big data management in gaza strip hospitals: challenges
TRANSCRIPT
Big Data Management in Gaza Strip
Hospitals: Challenges and Opportunities
دارة البيانات الضخمة في مستشفيات قطاع غزة: التحديات والفرصإ
By
Bahaa J Elsirr
Supervised by
Prof. Yousif Ashour
Prof. of Management
A thesis submitted in partial fulfillment of the requirements for the degree of
Master in Business Administration, at faculty of graduate studies, Islamic
University of Gaza, Palestine.
Feb./2018
زةــغب ةــلاميـــــالإس ـةـــــــــامعـالج
البحث العلمي والدراسات العليا عمادة
التجـــــــــــــــــــــارةة ــــــــــــــــــــليـك
ادارة اعمــــــــــــــــالر ـــــــــــماجستي
The Islamic University of Gaza
Deanship of Research and Graduate Studies
Faculty of Commerce
Master of Business Administration
I
إقــــــــــــــرار
أنا الموقع أدناه مقدم الرسالة التي تحمل العنوان:
ج
Big Data Management In Gaza Strip Hospitals:
Challenges and Opportunities
ادارة البيانات الضخمة في مستشفيات قطاع غزة: التحديات والفرص
أقر بأن ما اشتملت عليه هذه الرسالة إنما هو نتاج جهدي الخاص، باستثناء ما تمت الإشارة إليه حيثما ورد،
لنيل درجة أو لقب علمي أو بحثي لدى أي الاخرين لم يقدم من قبل وأن هذه الرسالة ككل أو أي جزء منها
مؤسسة تعليمية أو بحثية أخرى.
Declaration
I understand the nature of plagiarism, and I am aware of the Hospitals’s policy on
this.
The work provided in this thesis, unless otherwise referenced, is the researcher's own
work, and has not been submitted by others elsewhere for any other degree or
qualification.
:Student's name بهاء الدين جمال السر اسم الطالب:
:Signature التوقيع:
:Date 2018 التاريخ:
II
Abstract
The research aimed to understand the main barriers and opportunities to adopt Big Data
technology in Palestinian hospitals in the Gaza Strip. The research used analytical
descriptive methodology, a structured questionnaire consisted of five main parts which
include the following information: Demographic data, IT skilled labor, Cultural and
organizational, Security and, Budget constraints, Factors to adopte Big Data projects in
Palestinian hospitals. Also used a qualitative complement- interview as a data collection
tool from healthcare data scientist, site to enrich the assessment of Big Data’s challenges
and opportunities, and to provides more complete answers and depth of the information
The population of research selected all of the members of Information Technologist' (IT)
staff who are working in Information Systems Development Unit, Information Systems Unit
and in the major hospitals has Healt Information System (HIS) "EL-sheaf-Nasser-European
Gaza Hospitals" related to Ministry of Health (MoH). research population consisted of (142)
employees with qualifications related to the field of information technology, (82)
questionnaires recovered from (114) was distributed.
The adoption of Big Data Management was hostaged by factors relating to research, result
show that, (58.4 %) of Top Management support adoption of Big Data, (60.85%) Cultural
and organizational will facilitator the Big Data, (68.8%) IT staff at MoH has skills to adopt
Big Data, (81.7%) Security and privacy is challenges in the adoption of Big Data, (76.7%)
Budget constraints and undiscovered business value with the Big Data.
Finally, The research sets recommendations that will facilitate adopte Big Data. First, MoH
shoud setup an effective big data management strategy to address these challenges, and
should build capacity for data management and analytics. Second, Big Data project require
IT skills allied with clinical understanding, and therefore, MoH should send IT staff to
scientific missions to take advantage of technological developments surrounding Big Data.
and The hospitals should have a performance assessment system that points a clear criteria
for staff ability to deal with Big Bata, that performance system construe to an incentive
system. Finally, MoH with Limite budget is not expected to approved Big Data project,
Therefore, the researcher is advised to design and market the project to donors, for its
importance on operations, its an attractive option to the hospitals, that will support the
decision-making process and bitter support the diagnostic process, especially MoH has IT-
Team is amorous to development.
III
الملخص
استكشاف التحديات والفرص الرئيسية في مستشفيات قطاع غزة لتبني "تكنولوجيا البيانات إلى ت الدراسةھدف مھارات ،، الثقافة التنظيميةالعليا الإدارة دعم) وھم المتغيرات من خمسة تأثير مدىوقد درست ، الضخمة" تقنية البيانات الضخمة. تبني على تكلفة التقنية(، ،والحماية الأمان ،تالمعلوما تكنولوجيا يموظف
في المعلومات تكنولوجيا بمجال تتعلق التي المؤھلات ذوي الموظفين من موظف) 142مجتمع الدراسة شمل )وحدتي بالاضافة الى وروبي،غزة الأ مجمع ناصر، ومستشفى مجمع الشفاء، -مستشفيات قطاع غزة الرئيسية قام الباحث باتباع التابعة لوزارة الصحة الفلسطينية. وقد -وحدة نظم المعلوماتتطوير تكنولوجيا المعلومات، و
من ةاستبان ((82استردادتم البيانات، لجمع واتكأد والمقابلة الاستبانةالمنهج الكمي والوصفي مستخدماً ( مقابلات مهيكلة مع 7، وثم اجريت )الإحصائي التحليل برنامج ستخدامبا تحليلھا ثمو توزيعھا تم، (114)لأص
خبراء في ادارة المعلومات الصحية في غزة، للمساعدة في فهم وتفسير النتائج وتسليط الضوء أكثر على أبرز دلالة ذات علاقة ھناك أن التحليل نتائج توضح التحديات والفرص المتاحة لتطبيق تكنولوجيا ادارة البيانات.
فكانت ،0.05 دلالةى مستو عند ت الخمسة،المتغيرا وبين تقنية ادارة البيانات الضخمة تبني بين إحصائية%( الثقافة التنظيمية للمؤسسة 60.9%( أن الادارة العليا تدعم تبني ادارة البيانات الضخمة،)58.4النتائج )
جيا المعلومات لديه استعداد ويدعم تبني ادارة تكنولو %( من فريق 68.8تسهل تبني ادرة البيانات الضخمة، )%( من المستطلع آرائهم يعتبر السرية وأمن المعلومات تحدي لتبني ادارة البيانات 81.7البيانات الضخمة، )
%( يرى أنه يوجد قيود الموازنة وعدم معرفة فوائد تكنولوجيا ادارة البيانات الضخمة تحدي 76.7الضخمة، و) أساسي.
تقنية تبني إمكانھاأن ب وزارة الصحة الفسطينيةالمستشفيات العامة و توصي فإنھا الدراسة، صياتتو عن أماة علمي لبعثات إرسالھمو المعلومات تكنولوجيا موظفيتدريب ب اھتمام وجد إذا عملياتھا، فيالبيانات الضخمة
شراء نظم حماية خلال من لحمايةوا بالأمان اھتمام وجد إذا أيضا ،لاكتساب مهارات ادارة البيانات الضخمةا عملياتھ في تقنية ادارة البيانات الضخمة تبني في حيوي دورهذا الة العليا لالإدار دعم ان شك وبدون ،متطورةل اعتماد استراتيجية لتعزيز الثقافة التنظيمة التي تسهل تبني ادارة البيانات الضخمة وتأكيد مشاركة خلا من
نحين لتجاوز قصور الجانب تسويق مشروع هذه التقنية للما ضرورة مع ممارسات،هذه الالكادر الطبي في تغطية تكاليف تقنية البيانات الضخمة.المالي و
IV
(31،30، الآيات )سورة الكهف
V
Dedication
“To my mother soul, to my dear father who emphasized
the importance of education, they helped me throughout my life,
and supported me to continue my education. To my wife for her
quiet patience and unwavering love were undeniably. To my
lovely brothers and sisters for their never-ending unconditional
support.
I dedicate this work to my mother and prayed God
Almighty to be beneficial”
Researcher
VI
Acknowledgment
I would like to express my gratitude to my supervisor Professor
Yousif Ashour for the useful comments, remarks and engagement
through the learning process of this master thesis. Furthermore I would
like to thank Assistant Professor Khalid A. Dahleez for introducing me
to the topic as well for the support on the way.
