sch

oen

et a

l 2

006)

bull P

olic

y dr

awba

cks

and

supp

orts

(And

reacute e

t al

20

08

C

hen

amp H

siao

20

12

Chu

mbl

er e

t al

20

11

Gor

oll

et a

l 2

008

Mil

ler

amp S

im

2004

S

choe

n et

al

200

6 S

imon

et

al

2008

V

ishw

anat

h et

al

200

9 W

itte

r 2

009)

bull T

rans

ienc

e of

ven

dors

(Bat

es e

t al

20

03

For

d et

al

200

6 R

ande

ree

200

7)

bull W

orkfl

ow r

edes

ign

(Boo

nstr

a amp

Bro

ekhu

is 2

010

Bow

ens

et a

l

2010

C

haud

hry

et a

l 2

006

Dix

on e

t al

20

10

Fur

ukaw

a 2

011

G

orol

l et

al

200

8 L

oren

zi e

t al

20

09

Men

ache

mi

amp B

rook

s 2

006

Mil

ler

amp S

im 2

004

Zan

dieh

et

al

2008

Z

arou

kian

20

06)

Per

sona

l

bull A

ge s

peci

alty

pos

itio

n

fam

ilia

rity

(Ang

st e

t al

20

10

Ber

gman

-Eva

ns e

t al

20

08

Che

n amp

Hsi

ao

2012

E

gea

amp G

onza

lez

201

1 H

andy

et

al

2001

Je

ng amp

Tze

ng 2

012

Kim

amp H

an 2

008

Mil

ler

amp S

im 2

004

Mor

ton

amp W

iede

nbec

k 2

010

Pai

amp H

uang

20

11

Pol

ice

et a

l 2

011

Rah

impo

ur e

t al

20

08

Ros

eman

n et

al

201

0 V

ishw

anat

h et

al

200

9 W

u et

al

20

07)

bull M

otiv

atio

n(B

ecke

tt e

t al

20

11

Cre

ssw

ell

amp S

heik

h 2

012

Dix

on 1

999

Fra

mba

ch amp

Sch

ille

wae

rt 2

002

Gre

enha

lgh

et a

l

2009

P

ilio

uras

et

al

2011

W

u et

al

20

07 Y

arbr

ough

amp S

mit

h 2

007

Yu

et a

l 2

009)

bull P

rodu

ctiv

ity

(Bow

ens

et a

l 2

010

D

eLia

et

al

2004

M

orto

n amp

Wie

denb

eck

200

9 Y

oon-

Fla

nner

y et

al

200

8)

bull P

erso

nal

inno

vati

vene

ss(F

ram

bach

amp S

chil

lew

aert

200

2 H

ung

et a

l 2

012

Jen

g amp

Tze

ng

2012

M

oore

s 2

012

Vis

hwan

ath

et a

l 2

009

Y

i et

al

200

6)

bull S

elf-

effi

cacy

(Cha

u amp

Hu

200

2 C

hen

amp H

siao

20

12

Cho

w e

t al

20

12a

201

2b

Cre

ssw

ell

amp S

heik

h 2

012

Dix

on 1

999

Kuk

afka

et

al

200

3 L

egri

s et

al

200

3 M

cFar

land

amp H

amil

ton

200

6 R

ahim

pour

et

al

2008

W

u et

al

20

07

Wu

et a

l

2009

Yu

et a

l 2

009)

bull A

nxie

ty(A

ggel

idis

amp C

hatz

oglo

u 2

009

Che

ng 2

012

Kuk

afka

et

al

2003

L

udw

ick

amp D

ouch

ette

2

009

Sto

rey

amp B

ucha

nan

20

08

Wu

et a

l 2

007

Yar

brou

gh amp

Sm

ith

200

7)

Inte

rper

sona

l(C

hang

201

2 C

hen

amp H

siao

20

12

Chi

asso

n et

al

200

7 D

uumlnne

beil

et

al

2012

F

ram

bach

amp S

chil

lew

aert

20

02

L

iu a

nd M

a 2

005

Wu

et a

l 2

007

Yan

g 2

004

Yar

brou

gh amp

Sm

ith

200

7 Y

u an

d G

agno

n 2

009

Yus

of e

t al

20

08)

bull D

octo

r-do

ctor

bull D

octo

r-nu

rse

bull D

octo

r-pa

tien

t


158


(78)


(79)

where






Impactfactors


doctor-doctor

doctor-nurse

doctor-patient

start-upcosts

ongoingcosts


lack offinancial

resources

information

quality

intensit of

IT utilization

data securty

documentation

technical support

complexity

customization

reliability

interconnectivity

interoperability

hardware issues

accuracy

content

format

timeliness



process orientation

standardization

staff reallocation

employment


incentives



workflow redesign

age

specialty

position

familiarity

motivation

productivity

personalinnovative-

ness

self-efficacy

anxiety



159









160





721 Criteria



Human factors

ergonomics


social psychology


change management

Information systems



161

















Barriers













Other 147 231




162







163






164








165







166







167







168







169






170








171










7218 Methodology




172

















173












174




















175




NETBENEFITS

USERSATISFACTION

INTENTIONTO USE

INFORMATIONQUALITY

SYSTEM QUALITY

SERVICEQUALITY

USE


Structure

Strategy


ManagementProcess




176





Information quality




User satisfaction






177

TECHNOLOGY

HUMAN

ORGANIZATION

SystemQuality

InformationQuality

ServiceQuality

System Use

Net Benefits

Fit

Influence

User Satisfaction

Structure

Environment





(Fig 711)


Technological

Sys

tem

Qua

lity

Info

rmat

ion

Qua

lity

Ser

vice

Qua

lity

Human

Sys

tem

Use

Use

r S

atis

fact

ion

Organizational

Str

uctu

re

Env

ironm

ent

Net BenefitsC

linic

al

Fin

anci

ial



178





References














179


















180



















181


















182

















183


















184



















185




















Record Systems









81 Introduction





190








OM Koumlk et al

191















192





Attitude Toward Act

Subjective Norm



OM Koumlk et al

193







Image

Job Relevance

Output Quality

Subjective Norm








194






Effort Expectancy

Social Influence





System Quality

Information Quality

Use

User Satisfaction

Individual Impact



OM Koumlk et al

195










Information Quality

System Quality

Service Quality

Intention to Use

User Satisfaction

Net Benefits

Use



196




Accesibility

Service Quality

Quality of Sup

Information Qua

Usage Time

Compatibility

Social Influence

Understandibility

Image

Cost

Ease of Use

Usefulness

Attitude

Intention

Self- Efficacy


Image

Subjective Norm

Job Role

Computer Level

Usefulness

Ease of Use



OM Koumlk et al

197












198






Certified EHR



Information Needs

Competition



Control






Control Variables


OM Koumlk et al

199

83 Framework











200






the system use


Subject Article









OM Koumlk et al

201

Tabl

e 8

3 E

xpla

natio

n of

the

cons

truc

ts

Con

stru

ct

Exp

lana

tion

Age

A

ge o

f th

e us

er

Ent

ity ty

pe

The

org

aniz

atio

n th

at th

e pa

rtic

ipan

t is

empl

oyed

at (

eg

hos

pita

l cl

inic

fam

ily h

ealth

cen

ter

Goa

l O

rgan

izat

ionrsquo

s go

al f

or u

sing

the

elec

tron

ic h

ealth

rec

ord

syst

em s

uch

as fi

nanc

ial

med

ical

and

adm

inis

trat

ive

Flex

ibili

ty

Syst

emrsquos

abi

lity

of a

dapt

ing

the

inte

rfac

e an

d w

orkfl

ow

acc

ordi

ng to

use

r re

quir

emen

ts (

Pola

t 20

10)

Bog

azic

i U

nive

rsity

MIS

Dep

artm

ent M

aste

r T

hesi

s U

ser

inte

rfac

e A

ll us

er-f

acin

g gr

aphi

cal i

nter

face

incl

udin

g bu

ttons

men

us o

ptio

ns v

isua

lizat

ion

and

use

r-fr

iend

lines

s Se

curi

ty

The

arc

hite

ctur

e th

at k

eeps

the

reco

rds

from

una

utho

rize

d ac

cess

dat

a lo

ss a

nd d

ata

man

ipul

atio

n (B

lobe

l 20

06 )

Task

ndashtec

hnol

ogy

fi t

Info

rmat

ion

syst

em w

hich

hav

e a

fl exi

ble

wor

kfl o

w a

nd a

cle

ar g

raph

ical

inte

rfac

e ca

n ea

sily

ada

pt to

the

task

s of

an

indi

vidu

al (

Dis

haw

amp S

tron

g 1

999 )

In

tegr

atio

n ha

rdw

are

Syst

emrsquos

inte

grat

ion

capa

bilit

y w

ith m

edic

al d

evic

es s

uch

as u

ltras

ound

lab

equ

ipm

ent

etc

In

tegr

atio

n so

ftw

are

Syst

emrsquos

org

aniz

atio

n ca

pabi

lity

with

oth

er s

oftw

are

syst

ems

such

as

acco

untin

g n

atio

nal i

dent

ity d

atab

ase

an

d in

sura

nce

com

pani

es (

Med

ula

Mer

nis)

Thi

s fu

nctio

nalit

y pr

ovid

es d

ata

cons

iste

ncy

amon

g sy

stem

s an

d al

so s

ave

criti

cal t

ime

for

the

user

s D

ose

func

tiona

lity

(Fun

cDos

e)

Syst

emrsquos

fun

ctio

nalit

y of

kee

ping

dos

e in

form

atio

n re

gard

ing

the

patie

ntrsquos

med

icat

ion

Ran

ge f

unct

iona

lity

(Fun

cRan

ge)

Syst

emrsquos

fun

ctio

nalit

y of

kee

ping

min

imum

max

imum

val

ues

rega

rdin

g th

e te

st r

esul

ts b

lood

val

ues

etc

M

edic

al in

form

atio

n fu

nctio

nalit

y (F

uncX

Med

) Sy

stem

rsquos f

unct

iona

lity

of p

rovi

ding

req

uire

d ad

ditio

nal m

edic

al in

form

atio

n to

the

user

s in

the

case

of

nece

ssity

Acc

essA

LL

U

serrsquo

s ac

cess

to a

ll re

quir

ed in

form

atio

n in

pat

ient

rec

ords

A

ccur

acy

Syst

emrsquos

cap

abili

ty to

hav

e ac

cura

te a

nd s

ensi

tive

info

rmat

ion

(Hay

rine

n et

al

200

8)

Com

plet

enes

s Sy

stem

rsquos c

apab

ility

to h

ave

com

plet

e in

form

atio

n (O

vret

veit

et a

l 2

007 )

U

p-to

-dat

enes

s Sy

stem

rsquos c

apab

ility

to u

pdat

e in

form

atio

n re

gula

rly

(con

tinue

d)


202

Tabl

e 8

3 (c

ontin

ued)

Con

stru

ct

Exp

lana

tion

Stan

dard

izat

ion

Syst

emrsquo f

unct

iona

lity

to k

eep

info

rmat

ion

alig

ned

with

nat

iona

l and

inte

rnat

iona

l sta

ndar

ds (

Yos

hiha

ra 1

998 )

M

obili

ty

Syst

emrsquos

fun

ctio

nalit

y to

off

er u

ser

acce

ssib

ility

fro

m a

nyw

here

at a

ny ti

me

Sys

tem

rsquos d

egre

e to

the

user

rsquos e

ase

of a

cces

s to

the

info

rmat

ion

(Top

acan

200

9 )