I would not forget Professor Faris Moamer and Doctore Ayman
A. Radi for accepting to discuss this research.
Also, I like to thank Eng. Alaa El-shorafa who helped me during data
collection, I like to thank the participants in my survey, who have
willingly shared their precious time during the process.
I am also grateful to the Mr Ibrahim El-sirrsawy, Mrs Reem AL-
zeer, Mr Hani Al-Wahedi, Eng. Luay Frigaa, Eng. Ashruf Saqer, Dr.
Mohamed Zacott, Mr Kamal Moussa, and Mr Nedal Zurob, from MoH.
VII
Table of Contents
I ......................................................................................................................... إقــــــــــــــرار
Abstract ............................................................................................................................ II
III .............................................................................................................................. الملخص
Dedication ........................................................................................................................ V
Acknowledgment ............................................................................................................ VI
Table of Contents ........................................................................................................... VII
List of Tables .................................................................................................................. IX
List of Figures .................................................................................................................. X
List of Abbreviations ...................................................................................................... XI
Chapter 1 Introduction ................................................................................................. 1
1.1 Introduction: ......................................................................................................... 1
1.2 Research problem ................................................................................................ 2
1.3 Research Questions: ............................................................................................. 3
1.4 Variables .............................................................................................................. 4
1.5 Research Hypotheses: .......................................................................................... 4
1.6 Purpose of research .............................................................................................. 5
1.7 Research Importance: .......................................................................................... 5
1.8 Research Limitations and Challenges .................................................................. 6
1.9 Data Resources .................................................................................................... 6
1.10 Research Terminology: ........................................................................................ 6
1.11 Chapter Summary ................................................................................................ 7
Chapter 2 Literature Review ........................................................................................ 9
2.1 Introduction .......................................................................................................... 9
2.2 Big Data Management, Big Hope ........................................................................ 9
2.3 Palestinian Health systems overview ................................................................. 20
2.4 Palestinian Healthcare Big Data ........................................................................ 24
2.5 Big Data in answering Healthcare systems’ challenges .................................... 32
2.6 Challenges in Adoption Big Data technology in hospitals: ............................... 33
2.7 Chapter Summary .............................................................................................. 39
Chapter 3 Previous Studies ......................................................................................... 41
3.1 Introduction ........................................................................................................ 41
3.2 List of Relevant Previous Studies ...................................................................... 41
3.3 Commentary ........................................................................................................ 48
VIII
3.4 Chapter Summary ............................................................................................... 48
Chapter 4 Methodology ................................................................................................ 50
4.1 Introduction .......................................................................................................... 50
4.2 Research methodology ......................................................................................... 50
4.3 Research tools ...................................................................................................... 51
4.3.4 Study Design Stages .......................................................................................... 45
4.4 Data Collection ..................................................................................................... 51
4.5 Population and sample size ................................................................................. 51
4.6 Data Measurement .............................................................................................. 52
4.7 Test of Normality,Validity and Reliability of Research Tool ............................. 52
4.8 Chapter Summary ................................................................................................. 53
Chapter 5 Data Analysis and Result ........................................................................... 55
5.1 Introduction ......................................................................................................... 55
5.2 Sample characteristics .......................................................................................... 55
5.3 Big Data Adoption Level ..................................................................................... 57
5.4 Analyzing Hypotheses: ........................................................................................ 78
5.5 Chapter Summary ................................................................................................. 85
Chapter 6 Recommendations ....................................................................................... 87
6.1 Introduction ........................................................................................................... 87
6.2 Recommendations: ................................................................................................ 87
6.3 A roadmap for adoption Big Data in Palestinian Hospitals: ................................. 87
6.4 Future Research .................................................................................................... 88
6.5 Conclusion ............................................................................................................ 51
The Reference List ........................................................................................................ 53
Appendix ........................................................................................................................ 59
Appendix-A: Test of Normality, Validity and Reliability of Research Tool Test of
Normality for each field: ............................................................................................. 59
Appendix-B: Questionnaire (English) ........................................................................ 68
Appendix-C: Questionnaire (Arabic) .......................................................................... 74
Appendix-D: Interview transcription and Coding (English) ...................................... 81
IX
List of Tables
Table (2.1): A Definitional Frame Work For Big Data .............................................. 11
Table (2.2): Platforms And Tools For Big Data Analytics ......................................... 18
Table (2.3): Healthcare Hospitals In Gaza Strip. ........................................................ 22
Table (2.4): Ratio Of Healthcare Providers To Population - A Regional
Comparison ................................................................................................................. 22
Table (2.5): Example Data Sources Within A Healthcare Delivery System. ............. 24
Table (3.1) : Research Population ............................................................................... 51
Table (3.2 ): Primary Quantitative Data Will Collected Through Surveys. ............... 52
Table (4.1): Illustrates Sample Characteristics ........................................................... 55
Table (4.2): Means And Test Values For “The Adoption Of Big Data” .................... 57
Table (4.3): Means And Test Values For “Top Management Support Of The Big
Data Technology” ....................................................................................................... 60
Table (4.4): Means And Test Values For “Cultural And Organizational With The
Big Data Technology،،. .............................................................................................. 64
Table (4.5): Means And Test Values For “It Skills Team With The Big Data
Technology" ................................................................................................................ 68
Table (4.6): Means And Test Values For “Security And Privacy With The Big
Data Technology" ....................................................................................................... 72
Table (4.7): Means And Test Values For “Budget Constraints And Undiscovered
Business Value With The Big Data Technology،،. .................................................... 75
Table (4.8): Stepwise Regression ............................................................................... 78
Table (4.9): Anova For Regression ............................................................................. 79
Table (4.10) Shows The Analysis Of Variance For The Regression Model. ............. 79
Table (4.11): Correlation Coefficient Between Top Management Support And The
Adoption Of Big Data. ................................................................................................ 80
Table (4.12): Correlation Coefficient Between Cultural And Organizational
Factors And The Adoption Of Big Data Adoption . ................................................... 81
Table (4.13): Correlation Coefficient Between Skills Of It Human Resources And
The Adoption Of Big Data. ......................................................................................... 82
Table (4.14): Correlation Coefficient Between Security And Privacy And
Adoption Big Data. .................................................................................................... 83
Table (4.15): Correlation Coefficient Between Budget Constraints And Adoption
Big Data. ..................................................................................................................... 84
X
List of Figures
Figure (1): Life Cycle And Management Of Data Using Technologies And
Terminologies Of Big Data ......................................................................................... 15
Figure (2): Conceptual Classification Of Bd Challenges. .......................................... 17
Figure (3): Map-Reduce/Hadoop Architecture. .......................................................... 19
Figure (4): Research Design- Procedure ..................................................................... 45
Figure (5): Illustrates Challenges In Big Data ............................................................ 31
Figure (6): Main Barriers To Setup Big Data Project Within Hospitals. ................... 31
XI
List of Abbreviations
BD Big Data
BDM Big Data Management
DAMA Data management Association
EHR Electronic health record
GDP Gross Domestic Product
HDFS Hadoop Distributed File System
IS Information System
IDC Industrial Development Corporation
MoH Minestry of Healthcare
NoSQL Not Only SQL
VLDB Administrating Very Large Databases
WHO Word Health Organization
*Note: Sort Alphapiticaly
Chapter 1
Introduction
1
Chapter 1
Introduction
1.1 Introduction:
Nowadays, The term ‘big data’ has increased vast prevalence lately among IT
experts. Big data describes the complex-massive amount of data that can be analyzed
using technology to gain business values that will help organizations to achieve
competitive advantages, Big Data often described by the concept of “Vs”, data
volume is high with “a variety” diverse sources and data formats,“velocity” data flow
in the system and the analysis process are in high speed (Gandomi & Haider, 2015;
McAfee et al., 2012), Thus these data generate an economic value for the enterprise
“value” (Gantz & Reinsel, 2012a), with “veracity” refers to the biases, abnormality
and noise in data. (White, 2012). So its required technology such as the NoSQL
databases and Hadoop/MapReduce frameworks, which have analytical capabilities
for capturing, processing, transforming, detecting and extracting value and deep
insights within an acceptable time.