Priv

acy

unau

thor

ized

acc

ess

(Pri

vacy

UA

) Sy

stem

rsquos f

unct

iona

lity

to p

reve

nt u

naut

hori

zed

acce

ss b

ut le

tting

aut

hori

zed

user

s to

acc

ess

requ

ired

info

rmat

ion

(Dob

bing

200

1 )

Med

ical

info

rmat

ion

shar

ing

(Pri

vacy

MD

) U

serrsquo

s at

titud

e to

pat

ient

info

rmat

ion

bein

g se

en b

y ot

her

care

take

rs

Kno

wle

dge

shar

ing

Use

rrsquos

attit

ude

to s

hare

med

ical

info

rmat

ion

with

co-

wor

kers

for

con

sulta

tion

(Uec

kert

et a

l 2

003 )

Su

ppor

t qua

lity

The

qua

lity

of th

e su

ppor

t pro

vide

d by

gui

delin

es s

yste

m h

elp

func

tiona

lity

ven

dor

team

and

co-

wor

kers

Se

lf-c

onfi d

ence

In

divi

dual

rsquos o

wn

skill

s ow

n co

mpu

ter

usag

e (T

anog

lu 2

006 )

E

ase

of le

arni

ng

Syst

emrsquos

rat

e on

how

eas

ily it

can

be

lear

ned

(Hol

broo

k et

al

200

3 )

Eas

e of

use

Sy

stem

rsquos r

ate

on h

ow it

can

be

used

with

leas

t eff

ort (

Dav

is 1

989 )

U

sefu

lnes

s Sy

stem

rsquos p

ositi

ve e

ffec

ts o

n th

e en

hanc

ing

indi

vidu

alrsquos

wor

k (D

avis

198

9 )

Atti

tude

In

divi

dual

rsquos p

ositi

ve o

r ne

gativ

e pe

rcep

tion

abou

t the

sys

tem

(Fi

shbe

in amp

Ajz

en 1

975 )

Q

ualit

y of

car

e R

ate

of th

e pr

oduc

tivity

in th

e he

alth

care

ser

vice

s in

clud

ing

num

ber

of s

ucce

ssfu

l tre

atm

ents

num

ber

of

succ

essf

ul d

iagn

osis

etc

(L

udw

ick

amp D

ouce

tte 2

009 )

E

ffi c

ient

use

R

ate

on h

ow th

e in

divi

dual

effi

cie

ntly

use

s th

e sy

stem

D

iffu

sion

R

ate

on h

ow th

e sy

stem

is s

prea

d w

ithin

the

orga

niza

tion

Infu

sion

R

ate

on h

ow th

e in

divi

dual

use

s th

e of

feri

ngs

of th

e sy

stem

U

se d

ensi

ty

Rat

e on

how

foc

used

the

indi

vidu

al u

sed

the

syst

em

Satis

fact

ion

Rat

e on

how

hap

py th

e in

divi

dual

is o

n us

ing

the

syst

em

OM Koumlk et al

203

Tabl

e 8

4 M

ajor

con

stru

cts

and

thei

r lit

erat

ure

Con

stru

ct

Ana

lyze

d lit

erat

ure

Age

Sh

abbi

r et

al

( 201

0 ) V

enka

tesh

et a

l ( 2

003 )

E

ntity

type

Ja

hanb

akhs

h et

al

( 201

1 ) H

elle

so a

nd L

oren

sen

( 200

5 ) S

agir

oglu

( 20

06 )

Iak

ovid

is (

1998

) Se

curi

ty

Uec

kert

et a

l ( 2

003 )

Dob

bing

( 20

01 )

Ovr

etve

it et

al

( 200

7 ) H

olbr

ook

et a

l ( 2

003 )

Haa

s et

al

( 201

0 )

Jaha

nbak

hsh

et a

l ( 2

011 )

Ta

skndasht

echn

olog

y fi t

N

atar

ajan

et a

l ( 2

010 )

Hol

broo

k et

al

( 200

3 ) C

ayir

( 20

10 )

Dis

haw

and

Str

ong

( 199

9 ) H

yun

et a

l ( 2

009 )

Sa

giro

glu

( 200

6 )

Satis

fact

ion

Hay

rine

n et

al

(200

8) D

eLon

e an

d M

cLea

n (1

992

200

3) L

ikou

rezo

s et

al

( 200

4 )

Eas

e of

use

D

avis

( 19

89 )

Ven

kate

sh e

t al

( 200

3 ) Y

u et

al

( 200

9 ) H

olbr

ook

et a

l ( 2

003 )

Sai

twal

et a

l ( 2

010 )

Top

acan

( 20

09 )

Use

fuln

ess

Yu

et a

l ( 2

009 )

Hol

broo

k et

al

( 200

3 ) S

habb

ir e

t al

( 201

0 ) D

avis

( 19

89 )

Ven

kate

sh e

t al

( 200

3 ) V

enka

tesh

and

D

avis

( 20

00 )

Top

acan

( 20

09 )

Atti

tude

Fi

shbe

in a

nd A

jzen

( 19

75 )

Dav

is (

1989

) V

enka

tesh

and

Dav

is (

2000

) T

opac

an (

2009

) E

ase

of le

arni

ng

Hol

broo

k et

al

( 200

3 ) H

ayri

nen

et a

l (2

008)

DeL

one

and

McL

ean

(200

3)

Info

H

ayri

nen

et a

l (2

008)

Yos

hiha

ra (

1998

) O

vret

veit

et a

l ( 2

007 )

Cay

ir (

2010

) B

asog

lu e

t al

(200

9)

Jaha

nbak

hsh

et a

l ( 2

011 )

Wan

g et

al

( 201

0 )

Qua

lity

of c

are

Lud

wic

k an

d D

ouce

tte (

2009

) H

ayri

nen

et a

l (2

008)

Col

lins

and

Wag

ner

( 200

5 ) B

row

n an

d W

arm

ingt

on (

2002

)

Cho

et a

l ( 2

010 )

Tan

ge e

t al

( 199

7 ) D

ossl

er e

t al

(201

0)

Self

-con

fi den

ce

Tano

glu

( 200

6 ) D

avis

( 19

89 )

Yu

et a

l ( 2

009 )

Agg

elid

is a

nd C

hatz

oglo

u ( 2

009 )

Tun

g an

d C

hang

( 20

08 )

Priv

acy

Dob

bing

( 20

01 )

Lud

wic

k an

d D

ouce

tte (

2009

) H

aas

et a

l ( 2

010 )

Saf

ran

and

Gol

derb

erg

(200

0) B

lobe

l ( 20

06 )

Use

r in

terf

ace

Saitw

al e

t al

( 201

0 ) W

ang

et a

l ( 2

010 )

Dob

bing

( 20

01 )

Pol

at (

2010

) B

row

n an

d W

arm

ingt

on (

2002

)


204








(Table 85 )






OM Koumlk et al

205





of use (Table 86 )













206

84 Methodology













OM Koumlk et al

207







843 Pilot Study













208








OM Koumlk et al

209


85 Findings















210





8512 User Interface









OM Koumlk et al

211















212






8518 Functionality







OM Koumlk et al

213














214







chosen











OM Koumlk et al

215

Tabl

e 8

13

Exp

ert f

ocus

stu

dy r

esul

ts

Con

cept

Fr

eque

ncy

Con

cept

Fr

eque

ncy

Con

cept

Fr

eque

ncy

Age

8

Occ

upat

ion

4 G

uide

lines

3

Eas

e of

use

8

Eas

e of

lear

ning

4

Com

pute

r lit

erac

y 3

Com

patib

ility

7

Geo

grap

hic

area

4

Gen

der

2 Sa

tisfa

ctio

n 7

Popu

latio

n se

rvic

ed

4 C

linic

al s

peci

alty

2

Use

fuln

ess

6 H

ospi

tal s

ize

4 Pr

ofes

sion

al s

uppo

rt

2 A

ccur

acy

6 V

endo

r su

ppor

t 4

Tool

exp

erie

nce

2 Se

curi

ty

6 So

cial

infl u

ence

4

Rat

e of

suc

cess

ful t

reat

men

ts

2 Q

ualit

y of

sup

port

5

Func

tiona

l cha

ract

eris

tics

4 H

abit

2 St

anda

rdiz

atio

n 5

Acc

essi

bilit

y 4

Tru

st

2 In

form

atio

n qu

ality

5

Rat

e of

suc

cess

ful d

iagn

osis

4

Mar

ital s

tatu

s 1

Secu

rity

5

Edu

catio

nal l

evel

3

Job

expe

rien

ce

1 Fa

cilit

atin

g co

nditi

ons

5 Ta

skndasht

echn

olog

y fi t

3

Ade

quat

e re

sour

ces

1 Jo

b re

leva

nce

5 R

isk

3 Fl

exib

ility

1

Rat

e of

dec

isio

n ef

fi cie

ncy

5 Pl

ace

of r

esid

ence

3

Beh

avio

ral c

ontr

ol

1 R

espo

nse

time

5 M

anag

eria

l sup

port

3

Com

pute

r ex

peri

ence

1

Use

r in

terf

ace

5 V

olun

tari

ness

3


216











OM Koumlk et al

217













218


Entity type Central

Goal









8543 Descriptives


OM Koumlk et al

219








220







8545 ANOVA Results



OM Koumlk et al

221

Tabl

e 8

21

Reg

ress

ion

resu

lts f

or d

epen

dent

fac

tors

Dep

ende

nt

Inde

pend

ent

Coe

ffi c

ient

bet

a St

anda

rdiz

ed c

oeffi

cie

nt

Sign

ifi ca

nce

R 2

Adj

uste

d R

2

Qua

lity

of c

are

(Con

stan

t)

005

0

786

057

8 0

575

Use

fuln

ess

063

0

55

000

0 A

ttitu

de

027

0

24

000

0 E

ffi c

ient

use

(C

onst

ant)

minus

020

0

697

054

2 0

529

TT

F 0

57

027

0

000

Use

rInt

erfa

ce

079

0

28

000

0 A

cces

sAL

L

068

0

14

000

2 Fu

ncX

Med

0

33

009

0

049

Info

0

39

017

0

009

Inte

grat

ionS

W

039

0

11

001

8 Fu

ncD

ose

034

0

09

004

4 D

iffu

sion

(C

onst

ant)

0

10

061

1 0

572

056

9 U

sefu

lnes

s 0

67

054

0

000

Atti

tude

0

29

024

0

001

Infu

sion

(C

onst

ant)

minus

024

0

346

046

4 0

460

Use

fuln

ess

069

0

49

000

0 E

oU

031

0

22

000

1 U

se d

ensi

ty

(Con

stan

t)