Healthcare Big Data show no criterion definition and has been attached with
Electronic Health Record (HER) (Velthuis, et al., 2013). its refers to the patient
database such as Lab reports, physician notes, X-Ray reports, case history, list of
medical team in a certain hospital, national health register data, pharmacy and
storage of medicines, medical instruments and their expiry date. So Healthcare
hospitals are requirement high technology to get an overview of community health
care coordination, health management, and patient engagement, and (Groves et al.,
2013) classifies five key pathways in which Big Data that offers value in healthcare:
right living, right care, right provider, right value and right innovation.
Several studies in the area of big data projects refer to challenges to its
successful implementation. cultural and organizational sluggishness (McAfee et al.,
2012), as well as skilled labor constraints (Chen, et al., 2012), Moreover, data
privacy and security (Feldman, et al., 2012), the primary high investment combined
with undiscovered economic value of the project.
In this context, Mosbah (2010) in his research show that there are constraints
limit the effectiveness of Palestine healthcare information systems sprouting a
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weakness of the required funds, failure to provide adequate training, a lack of vision
about the need for comprehensive planning for e-health applications.
It is essential to determine whether MoH hospitals is “ready” for Big data
projects implementation, Thus introducing Big Data to the healthcare must be
preceded by sound research and highlights the barriers and facilitators which, when
addressed by policy-makers, can guide successful planning and implementation of
Big Data Management (BDM).
1.2 Research problem
Healthcare is characterized by the complexity (Bennani et al., 2008), and by a
wide spectrum of different actors that plays a vital role in the health system, such as
service receivers, service providers of medical and administrative staff, and health
insurance agencies. These health institutions, which include primary care centers,
hospitals, rehabilitation services, medical points, drug stores, and medical Laboratory
are provide services to a large number of patients, continuously and under pressure,
by massive administrative and medical staff. This situation illustrates the
complexities of health care institutions (Schweiger et al., 2007).
Recently the MoH faced an increase in the number of patients referred abroad
for hospitalization and consultation due to the shortage of some medical specialties,
lack of sophisticated diagnostic aids, and an increase in the number of bad causalities
from domestic and Israeli military aggression (MoH, 2014). This increased the
burden on the Ministry budget worsening its ability to address the increasing
demands of the population for services.
In fact, (WHO, 2013) studied the main weaknesses pointed in Gaza strip
health care, summarized as follows, the absence of performance indicators for
decision-support, poor levels of governance, Lack of standardized care ,Weak health
information and Lack of support. And Mosbah (2010) in his thesis agrued that
Palestinian health institutions concerning quality, several deficiencieswere identified,
including the absence of performance indicators to support decision-making and an
insufficient quality conscience culture.
3
Therefore, hospitals generate biggest and fastest growing data. actually, Freja,
(2017, Oct 19) in personal interview argue that MoH hospitals generate clinical data,
is estimated from 160 to 190 Gigabytes daily, presenting an increase between 58-70
terabytes per year. Hence, Healthcare Management Information Systems (HMIS) are
Under severe pressure due to handling high “Volume” , this increased “Volume” of
data is due to the diversity of sources and form “Variety” of health data. In other
words, the great dependence of healthcare providers on EHR (Chen et al., 2012).
On the other hand, medical services are combined activities including a
practitioners (eg, doctor, nurse, technologist, pharmacist, etc.) who work to provide a
type of healthcare service, which are diagnostic activities, directed towards the
patient and his treatment, with current and ongoing development of connecting new
medical instruments with technological systems, activating patient sensors, providing
home care devices, providing smart mobile devices with individuals including health
applications, we are talking about health communities, as well as the rise of
telemedicine data, are suppling the flow of health data (Crown, 2015).
Thus the complexity and huge data in hospitals has reached a point where
must it to uese technology tools. Therefore, this thesis aims to study the readiness in
Palestinian healthcare system in the Gaza Strip to Big Data technology, to understand
the facilitators and barriers of this process.
1.3 Research Questions:
The research seeks to answer the following main question:
What are the main challenges and opportunities considering the adoption
Big Data technology into MoH hospitals?
For the research question, there are sub-questions defined that help to oversee
the steps to achieve a similar answer to the research question:
a. What are the key challenges and opportunities to big data adoption in
MoH hospitals?
b. What are the benefits of big data adoption in MoH hospitals?
c. What are the costs of big data technology in MoH hospitals?
4
1.4 Variables
The model consists of five main indicators, which were covered, in
paragraphs. These indicators have the same importance level, the literature
highlights that top management support and governance structures (Rosemary et
al., 2015) data privacy and security fears (Feldman et al., 2012), cultural and
organizational rigidity (McAfee et al., 2012), skilled labor limitations (Chen et
al., 2012) and the high initial investment and unclear benefits (Zillner, et al.,
2014) as main barriers.
Source: Halaweh et al. (2015)/ Journal of International Technology and Information Management.
1.5 Research Hypotheses:
The research aims to test the following hypotheses:
H1: There is an effect of top management support on adoption Big Data projects in
Palestinian hospitals. (at level of significance α= 0.05).
H2: There is an effect of IT skilled staffe on adoption Big Data projects in
Palestinian hospitals. (at level of significance α= 0.05).
H3: There is an effect of Cultural and organizational elasticity on adoption Big Data
projects in Palestinian hospitals. (at level of significance α= 0.05).
H4: There is an effect of Budget constrains and business value on adoption Big Data
projects in Palestinian hospitals. (at level of significance α= 0.05).
H5: There is an effect of Security and Privacy on adoption Big Data projects in
Palestinian hospitals. (at level of significance α= 0.05).
Independent Variables
Top management support
Cultur and organization
IT skilled stuff
Security and Privacy
Budget constraints and undiscovered business
Dependent Variables
Readiness to Adoption of Big Data
Technology
5
1.6 Purpose of research
To explore the main barriers and opportunities in the Palestinian healthcare
system in the Gaza Strip to adopt "Big Data Technology", and to know the best Big
data management tools required to meet their needs, so its gives an insight of how
we can discover further value from the data generated by healthcare.
1.7 Research Importance:
The importance of this research appears from the fact that Big Data in
healthcare is still fresh and is not applied in Gaza. It needs to study the factors
influencing the adoption of Big data management project and determining which of
these factors is challenging or facilitating.
a. According to the Researcher:
The research is interested in the subjects of "BigData Management", specially
elements of Big Data term are available in health sector data and ots managed in a
technological way, since he believes on the importance of big data roles in
improving healthcare service.
b. According to Other Researchers
The researcher hopes from this research to be a good source of information
and knowledge to other researchers, and to be a trusted reference to them for their
research.
c. According to MoH and Hospitals
The research has a great importance to MoH and Hospitals since it makes
them realize and understand the importance of implementing and using big data
technology in their Hospitals, which processing, integrating factors and presenting
indicators to stakeholders, thus its contribute of patient service facility, until now
there is no single resesrch which studied Big Data Management in Palestinian
healthcare system, and explore bariears and facilitators to adopts Big Data analytical
tools.
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1.8 Research Limitations and Challenges
The researcher applies it on Gaza Strip Hospitals.
1.9 Data Resources
Primary data: the data obtained from a structured questionnaire, distributed
in heath care hospitals, and interview, chosen to attain the research goals.
Secondary data: the data obtained from several sources, like books, journals,
previous related studies or reports, and other sources and references that are related
to the research subject.
1.10 Research Terminology:
Big Data
It’s a puzzles tearm that means the data is very high “volume” with a
“variety” of sources and shapes, flowing, processed and analyzed at high “velocity”,
therefore generating economic “value” for the enterprise.
Data Management
It’s defined by DAMA as "development, execution and supervision of plans,
policies, programs and practices that control, protect, deliver and enhance the value
of data and information assets".(Varga, 2010)
Big Data Management
It’s about two things—big data and data management—plus how the two
work together to achieve business and technology goals (Rossum, 2013.), serves as
the basic step for managing and administrating very large databases.
Hadoop Map-Reduce system
Which applies map operations to the data in partitions of cluster using a
distributed file system (HDFS), it divides the data into smaller parts and distributes it
across the various servers/nodes, sorts and redistributes the results based on key
values in the output data, and then performs reduce operations on the groups of
output data items with matching keys from the map phase of the job.(Shvachko, et
al., 2010)
7
Health Information system (HIS)
It’s created the ability to electronically store, maintain and move data across
in a matter of seconds and has the potential to provide healthcare with remarkable
increasing productivity and quality of services.(Wager, et al., 2017)
1.11 Chapter Summary
In this chapter the researcher introduced the problem under study, elaborated
on the study objectives, questions and hypotheses, five main hypotheses, and
explained the various variables handled throughout the study. He also pointed out the
importance of the research to the different parties encompassing the researchers
himself, other researcher, MoH and hospitals. Study boundaries and challenges were
also briefed.
8
Chapter 2
Literature Review
9
Chapter 2
Literature Review
2.1 Introduction
The rapid increase in the amount of medical information in the health sector,
especially hospitals, - this dramatic increase in the amount of data generated by
various modern medical devices that automatically export data to Health
Management Information System (HMIS) -, has pushed hospitals to a important
issue of how to use health information technology to improve the quality of health
care service.
Researchers refer to the huge data that are uncontrollable by current
technologies and software tools as Big Data. Its may come in different forms such as
text, images, videos, sounds, other. (Fernández, et al., 2015). Hence, in this chapter
comprehensively classifies the various attributes of BigData, including its nature,
definitions, management, analysis, challenges. This research also highlight on
Palestinian Health systems, Big Data healthcare and its benefits for hospitals,
underlying technologies, factors to be consider in Big Data adoption.
2.2 Big Data Management, Big Hope
Big Data Management (BDM). is look like coins has two sides or segment -
big data and data management-, plus how these two sides work together to reach the
goals of the enterprise (Rossum, 2013.), and Data Management is defined by Data
Management Association as "development, execution and supervision of plans,
policies, programs and practices that control, protect, deliver and enhance the value
of data and information assets".(DAMA, 2009), So Big Data Management serves as
the basic step for managing and administrating very large databases (VLDB). So
these huge amount of data should be managed this data whenever in order to utilize
this information. This is known as Big Data Management (BDM).
As the trend of Big Data is increasing day by day accordingly all the
developers, IT professionals are realizing the need of Big Data. Thus at the initial
stages of development they used to manage this data by changing data into digital
form. Yet, with the expanding data this strategy failed. So they were an urgent need
10
to develop strategies to manage the huge data. This gave rise to the new term of Big
data Management (BDM) in the era of mechanization. This was done to reuse the
data over and over by the concerned association as well as by the other related
enterprises.
Big data technologies have already reformed the way Twitter, Groupon,
Facebook and numerous other new plans of action. This is a vital move in innovation
that may wind up much greater than commercialization of the Internet. Entire sector
of mechanization is affected by the Big Data Management because it them with the
new chances to store and reuse the data.(Kaur & Monga, 2016).
2.2.1 Big Data
Big Data refers to the processing of huge data obtained from diverse sources
(McAfee et al., 2012), Big Data analytics has been defined as technologies e.g.
database, data marts, Hadoop, Online Analatical Processing (OLAP) and data mining
tools, that an enterprise need this tools capable of manage and analyze huge-complex
data to enhance performance in various dimension (Kwon, et al., 2014)
Big Data has been creating excitement, becoming a buzz word all over the
world, and associated with the term Managment Revolution (McAfee et al., 2012).
On the other hand, despite the rapid development of Big Data, there was confusion
about the definition (Gandomi & Haider, 2015). Most researchers use the idea of
“Vs” o get a handle on the concept, varying between 3 to 5 “Vs”. As described
below, the data is huge “volume” with a “variety” of sources and shapes, flowing,
processed and analyzed at high “velocity”, therefore generating economic “value” for
the enterprise.
McAfee et al. (2012) and Gandomi and Haider (2015) pointted definitions
associated with data characteristics itself, so thay argue about three feature principle
highlights to Big Data, “Volume” is associated with the huge size of the data, which
could which is relative between various enterprises ranging from terabyte or
petabyte. “Variety” is associated not only with the variety of sources and data
formats, But its linked to heterogeneity of data, semi-structured and unstructured.
“Velocity” it’s the final feature argue by them wich mean speed at which data is
11
generated and transported. (Russom, 2011) Later on, Big data included “Value” in
which the economic benefits generated by analysis and processing of Big Data
(Gantz & Reinsel, 2012b). In addition to managing data, firms need that information
is “Veracity” which is equivalent to quality and to the importance of data (Marr,
2015).
Through another viewing, which is in terms of data processing technology,
Big Data refers to a set of emerging technologies designed to “extract value from
huge volumes of a wide variety of data by enabling high-velocity capture, discovery,
and analysis” (Villars, 2001). These technologies contain pattern recognition, data
warehouses, and natural-language credit (Costa, 2014). The information assembled
from big data can be a great degree valuable to those in medical and public health
fields engaged in research, behavioral analysis, interposition, and program
implementation (Margolis et al., 2014).
Overall, Wamba et al., (2015) Through systematic review sees Big Data “as a
universal approach to managing, processing and analyzing data that characterize with
5 Vs, in order to create an effective and successful way to deliver sustainable value,
measure performance, and create a competitive advantage for the enterprise to win
customer satisfaction.
Saxena, et al., (2016) in their search they prepared a summary of definitional
dimensions of Big Data in below Table(2.1):
Table (2.1): A definitional frame work for Big Data
Authors Defining features of Big Data
Bello-Orgaz et al.
(2016)
…data sets that are terabytes to petabytes and even exabytes in
volume, and the huge data needs new tools to capture, store, manage,
and analyze them effectively.
Wamba et al. (2015) A universal approach to managing, processing and analyzing data
that characterize with Volume, velocity, variety, veracity and value
(5Vs), in order to create an effective and successful way to deliver
sustainable value, measure performance, and create a competitive
advantage for the enterprise to win customer satisfaction.
12
Authors Defining features of Big Data
Shin (2015) Big Data resulting from a large amount of raw information
generated, which is collected and analyzed by marketable and system
of government
Marr (2015) Volume, velocity, variety, veracity and value (5Vs)
Boyd and Crawford
(2012)
A cultural, technological and social phenomenon union with
technology, analysis.
Zikopoulos et al.
(2012)
Volume, velocity, variety and value of data (4 Vs)
Laney (2001) Volume, velocity and variety of data (3Vs)
In the context of the definition, There are a lot of organizations that deal with
Big Data, In order to get a handle with complexities introduced by volume, velocity
and variety, (Talend, 2013) considers the following:
i. Walmart collects 2.5 petabytes of data imported into several databases
from 1 million customers every hour.
ii. 40 billion photos that Facebook deals with from the user base.
iii. Decoding the human genome in the beginning took 10 years to process;
now it can be accomplished in one week, by Big Data technology.
iv. Experts estimate that the average hospital have 665 terabytes of patient
data, 80% of which is unstructured data like CT scans and X-rays.
Building on the aforementioned definitions, the researcher argue that Big
Data could be defined as data sources with a very high “volume” with a “variety” of
sources and shapes, flowing, processed and analyzed at high “velocity”, which
require new tools and methods to capture, curate, mange, and process them in an
efficient way, therefore generating economic “value” for the enterprise.
13
2.2.2 Life Cycle and Management of Data Using Technologies:
Khan et al. (2014) thay devised a life cycle of big data management, The
stages of the data life cycle begin with the collection process then filtering data
before entering the analysis period, and then storage, publication, retrieval, and
discovery stages. Data life cycle is transform raw data into published information, as
an important aspect of enterprise data management in a scientific way.
Thay define troubles faceing Organizations with Big Data life cycle:
The process of designing BDM stages like any life cycle, the advanced steps
depend on the step that preceded it, so the beginning must be correct to obtain the
informations required.