085

0

000

042

1 0

417

Use

fuln

ess

062

0

51

000

0 A

ttitu

de

019

0

16

004

4 Sa

tisfa

ctio

n (C

onst

ant)

minus

043

0

009

071

2 0

710

EoU

0

56

045

0

000

Use

fuln

ess

054

0

44

000

0


222







020

044

045

051

054

055

Diffusion

Infusion

Attitude

Use Density

Satisfaction

Quality of Care

EoU

Usefulness

Use Density

EoL

Self Confidence

Func Dose

User Int

Mobility

Info

Flexibility

p lt 0100 p lt 0010 p lt 0001


OM Koumlk et al

223

027 Efficient Use

FuncXMed

Func Dose

User Interface

TTF

Access All

Info

IntegrationSW

p lt 0100 p lt 0010 p lt 0001















225












226









OM Koumlk et al

227







20 66





228

0123456789

EoU

EoL

Usefulness

Attitude

Satisfaction

QualityofCare

EfficientUse

UseDensity

Diffusion

Infusion

1

2

3



1 2 3 4





OM Koumlk et al

229






1 2 3 4


000

100

200

300

400

500

600Flexibility

Info

AccessALL

KnowledgeShare

Mobility

PrivacyMD

PrivacyUA

Security

SelfConfidence

SupportQuality

TTF

UserInterface

1

2

3

4



230







86 Conclusion





OM Koumlk et al

231

861 Limitations


862 Implications








232




87 Appendices











OM Koumlk et al

233

(con

tinue

d)

Oumlze

llikl

er

Anl

am

Accedilı

klam

a D

emog

raph

ics

Dem

ogra

fi k

Kul

lanı

cını

n de

mog

rafi k

oumlze

llikl

eri

1 A

ge

Yaş

K

ulla

nıcı

nın

yaşı

2

Edu

catio

nal L

evel

E

ğitim

Duumlz

eyi

Kul

lanı

cını

n eğ

itim

duumlz

eyi

3 G

ende

r C

insi

yet

Kul

lanı

cını

n ci

nsiy

eti

4 In

com

e G

elir

K

ulla

nıcı

nın

aylık

gel

iri

5 M

arita

l sta

tus

Evl

ilik

Dur

umu

Kul

lanı

cını

n ev

lilik

dur

umu

6 Jo

b ex

peri

ence

İş

Den

eyim

i K

ulla

nıcı

nın

iş d

eney

imi

7 Pl

ace

of r

esid

ence

İk

amet

Yer

i K

ulla

nıcı

nın

ikam

et y

erin

in ouml

zelli

liği (

koumly

ilccedile

şeh

ir m

erke

zi)

8 O

ccup

atio

n M

esle

k K

ulla

nıcı

nın

mes

leği

In

term

edia

ry

Ara

cı Ouml

zelli

kler

K

ulla

nıcı

yaz

ılım

la e

tkile

şim

e ge

ccediltiğ

i sır

ada

orta

ya ccedil

ıkan

oumlz

ellik

ler

kiş

inin

yaz

ılım

ı kul

land

ığın

da k

azan

dığı

fay

da

kulla

nım

ın k

olay

olm

ası g

ibi

9 E

ase

of u

se

Kol

ay K

ulla

nım

Y

azılı

mın

kol

ay k

ulla

nım

ı 10

U

sefu

lnes

s Fa

yda

Yaz

ılım

ın k

ulla

nım

dan

doğa

n fa

yda

11

Eas

e of

lear

ning

K

olay

Oumlğr

enm

e Y

azılı

mı k

ulla

nmay

ı oumlğr

enm

enin

kol

aylığ

ı C

linic

al v

aria

bles

K

linik

Oumlze

llikl

eri

Has

tane

ile

ilgili

değ

işke

nler

12

G

eogr

aphi

c ar

ea

Coğ

rafi

Kon

um

Has

tane

nin

coğr

afi k

onum

u (ş

ehir

mer

kezi

ilccedil

e k

oumly g

ibi)