1. Raw Data: the increase in data volume, data sources and variety. Data are
generated in multiple forms -structured, unstructured and semistructured-, the
diversity in the form and sources, in addition to the high speed and size of the data
adversely affects data analysis, management and storage.
2. Collection/Filtering/Classification: Data collection is an important stage
to take and know the outlines of the system, where it is determined methods of data
collecting. Huge amounts of data are created in the forms of "log files" data-
Documents are automatically generated and timed for events related to a given
system. Almost all software applications and systems produce log files. This method
is used to collect data by automatically recording from sources.- so its a special
technique used to collect raw data from its sources automatically, considering
process of classification, filteration, and how to divide groups, coding and
knowledge of relation.
3. Data Analysis: At this stage, data analysis has two main things, the first
thing is to understand the relationships among features, and the second is to develop
effective methods of extracting data that enables accurate prediction towards drawing
future vision. The organization is able to obtain Big information that contributes to
building a competitive advantage. Thus, there must be high techniques to deal with
huge data. Available analytical techniques include data mining, visualization,
statistical analysis, and machine learning. Data mining is the process of computing to
14
discover patterns in Big Data sets , It is a basic process where intelligent methods are
applied to extract informations. widely used in fields such as medicine and business.
4. Storing/Sharing/Publishing: After data collection, classification and
analysis, the process of storage, sharing and publishing is for the benefit of the
organization and its stakeholders (eg industries, communities, media). Therefore,
huge datasets must be stored and managed with high confidence, availability, and
accessibility; so the storage process needs large spaces and therefore, searching for
data storage is essential.
5. Security: The biggest challenge for Big Data from a security point of view
is Privacy. Its often contains huge amounts of personal information, so user privacy
is a major concern, due to the large amount of stored data, invasion that affect large
data can impact more devastating consequences, the most damaging is the legal
implications. Thus, organizations must ensure that they have the right balance
between the usefulness of data and privacy, integrity and availability.
6. Retrieve/Discover: Data Discover ensures data quality and value addition.
At this point concerning the added value, its participate in all previous stages,
including also recovery, management, archiving, protection, and illustration. After
data are published, information oriented to decision makers in the organization, to
make the right decisions and to identify their needs and to support the current
superior results and future plans, its important to allow researchers to access this
information and regenerate the data according to their interests.
See: Fig. 1. Big Data Life Cycle and Management of Data Using
Technologies.
15
Figure (1): Life Cycle and Management of Data Using Technologies and Terminologies of Big Data.
Source: Khan et al. (2014)/ Scientific World Journal.
16
2.2.3 Big Data Management challenges
Big Data offering both big opportunities and big challenges through the
overabundance of sources from different domains; For example, the opportunities
include value creation (Brown, et al., 2011), improve supply chain and resource
allocation flexibility (Kumar, et al., 2013). and amusing capabilities of business
intelligence to make better insights for better business decisions (Chen & Zhang,
2014), On the other hand, the challenges such as data integration complications
(Gandomi & Haider, 2015), lack of team-skills,(Kim, et al., 2014), data security and
privacy concerns (Barnaghi, et al., 2013), poor infrastructure and minor data
warehouse (Barbierato, et al., 2014)
Sivarajah, et al., (2017) shows that the potential value of Big Data can not be
detected by simple statistical analysis. In fact, there is an opportunity for this data,
despite the challenges in storage, processing and managing it. Big data requires
highly efficient, scalable and flexible technologies to manage huge amounts of data
well. Thay discussed many different effects that need to be explored in order to
understand the Big Data challenge. These are visible in Fig.(2) show the
classification of BD challenges, its based on three:
a. Data challenges relate to the characteristics of the data itself (e.g. data
volume, variety, velocity, veracity, volatility, quality, discovery and
dogmatism).
b. Process challenges are related to series of how techniques: how to
capture data, how to integrate data, how to transform data, how to select
the right model for analysis and how to provide the results.
c. Management challenges cover for example privacy, security, governance
and ethical aspects.
17
Figure (2): Conceptual classification of BD challenges.
Source: U. Sivarajah et al. / Journal of Business Research (2017)
18
2.2.4 Big Data analytical tools:
Big data analytics tools, are extremely complex, programming intensive, and
require the application of a variety of skills. They have often emerged as ad hoc
platforms and platforms for open source development, and therefore they lack the
support and user-friendliness that vendor-driven proprietary tools possess.
There are a lot of data management tools to choose from, these tools are Map
Reducing, Hadoop, NoSQL, Hive, Zookeeper, HBase, Cassandra, Mahout (P. C.
Zikopoulos, 2013; P. Zikopoulos & Eaton, 2011), are summarized in Table (2.2)
Table (2.2) : Platforms and tools for big data analytics
Platform / Tool Description
The Hadoop
Distributed
File System (HDFS)
its open-source distributed data processing platform, belongs to NoSQL
technologies, HDFS enables the underlying storage for the Hadoop
cluster. It divides the data into smaller parts and distributes it across the
various servers/nodes.
MapReduce MapReduce provides the interface for the distribution of sub-tasks and the
gathering of outputs. When tasks are executed, MapReduce tracks the
processing of each server/node.
Hive Hive is a runtime Hadoop support architecture that leverages Structure
Query Language (SQL) with the Hadoop platform. It permits SQL
programmers to develop Hive Query Language (HQL) statements akin to
typical SQL statements.
Zookeeper Zookeeper allows a centralized infrastructure with various services,
providing synchronization across a cluster of servers. Big data analytics
applications utilize these services to coordinate parallel processing across
big clusters.
HBase HBase is a column-oriented database management system that sits on top
of HDFS. It uses a non-SQL approach.
Cassandra It is designated as a top-level project modeled to handle big data
distributed across many utility servers. It also provides reliable service
with no particular point of failure and it is a NoSQL system.
Mahout Mahout is yet another Apache project whose goal is to generate free
applications of distributed and scalable machine learning algorithms that
support big data analytics on the Hadoop platform.
These are the tools used by different organizations to store and manage the
Big Data. The suitable tools that fitting to working is needed to be understood. Thus
the complexity and type of big data in our institution must be known and then only
these tools can be used.
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The most significant platform for big data analytics is the open-source
distributed data processing platform Hadoop (Apache platform), at first developed
for such routine functions as aggregating web search indexes. It belongs to the class
“NoSQL” technologies, that evolved to aggregate data in unique ways. Hadoop has
the potential to process extremely large amounts of data mainly by allocating
partitioned data sets to numerous servers (nodes), each of which solves different
parts of the larger problem and then integrates them for the final result (Borikar, et
al., & Gahirwal; Borkar, et al., 2012; Ohlhorst, 2012; P. Zikopoulos & Eaton, 2011).
As Figure:(3) indicates, Map-Reduce/Hadoop architecture.
Figure (3): Map-Reduce/Hadoop architecture.
Source: Khan et al. (2014)/ Scientific World Journal.
Hadoop can serve as data organizer and analytics tool. It offers a great
capabilities of possible in enabling organizations to enable enterprises to extract high
value from its data, that was previously difficult to manage.
On the other hand, Hadoop could become a challenge for the organization,
management team, and lack of IT-staff with Hadoop skills, for these are not ready to
fully embrace Hadoop completely. Finally, on the far right, a wide variety of
techniques and technologies has been developed and adapted to aggregate,
manipulate, analyze, and visualize big data in healthcare, so organizations must
address the a key points to be fitting to adoption a big data tools.
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2.3 Palestinian Health systems overview
2.3.1 Health care
Healthcare system is an important element of the public sector, WHO (2013)
has defined healthcare as "all activities aimed at promoting, restoring and
maintaining health" and in recent years has expanded the definition to include the
prevention of household poverty that leads to disease, it includes work on primary
care, secondary care and third care, as well as public health. Health sector institutions
are under growing pressure to coordinate and integrate more efficiently with their
similar across geographic, institutional and professional boundaries (Jawger, 2005).