13

Po

pula

tion

serv

iced

H

izm

et E

ttiği

Nuumlf

us

Has

tane

nin

hizm

et v

erdi

ği k

işi s

ayıs

ı 14

H

ospi

tal s

ize

Has

taha

ne B

uumlyuumlk

luumlğuuml

H

asta

neni

n fi z

ikse

l buumly

uumlkluuml

ğuuml

15

Oth

er c

linic

al v

aria

bles

D

iğer

Değ

işke

nler

H

asta

ne il

e ilg

ili d

iğer

değ

işke

nler

16

A

dequ

ate

reso

urce

s K

ayna

klar

H

asta

neni

n se

rvis

icin

ayi

rabi

lece

gi k

ayna

klar

17

C

linic

al s

peci

alty

U

zman

lık A

lanı

H

asta

neni

n ge

nel u

zman

lık a

lanı

Su

ppor

t D

este

k Y

azılı

mı k

ulla

nanl

ara

veri

len

tekn

ik d

este

k

87

2 2

Exp

ert F

ocus

Gro

up Q

uest

ionn

aire


234

18

Man

ager

ial s

uppo

rt

Youmln

etim

Des

teği

Y

oumlnet

icile

rin

serv

isin

kul

lanı

lmas

ı iccedili

n ve

rdiğ

i des

tek

19

Peer

sup

port

A

rkad

aş D

este

ği

Yaz

ılım

kul

lanı

mı s

ıras

ında

yaş

ıtlar

ının

dan

veya

ark

adaş

ları

ndan

al

dığı

des

tek

20

Prof

essi

onal

sup

port

Pr

ofes

yone

l Des

tek

Yaz

ılım

kul

lanı

mı s

ıras

ında

pro

fesy

onel

lerd

en a

lınan

des

tek

21

Ven

dor

supp

ort

Satıc

ı Des

teği

Sa

tıcı fi

rm

anın

sağ

ladı

ğı y

ardı

m v

e de

stek

22

Q

ualit

y of

sup

port

D

este

ğin

Kal

itesi

V

erile

n ya

rdım

ve

dest

eğin

kal

itesi

23

So

cial

infl u

ence

So

syal

Etk

enle

r Y

azılı

mı k

ulla

nan

kişi

nin

ccedilevr

esin

deki

lerd

en

aldı

ğı e

tki

24

Com

patib

ility

U

yum

lulu

k Y

azılı

mı ouml

ncek

i suumlr

uumlmle

ri v

eya

ccedilalış

tırıld

ığı o

rtam

daki

diğ

er

sist

emle

re u

yum

u C

onte

nt

Serv

is İ

ccedileri

ği

Yaz

ılım

ın s

undu

ğu b

ilgin

in iccedil

eriğ

i 25

A

ccur

acy

Doğ

rulu

k Su

nula

n bi

lgin

in d

oğru

luğu

26

St

anda

rdiz

atio

n St

anda

rd

Bilg

inin

sta

ndar

t bir

şek

ilde

sunu

lmas

ı 27

In

form

atio

n qu

ality

B

ilgi K

alite

si

Sunu

lan

iccediler

iğin

kal

itesi

28

Se

curi

ty

Bilg

inin

Guumlv

enliğ

i İccedil

eriğ

in b

aşka

ları

nın

eriş

emey

eceğ

i bir

ort

amda

sak

lanm

ası

29

Tool

exp

erie

nce

Den

eyim

K

ulla

nıcı

nın

benz

er s

ervi

s ya

uumlruuml

n ile

ilgi

li ge

ccedilmiş

den

eyim

leri

30

Im

age

İmaj

K

ulla

nıcı

ları

n et

rafl a

rınd

aki i

nsan

lara

ken

dile

rini

far

klı

ayrı

calık

lı ve

oumlnc

uuml gouml

ster

me

iste

ği

31

Satis

fact

ion

Mem

nuni

yet

Kul

lanı

cını

n ya

zılım

dan

mem

nun

kalm

ası

32

Vol

unta

rine

ss

Goumln

uumllluuml

luumlk

Kul

lanı

cını

n yuuml

kuumlm

luumlluuml

ğuuml o

lmad

an is

teye

rek

yazı

lımı k

ulla

nmas

ı 33

Fa

cilit

atin

g co

nditi

ons

Kol

ayla

ştır

ıcı

Koş

ulla

r Y

azılı

mın

kul

lanı

mın

ı kol

ayla

ştır

acak

koş

ulla

r

34

Func

tiona

l cha

ract

eris

tics

Fonk

siyo

nel

Oumlze

llikl

er

Yaz

ılım

ın f

onks

iyon

el ouml

zelli

kler

i

35

Flex

ibili

ty

Kiş

isel

leşt

irile

bilir

lik

Yaz

ılım

ın f

onks

iyon

ları

nı is

teğe

goumlr

e de

ğişt

ireb

ilmek

Oumlrn

eğin

m

enuumln

uumln s

ıras

ı uumlze

rind

e de

ğişi

klik

yap

abilm

esi

Oumlze

llikl

erA

nlam

Accedilı

klam

aD

emog

raph

ics

Dem

ogra

fi kK

ulla

nıcı

nın

dem

ogra

fi k ouml

zelli

kler

i

(con

tinue

d)