Therefore hospitals and Primary health care all around the word are "over burdened
and under server pressure" (Clemensen, 2011). Hence, there are many issues in
improving the health like that which is focusing on improving treatment quality and
patient satisfaction (Schweiger, 2007).
2.3.2 Health systems
Health systems have been defined as “all the organizations, institutions and
resources that are devoted to producing health actions” (WHO, 2013), Servicing,
resource production, financing and preserving. Hence, its objectives include
improving the health of the population and meeting their expectations in preparation
for ill expenses, health care system is constantly changing, patients are living longer
and chronic diseases and health problems such as chronic pain, obesity and diabetes
are on the rise (Eide, 2010). On the other hand, medical services are combined
activities including a practitioners (eg, doctor, nurse, technologist, pharmacist, etc.) who
work to provide a type of healthcare service, which are diagnostic activities, directed
towards the patient and his treatment, this is not only a collaborative process among
members, but a very important process of safety, is characterized by the complexity and
high coordinations (Bennani, 2008), its also complicated by a wide range of different
actors such as service receptors, service providers, and health insurance agencies, and
patients receive services provided from numerous individuals and institutions, as well as
health care professionals, hospitals, outpatient care services. (Schweiger, 2007).
21
2.3.3 Palestinian Health systems
The Palestinian Health system is composed of primary, secondary and tertiary
levels of care. Service providers include the Ministry of Health (Borikar et al.2016),
the United Nations Relief and Working Agency (UNRWA), national and
international NGOs, private (for profit) health sector providers. With such multitude
of service providers there are numerous challenges in providing a well-coordinated,
standardized health service provision during “normal” times and frictions are deemed
to exacerbate during emergencies.
Manenti, et al., (2016) argues that the health system in the Palestinian
territory has three distinct political, financial and coordination features.
a. It operates in a context of political instability and conflict, which undermines
effective system governance.
b. Its financial viability is severely constrained by its dependence on donor
funding, which is subject to fluctuations depending on political considerations
c. The coordination and collaboration challenges of implementing Ministry of
Health programmes in the West Bank and Gaza Strip are further impediments
for planning and management of health services under occupation.
These are further compounded by each region’s geopolitical challenges:
while the Gaza Strip is a contiguous territory but closed, the West Bank is
fragmented into dozens of isolated ‘islands’ by the presence of settlements, military
zone, controlled roads and barriers, requiring health services coverage for small and
access-restricted Palestinian communities. Furthermore, the Palestinian health system
includes the six specialized nongovernmental hospitals that developed historically in
east Jerusalem but which are today separated from their catchment areas in the rest of
the West Bank and Gaza Strip by administrative and physical barriers.
The health infrastructure in Gaza comprises of MoH, UNRWA, NGO,
military medical services and numerous private sector health care providers. (30)
hospitals cater for secondary (29) and tertiary (3) requirements, inclusive of a range of
specialized medical facilities like the Ophthalmic Hospital in Gaza or the Al Helal Al
22
Emirati Maternity hospital in Rafah. One of the (31) hospitals, Al Shawa in Beit Hanoun
has been closed since the beginning of 2014.
MoH and WHO monitor (97) primary health care facilities of various service
providers (please see below table for details) that cover the primary health care needs of
(2) million people with different levels of service provision from basic to
comprehensive including (11) MoH facilities with emergency room capacity and (27)
MoH facilities covering reproductive health services.
NGO Primary Health Cinter (PHC) facilities equally cover some emergency
and RH service requirements of the population. Report (2017) see Table (2.3):
Table (2.3): healthcare Hospitals in Gaza strip.
MoH UNRWA NGO MoI Total
Hospitals 13 0 15 3 31
PHC 53 21 19 4 97
The Palestinian MOH is the main health services provider in the Gaza Strip at all
levels of care. The MOH is the authority responsible for supervision, regulation,
licensure, and control of the whole health service.
The MoH employs about (9,536) personnel (physicians, dentists, nurses,
pharmacists, midwives, paramedical and administrative staff) distributed among the
different levels of care (Palestinian MoH annual report, 2017).
In 2016, the number of physicians per 10,000 people was 7.8 across Gaza Strip
(compared to 26.6 in Jordan and 20.2 in Egypt). The number of nurses per 10,000
people was also low at 11.6, compared to the neighbouring countries (23.3 in Jordan and
29.6 in Egypt ). Furthermore, the Palestinian territory suffers from acute shortages in
certain sub-specialties (e.g., oncology).
The ratio of physicians and nurses per 100,000 population is well below that
of neighbouring countries Table(2.4).
Table (2.4): Ratio of healthcare providers to population - a regional comparison Country Physicians
per 100 000 population
Nurses
per 100 000 population
Gaza Strip 220 340
Egypt 202 233
Jordan 266 296
Note: extracted from MoH annual report,2016, Palestinian Central Bureau of Statistics, 2016.
23
In 2016, according to the Ministry of Finance, the MoH expenditure
amounted to US $ (171.8) million in Gaza Strip, representing 8.75% of the total
Palestinian National Authority budget. Salaries represent 48.65%, drug and
vaccination 21.25%, treatment abroad 15.55%, medical supplies 5.65%, laboratory
1.4%, and the other utilities 7.5% (WHO, 2016).
Recently the MoH faced an increase in the number of patients referred abroad
for hospitalization and consultation due to the shortage of some medical specialties,
lack of sophisticated diagnostic aids, and an increase in the number of bad causalities
from domestic and Israeli military aggression (MoH, 2016). This increased the
burden on the Ministry budget worsening its ability to address the increasing
demands of the population for services.
Mosbah (2010) in his thesis agrued that Palestinian health institutions
concerning quality, several deficiencieswere identified, including the absence of
performance indicators to support decision-making and an insufficient quality
conscience culture.
In fact, (WHO, 2013) studied the main weaknesses pointed in Gaza strip
health care, summarized as follows, the absence of performance indicators for
decision-support, poor levels of governance, Lack of standardized care ,Weak health
information and Lack of support.
Given the information above the challenges of the Palestinian healthcare
system can be summarized as: Shortage of material and financial resources, increase
health and medical requirements, with the trouble in political situation in Gaza
(instability and siege), limited opportunities for medical education and continuing
professional development, lack of accurate and timely health information, the poor
communication between health care providers and the citizens they serve, and the
weak in hospitals integration, that resulting in reduplication of services, depletion of
these hospitals' capacities and their mismanagement with poor services, so big data
applications could help in addressing these challenges.
24
2.4 Palestinian Healthcare Big Data
2.4.1 Healthcare Big Data
Healthcare Big Data hasn’t its own definition, its associated with other
subjects, namely electronic health record (EHR), or electronic medical record
(EMR), its a systematic collection of patient and population health information,
stored and managed in digitally format (Velthuis et al., 2013). Its refers to the patient
data such as national health register data, Lab reports, X-Ray reports, physician notes,
case history, list of doctors and nurses in a hospital, medicine, surgical instruments. (see
table 2.5), its show data sources within a healthcare delivery system. Nevertheless, the
traditional management informayion system role, it is believed that the enterprise has
reached a point where big data may play a major role (Groves et al., 2016). Thus,
healthcare hospitals are depending on big data technology, to collect all these
information about the patient, give caregivers an insightful overview for matching
health care, health management, and patient management.
Table (2.5): Example data sources within a healthcare delivery system.
Data Source Data Generated
Electronic Health Record Clinical documentation, patient history, results reporting, and
patient orders
Laboratory Information
System (LIMS)
Laboratory results (the LIMS is typically interfaced with the
EHR)
Diagnostic or monitoring
instruments
These range from magnetic resonance imaging (MRI) or
computed tomography scanners to ECHO-electrocardiograms
and vital sign monitors. as images (e.g., magnetic resonance
imaging), numbers (e.g., vital signs), text report (result
interpretation). May or may not be interfaced with the HER
Insurance claims / billing Information about the patient during the visit, and the cost of
services. are used to manage the costs of the services provided
to patients, at each clinic or hospital, and follow the payment
process by the patients and the owners of insurance, in this
system records the price of each service,
Pharmacy the introduction of pharmaceutical data, the introduction of
alternative drug data, the introduction of waste units, the
introduction of prescriptions, the recording of drug exchange
movements from the pharmacy and the automatic dispensing
of patients.