OM Koumlk et al

235

36

Acc

essi

bilit

y U

laşa

bilir

lik

Yaz

ılım

ın k

ulla

nıcı

lar

tara

fınd

an k

olay

ula

şala

bilir

olm

ası

37

Beh

avio

ral c

ontr

ol

Kul

lanı

cını

n ya

zılım

ı kul

lanm

ak iccedil

in y

eter

li ye

tene

kler

inin

ka

ynağ

ının

ve

fırs

atın

ın o

lup

olm

adığ

ı alg

ısı

38

Job

rele

vanc

e Iş

e U

ygun

luk

Yaz

ılım

ın d

okto

run

işin

e uy

gunl

uğu

Med

ical

M

edik

al

Yaz

ılım

med

ikal

ala

ndak

i etk

ileri

39

R

ate

of s

ucce

ssfu

l tre

atm

ents

B

aşar

ılı T

edav

ileri

n O

ranı

Y

azılı

mın

kul

lanı

cını

n uy

gula

dığı

teda

vile

rin

oran

ını a

rtır

mas

ı 40

R

ate

of s

ucce

ssfu

l dia

gnos

is

Baş

arılı

Teş

hisl

erin

O

ranı

Y

azılı

mın

kul

lanı

cını

n ko

yduğ

u te

şhis

leri

n or

anın

ı art

ırm

ası

41

Rat

e of

dec

isio

n ef

fi cie

ncy

Kar

ar v

erm

e ve

rim

liliğ

inin

ar

tırılm

ası

Yaz

ılım

ın k

ulla

nıcı

nın

kara

r ve

rme

doğr

uluğ

unu

artır

mas

ı

42

Res

pons

e tim

e Si

stem

in Ccedil

alış

ma

Hız

ı Y

azılı

mın

kul

lanı

m z

aman

ı Y

azılı

mın

kul

lanı

mas

ı ccedilok

zam

an a

labi

lir v

e ku

llanı

cıla

rın

yete

rinc

e va

kti o

lmay

abili

r 43

G

uide

lines

D

oumlkuumlm

anta

syon

Y

azılı

mın

doumlk

uumlman

tasy

onu

44

Hab

it A

lışka

nlık

K

ulla

nıcı

nın

mev

cut a

lışka

nlar

ı 45

T

rust

G

uumlven

ilirl

ik

Kul

lanı

cını

n ya

zılım

a du

yduğ

u guuml

veni

C

ompu

ter

liter

acy

Bilg

isay

ar

Oku

ryaz

arlığ

ı K

ulla

nıcı

nın

bilg

isay

ar b

ilgis

i ve

okur

yaza

rlığ

ı

46

Com

pute

r ex

peri

ence

B

ilgis

ayar

Den

eyim

i K

ulla

nıcı

nın

kaccedil

yıld

ır b

ilgis

ayar

kul

land

ığı

47

Com

pute

r lit

erac

y B

ilgis

ayar

O

kury

azar

lığı

Kul

lanı

cını

n bi

lgis

ayar

kul

lanı

mın

ı ne

kad

ar iy

i bild

iği

48

Use

r in

terf

ace

Ekr

an G

oumlruumln

tuumlsuuml

Y

azılı

mın

kul

lanı

cı e

kran

ları

nın

oumlzel

likle

ri

49

Task

ndashtec

hnol

ogy

fi t

Tekn

oloj

iGoumlr

ev U

ygun

luğu

Y

azılı

mın

kul

lanı

cını

n ya

ptığ

ı goumlr

evle

re u

ygun

luğu

50

R

isk

Ris

k Y

azılı

mın

kul

lanı

lmas

ında

n do

ğabi

lece

k ol

an r

iskl

er

51

Secu

rity

G

uumlven

lik

Yaz

ılım

ın k

ulla

nılm

ası i

le o

luşa

n bi

lgik

ulla

nıcı

has

ta g

uumlven

liği


236



(continued)

OM Koumlk et al

237


(continued)