Human resources and Lists of employees and their roles and taskes , follow up of
25
Data Source Data Generated
supply chain staff and the work system, personnel affairs, appointment,
upgrade, holiday
Real-time locating systems Positions and interactions of assets and people
(Mimi. 2015 and Velthuis et al., 2013)
Growing datasets, in terms of size and extent of coverage. The healthcare
industry is one of the largest and fastest growing databases, whence of size, diversity
and geographical coverage worldwide. Actually, in 2011, estimated that clinical data
has reached 150 exabytes, representing an increase between 1.2-2.4 exabytes per
year (Kambatla et al., 2014). the rapid increase in volume, and from a variety of
sources, its due to the high interest in data from healthcare, in health research and
treatment, as well as the development of new medical devices, patient sensors, in-
home care devices, and the widespread spread of smart mobile phone wih medical
applications, give rise to sppearance of telemedicine, as well as the emergence of
genetic-related data, are feeding the flow of health data (Crown, 2015). all of that
are feeding the flow of health data. In other words, the heavy adoption of EHR by
care providers health data.(Chen et al., 2012), Therefore, the healthcare industry has
reached a point where Big Data presents a great potential.
2.4.2 The analytics process in Gaza Hospitals
The section was extracted from interviews reports and the web site of MoH
www.moh.gov.ps, its category based up on the main step of analytice process of
data, data generation, data Extraction, data analysis and data visualization.
First: Data Generation
There is a trend for hospitals and healthcare systems to manage clinical and
operational information to Meaningful Use. Several efforts have been being
undertaken to implement HIS more effectively in the Palestinian health system, Its
one of the most important goals achieved recently according to the annual report of
the ITd unit, and the data acquired through interviews reports.
The following parts were implemented: General constants system, User
system, Medical records system, Outpatient system, Reception system, Emergency
26
system, Entry and exit system and Laboratory system at Al-Shifa Medical Complex
and Gaza Euro- hospital, Naser Medical Complex and Rantisi Hospital.
So most Gaza hospitals still working on a host platforms its shaped like Echo-
System, the electronic health record is a central point in which many different
channels of multiple systems is branching. Examples of such platforms are described
below, They include:
a. Electronic Health Records (EHRs): EHRs have become the largest source
of data on patient health. EHRs also called electronic medical record or
electronic patient record, its the cornerstone of any computerized health
system. It divides many channels of information related to the provision of
health care to the patient, EHRs are not much different from traditional paper
records in their function and purpose, but they are completely different in
nature, characteristics, possibilities for use, The benefits used to capture
family, surgical, and medical history, allergies and immunizations,
laboratory results and other condition-specific information. It can record
complete patient data such as personal data, date of birth, job, nationality,
address, nearest relative, infectious disease, patient's (medical insurance),
follower, and tracking the presence of the file anywhere in the hospital.
b. Laboratory Information Management System (LIMS): a LIMS is a
system for managing laboratory sample data, storing interim and final results
of the examination. These data usually contain metadata (date / time of
collection, container type, preservatives, etc.), as well as the result of the
examination required for the patient. a LIMS too so useful for quality
assurance purposes, the system records the consumables for each analysis. its
closely related to the laboratory store, where each analysis are automatically
deducted immediately after implementation.
LIMS also handling the existing Patient List for easy and quick procedure of
any patient, and also provides the possibility of dealing with the digital code
(Barcode), The digital code can be attached to the patient on his own sample
to identify it while the doctor analyzed the sample by passing the digital code
on the barcode reader shows, then the doctor can see data for the patient,
27
identify the normal range, the critical range for this analysis for men and
women, the reference range, transmits the results electronically to the
patient's file on EHRs.
c. Radiology Information System (RIS): These range from magnetic
resonance imaging (MRI) or computed tomography scanners to ECHO-
electrocardiograms and vital sign monitors. This reporting such as Ultra
Sound reports, CT reports and linking these reports with the patient's record
report that is transmitted to the EHR. Its presents and prints detailed and
analytical reports on the types and number of radiations performed in a given
period of time, and displays and prints a statement of patients with specific
radiographs, and provides statistics on the number of radiations according to
the doctor or department.. It also dealing with the existing Patient List for the
ease and speed of any other procedures for the patient.
d. Inpatient Information System: Which can be used to introduce medical
procedures, record patients' admission cases, record outpatient cases, enter
accompanying data, transfer patients from one department to another, locate
the patient by room and bed number, record doctor's data. This system is
linked to the system of accounts for patients to edit the bill for residence in
the internal departments and the system generates detailed reports on the
cases of entry and exit cases classified according to the doctor and the
situation. Provides statistical reports on family and room occupancy and the
rate of entry and exit.
e. Dietary Information System: To identify the meals of each patient, to
determine the meals of each patient, and to calculate the quantities of food
required for each day for all meals of patients in the hospital and also
displays and print detailed reports of the meals required per day and the
number with the number of room and bed for each patient.
f. Pharmacy Information System: It is used for the introduction of
pharmaceutical data, the introduction of alternative drug data, the
introduction of waste units, the introduction of prescriptions, the recording of
drug exchange movements from the pharmacy and the automatic dispensing
of patients, and involving inventory management.
28
g. Insurance claims / billing: These systems are used to manage the costs of
the services provided to patients, at each clinic or hospital, and follow the
payment process by the patients and the owners of insurance, in this system
records the price of each service, and determines the type of treatment of the
patient (Health insurance - state expense - companies - special account), and
follow the financial position of patients, such as patients waiting for
decisions - the patient's daily disbursements. Its calculates the costs of the
services provided to a particular patient and provides a range of bills and
claims to the competent authorities such as the health insurance, state-funded
treatment, the contracting companies and unions. Its also depend on the data
in the extraction of reports of interest to the decision-maker in the hospital,
such as details of the treatment of patients, the disclosure of accounts, and
follow-up patient disbursements daily.
Second: Data Extraction
Electronic Health Information Systems EHRs were not designed to give deep
analysis, mining, linking relationships and patterns. these was not considered, and as
such, the health centers are loses much of its information value, its “get the data out.”
Systems typically support data transmission, where EHR was purchased from the
Jordanian Care Company to European-Gaza hospital and there is an internal E-
hospital system designed from IT development unit in MoH, was setup in Elshefa
and Nasser Hospitals, then spreeds to other hospitals and healthcare centers, its an
eco-system covering all departments in hospital.
The current systems (e-hospital) aims are access to result that generate from
clinical department, and speeds access to information and control work. Its also aims
to store all medical and administrative information about patients on warehouse, Its
can be accessed from any point in the hospital, without back to patient file extraction
from archive.
Data imported from various operating systems: pharmacy, laboratory,
radiology, medical records, and emergency department, in (13) different hospitals,
and (56) Primary care clinics. Data is storing in local hospital, also all MoH
institutions stores in centeral warehouse.
29
The clinical data generated, is estimated from 130 to 150 Gigabytes daily,
presenting an increase between 48-60 terabytes per year, the database contained
information about patient demographic is (11) TB, (0.8) admission/discharge, (5.5)
laboratory, (0.8) pharmacy, (24) radiology, (0,5) emergency department, and (0,5)
diagnosis records.
Participants in the interviews indicated that the rapid groth in healthcare data
led to a shortage of space, they describes it as challenges, here the specialist pointed
about this problem, ".. setup of e-hospital in 2008 … the main problem was the
storage, so we buy "NAS" network-attached storage, its cost is (20,0000$), to
accommodate the volume of data, there is a scaning patients files and X-ray, that
needs big capacity to storing, only clinical lab data from Shifa Hospital daily is 100
MB. as a result, every 6 months data was deleted. This requires tools to continuously
development." Freja, L. (2017, Oct 19). Personal interview.