238

Tabl

e 8

34

Fact

or a

naly

sis

for

all i

tem

s

1 2

3 4

5 6

7 8

9 10

U

sef3

0

822

012

4 0

147

028

1 0

135

001

1 0

104

minus0

096

minus0

116

003

4 Q

oCar

e3

079

4 0

177

014

2 0

240

003

3 0

105

021

1 0

031

minus0

006

005

6 U

sef2

0

793

018

8 0

146

025

3 0

128

minus0

001

011

6 minus

004

6 minus

011

8 0

038

Atti

tude

2 0

793

030

0 0

132

013

2 0

115

005

9 minus

014

6 0

011

001

8 0

098

Atti

tude

1 0

781

024

9 0

209

025

0 0

121

008

5 minus

012

5 minus

001

3 minus

001

1 0

055

Use

f1

077

4 0

233

019

8 0

249

008

3 minus

009

8 0

004

minus0

021

000

5 0

027

Dif

fusi

on

074

9 0

279

022

9 0

192

minus0

030

001

2 0

146

010

5 minus

002

3 0

036

QoC

are2

0

702

023

9 minus

001

2 0

037

010

9 0

087

020

8 0

132

020

4 0

048

Use

f6

070

1 0

389

028

3 0

186

007

8 0

031

minus0

137

006

5 minus

010

1 0

045

Use

f4

063

0 0

487

031

1 0

289

008

3 minus

004

5 0

038

009

4 0

038

002

9 Sa

tisfa

ctio

n3

055

9 0

482

043

1 0

255

004

2 minus

001

4 0

127

011

3 minus

006

5 0

018

Infu

sion

0

532

036

9 0

287

026

3 minus

001

5 0

056

021

0 0

253

minus0

103

011

1 U

seD

ensi

ty

052

2 0

359

032

3 0

397

minus0

041

minus0

093

002

1 0

082

minus0

047

008

6 Q

oCar

e1

052

0 0

179

008

2 0

075

004

9 0

104

050

4 0

068

020

6 0

140

Satis

fact

ion1

0

484

044

6 0

476

034

9 0

094

minus0

030

012

1 0

163

minus0

006

minus0

019

EoU

2 0

480

043

2 0

378

036

3 0

105

002

2 minus

016

4 0

193

004

6 minus

004

3 Sa

tisfa

ctio

n2

046

7 0

461

043

3 0

371

010

3 minus

003

2 0

098

017

0 minus

000

6 minus

002

2 U

sef7

0

448

031

9 0

145

033

0 0

212

022

9 0

162

029

9 0

114

004

1 Se

lfC

onfi d

ence

0

427

015

6 0

169

036

9 0

147

minus0

113

minus0

317

021

4 0

048

014

5 U

sef5

0

407

016

6 0

174

033

0 0

309

017

8 0

169

018

9 0

174

006

1 U

serI

nter

face

6 0

284

071

5 0

158

016

5 0

171

minus0

081

015

1 minus

005

0 0

085

001

8

87

4 4

Fac

tor

Ana

lysi

s R

esul

ts

OM Koumlk et al

239

Use

rInt

erfa

ce1

032

3 0

711

033

1 0

164

minus0

002

minus0

003

minus0

052

003

8 minus

005

0 0

068

Use

rInt

erfa

ce5

036

3 0

681

028

5 0

333

004

9 minus

001

5 minus

006

8 minus

001

9 0

038

010

8 E

oU4

043

2 0

616

017

0 0

245

017

2 0

132

minus0

086

001

5 0

048

014

2 U

serI

nter

face

2 0

208

061

5 0

357

024

8 0

115

minus0

010

021

4 0

079

010

3 minus

005

2 U

serI

nter

face

4 0

317

058

9 0

128

035

4 0

146

004

0 minus

014

5 minus

004

5 0

019

010

7 Fl

exib

ility

3 0

359

053

7 0

211

032

0 0

244

016

6 0

213

014

4 minus

003

9 minus

001

7 Fl

exib

ility

1 0

327

051

0 0

099

014

7 0

054

027

8 0

176

minus0

030

minus0

197

minus0

031

Use

rInt

erfa

ce8

035

5 0

509

018

9 0

212

015

4 0

007

011

9 0

158

020

4 minus

009

5 E

oU1

046

5 0

485

041

0 0

207

002

4 minus

006

7 minus

011

6 0

129

minus0

043

003

4 M

obili

ty1

033

3 0

458

031

2 0

097

012

6 minus

012

3 0

179

031

5 minus

016

3 0

126

TT

F2

014

7 0

200

074

5 0

262

012

8 0

052

027

8 0

042

008

8 0

029

TT

F3

031

9 0

233

067

5 0

211

021

8 0

079

006

0 0

019

minus0

085

002

9 E

oU3

033

7 0

289

065

5 0

171

009

5 minus

003

1 minus

013

9 0

141

003

9 minus

003

2 E

oL

012

1 0

287

065

0 0

178

005

1 0

083

minus0

228

014

4 minus

002

3 0

029

TT

F1

010

1 0

190

064

9 0

341

008

3 0

087

029

1 minus

001

4 minus

003

8 0

037

Use

rInt

erfa

ce7

028

7 0

221

063

2 minus

007

5 0

180

minus0

050

001

5 minus

003

6 0

077

006

8 E

ffi c

ient

Use

0

237

032

4 0

454

030

5 0

123

016

0 0

163

021

6 0

250

003

6 Pr

ivac

yUA

0

059

004

6 0

356

019

9 0

318

007

4 0

256

minus0

093

003

7 0

288

Acc

urac

y 0

396

025

8 0

186

066

6 0

124

minus0

049

001

8 0

014

007

3 0

004

Con

sist

ency

0

440

030

7 0

180

062

0 0

107

minus0

017

minus0

059

013

2 0

018

minus0

068

Stan

dard

izat

ion

040

7 0

319

024

9 0

614

016

4 minus

001

9 minus

014

5 0

153

minus0

008

007

4 Se

curi

ty1

026

3 0

262

015

6 0

610

005

2 0

098

014

9 0

021

minus0

020

035

2 U

ptoD

ate

047

0 0

222

018

2 0

596

019

3 0

061

minus0

033

005

2 0

021

minus0

016

(con

tinue

d)


240

Com

plet

enes

s 0

416

034

6 0

231

056

6 0

078

001

7 0

186

005

0 0

153

minus0

038

Secu

rity

2 0

300

030

9 0

180

055

1 0

139

007

5 minus

000

1 minus

009

1 minus

008

3 0

344

Supp

ortQ

1 0

351

040

6 0

202

050

1 0

208

007

5 0

117

007

4 minus

003

6 minus

005

0 M

obili

ty2

030

7 0

300

009

0 0

159

071

5 minus

001

9 0

043

013

0 0

058

010

2 U

serI

nter

face

3 0

066

000

7 minus

036

0 minus

013

7 minus

061

5 0

100

minus0

037

minus0

024

minus0

024

minus0

059

Mob

ility

3 0

366

033

8 0

158

025

8 0

593

013

5 minus

003

8 0

185

002

4 0

015

Func

Ran

ge

minus0

023

013

5 minus

002

9 0

023

008

2 0

751

minus0

036

007

6 0

054

minus0

097

Func

Dos

e 0

141

minus0

126

014

5 0

008

minus0

139

063

0 0

148

015

7 0

126

014

4 Fl

exib

ility

2 0

192

013

6 0

370

minus0

023

038

4 0

119

047

2 0

042

001

6 minus

002

9 A

cces

sAL

L

minus0

015

minus0

007

008

4 0

032

010

5 0

235

minus0

051

078

1 0

128

minus0

035

Supp

ortQ

2 0

298

025

0 0

054

035

5 0

124

minus0

084

019

9 0

420

minus0

034

minus0

002

Inte

grat

ionS

W

minus0

019

007

3 0

043

003

4 0

040

minus0

050

002

0 0

149

073

7 0

146

Inte

grat

ionH

W

minus0

172

minus0

141

minus0

106

minus0

132

minus0

022

036

9 minus

005

5 minus

001

2 0

547

minus0

143

Func

XM

ed

010

0 0

041

012

8 0

168

008

6 0

344

013

6 minus

011

7 0

503

minus0

222

Kno

wle

dgeS

hare

0

070

005

6 minus

005

6 0

162

004

8 minus

006

8 0

057

006

7 minus

001

4 0

792

Priv

acyM

D

012

6 minus

004

4 0

428

minus0

183

011

2 0

024

minus0

125

minus0

196

003

9 0

536

Ext

ract

ion

met

hod

pri

ncip

al c

ompo

nent

ana

lysi

s R

otat

ion

met

hod

var

imax

with

Kai

ser

norm

aliz

atio

n a R

otat

ion

conv

erge

d in

13

itera

tions

Tabl

e 8

34

(con

tinue

d)

12

34

56

78

910

OM Koumlk et al

241

Component 1






Component 1





242
















(continued)

OM Koumlk et al

243





Usefulness 069 049 0000 EoU 031 022 0001












(continued)


244











(continued)


OM Koumlk et al

245







92 EoU (Constant) 259 0000 0306 0303 EoL 038 055 0000




References






246






















OM Koumlk et al

247

























248
























OM Koumlk et al

249















Series Foreword13

Preface

Contents






References





231 Care Providers



232 Government


234 Payers


2351 HIT Vendors






253 Policy Making








2511 Infusion






References




33 Diffusion Theory

331 An Innovation


3312 Compatibility

3313 Complexity

3314 Trialability

3315 Observability










347 Lazy User Model




3512 Penrose 1959






361 Model

362 Diagram

363 View

364 Domain


366 Tool

367 Simulation












377 SysML











3981 Open Coding

3982 Axial Coding


References






43 Research Design

431 Overview

432 Objectives





4334 Study Sampling



434 Analysis










4354 Limitations




4363 Assumptions




Outside Doubters

Walkthroughs

Hypothesis Testing

Tornado Diagram


Conceptual Validity


Believability


4368 Limitations

References




References






References







721 Criteria








7218 Methodology


References



81 Introduction





83 Framework

84 Methodology



843 Pilot Study


85 Findings



8512 User Interface






8518 Functionality










8543 Descriptives


8545 ANOVA Results



86 Conclusion

861 Limitations

862 Implications

87 Appendices






References

tugrul˜u.˜daim nima˜a. behkami orhun˜m.˜kök … · 2020. 5. 5. · nima˜a. behkami...

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