"...data in hospitals increases day after day, and we move to paperless in the
future - this is directed at the Ministry councils – data volum doubling constantly, for
example, in our hospital (European Gaza Hospital) the size of X-ray added at the
beginning of this year (2017) untilnow (7 months), reachs (3 TB), so these electronic
files need a huge servers and need modern processing techniques to obtain
knowledge..." ALaqad, I. (2017, Oct 22). Personal interview.
"… we have equipped a centeral storge that collects all health data in one
place, database transferred from hospitals online. Its started since 2014, all MoH
systems are currently in one place,… linked to government systems." Younis, H.
(2017, Oct 24). Personal interview.
Third: Data Analysis
Data analysis in MoH done manualy by users level and IS unit. There isn’t
analysis via machine learning, its only process-oriented data.
In the systems, all processes are automated and records, the system
scheduling process of any hospital service. It has helped cargivers in access to
medical history of patients, with secure access to healthcare information, patient
information confidentiality and patient identification. its control over hospital
30
inventory, utilisation of human as well as physical resources is monitored using MIS
reports utilisation of beds, operation theatres, pathology, doctors and nursing staff.
Considering that EHRs were not designed with platform tools that capable of
achieving patterns, metrics and prediction, Here aspiration, how to capture these data
with high quality and mange, analysis, and visualization at a low cost, until now its a
necessarily important mission. MoH Data scientist can serve for data Analysis.
Fourth: Visualization and Reporting
Visualization and reporting phase its as will as data analysis phase its done
manualy by IS units in MoH. There isn’t reporting systems such as interactive
dashboards that provide customized and updated graphical images of critical
performance metrics, trends, benchmarks or goals. It was apparent from the
participants' interviews, as one specialist explains"…current system in the output of
reports does not give scourcard or metrics, …we translate the existing data to the
indicators we want manually, and also its done through Information Systems Unit."
Eelzeer, R. (2017, Oct 19). Personal interview.
Also another participants saied that: "..the issue of indicators is very
important, we have focused recently on 120 performance indicators,...were manually
examined, now data entry computerized that facilitate reaching to data technically…
The issue of getting indicators quickly is our hope for the future." Alwhadi, H. (2017,
Oct 19). Personal interview.
Thus, the current CARE and E-hospital systems only process-oriented data,
healthcare has typically relied on centralized orientation data, undifferentiated report
documents that provide historical healthcare data about patients.
The IS unit in minestry of health introduce healthcare indecators and related
metric, that headway to top management to make decisions, So there is some of IS
unit report, see: www.moh.gov.ps.
31
2.4.2 Challenges in Big Data, pointed by data specialist in MoH:
In figure (5): the researcher have more discution with data specialists in MoH via
interview about issues related to data challenge received in the context of the
theoretical framework, the reviwer appointed the major challenge in Big Data is data
infrastructure, Data visualization, and Data integration.
It has been a major barierrs to adopte based on the experience of the respondents in
the field, The most challenges is Lack of budget, Security concerns, and Poor quality
of data.
Figure (5): Illustrates Challenges in Big Data.(Ranking Challenge in big data in MoH
Hospitals)
Figure (6): Main barriers to setup big data project within hospitals
3
6
0
4
1 1 2
6
4
1
5
2 1
4
2 1
0 0
2 1
5
2 3
0
Data growth Datainfrastructure
Datagovernance-
policy
Dataintegration
Data velocity Data variety Datacompliance
Datavisualization
Most challenges 2nd most challenge 3nd most challenges
0 1 2 3 4 5 6 7 8
Data governance issue
Not a business management priority
Unsure of technology requirements
Lack of budget
Security concerns
Shortage of big data skills
Work culture
Organizational complexity
Lack of leadership and commitment
Poor quality of data
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In this Figure (6) , the researcher have more discution about issues related to
barriers to adopte big data project within hospitals received in the context of the
theoretical framework, that Data governance issue, Not a business management
priority, Unsure of technology requirements, Lack of budget, Security concerns,
Shortage of big data skills, Work culture, Organizational complexity, Lack of
leadership and commitment, Poor quality of data
2.5 Big Data in answering Healthcare systems’ challenges
The application of big data in health care can create value in various areas of
health, and Coakley et al. (2013) given five main value pathways: right living, right
care, right provider, right value and right innovation.
In the first pathway "right living", Big data will allow people to improve their
well-being effectively by identifying and diagnosing high-risk patients for disease
prevention. For example, the technique seeks to improve the self-management of
asthma patients, by providing feedback based on data obtained from the patient's
inhaler sensor.
Secondly in " right care" pathway, Big Data can demonstrate a progress in
evidence-based medicine, through the value that generated by linking relationships
and deep mining of data, and certainly lead to enables caregivers to come up with
best treatment, at the right time.
Big Data prove a progress in evidence-based medicine by ensuring that all
providers are able to arrive at the best treatment possible in a timely manner. For
example, Premier, the US healthcare network, has more than 2,700 hospitals, health
systems and 400,000 doctors. Of course, the network has accumulated a huge
database of clinical, financial and patients data, and the supply chain, which
generates results comprehensive and comparable clinical outcome measures, are
provided on the use of resources and cost.. Thus, these products contributed to the
decision-making process and improved health care operations in approximately 330
hospitals, saving an estimated 29,000 lives and reducing healthcare spending by
nearly (IBM, 2013).
33
Moreover in "right provider" pathway, Big data answerable for improved
performance measurement and enhanced decision-making over the caregiver with
appropriate skills to diagnose and treat the specific patient, to give best outcomes.
The right value pathway is associated with the ability to reduce costs and
provide the same or higher quality levels, thereby maximizing value. An example is
the reduction of waste and abuse in the using tools or consumables by predicting
replacement time from real-time analysis, thus preventing fraud (Raghupathi &
Raghupathi, 2014)
Finally in "right innovation" pathway, Big Data can play a major role in
innovation, especially after studying medical experiments that have occurred or
through simulations, and then concluding improved methods. For example, errors in
clinical processes and the duration of the operation can be reduced by using an
improved trial method. (Manyika J, 2011).
Overall, by generating value through these pathways, Big Data will enable
development of a healthy learning system, where continuously exchange of feedback
between patients and cargivers will improve treatment optimization (Velthuis et al.,
2013). In fact, applied effectively in the United States, the impact in efficiency and
quality generated a value higher than $ 300 billion per year, which reduces expenses
at about 8% (Manyika J, 2011).
2.6 Challenges in Adoption Big Data technology in hospitals:
Several studies in the area of big data projects refer to challenges and barriers
to its successful implementation. particularly relevant in healthcare, cultural and
organizational sluggish (McAfee et al., 2012), besides a shortage of skilled labor
(Chen et al., 2012) are additionally considered top management support barriers.
Moreover, data privacy and security are great obsession for enterprises
(Feldman et al., 2012). And, the high costs of investment in Big Data projects,
especially in stand up stage combined with absence or difficulty of foresight the
future additional value generated.
34
So before costly investments are made it is essential to determine whether an
organization or community is “ready” for Big data projects implementation, Thus
introducing Big Data to the healthcare in Gaza Strip must be preceded by sound
research and also highlights the barriers and facilitators which, when addressed by
policy-makers, can guide successful planning and implementation of Big Data
Management (BDM).
2.6.1 Top management
Top management team includes all managers who responsible for the overall
operations of the organization, They are usually at the top of the organizational
hierarchy, its occupied by a senior managers with their different names; the general
manager, the executive managers and sometimes the board of directors. They hold
specific powers and authority. There responsibilities is about policy and strategy
development, Setting the overall objectives of the organization, drawing future plans
to achieve the desired goals. Chen et al. (2012) defines top management support as a
key success factor in business intelligence and analysis of huge data, and thay
deriving the maximum value is desired from data analysis needs to determine the
policies for configuring and customizing the implementation of analyzes, so that they
meet the objectives of the organization, and therefore this requires the full support
from decision-makers.
The research will asked about the availability of support from top
management in ministry of health. Ifinedo and Princely (2008) states that top
management support is one of the most positive influences on the success of newly
diffusing IT systems. That the implementation of Big Data is relatively unknown to
many organizations raises the importance of having top management support.
When introducing any new technology in the organization, it is important to
obtain top management support. Hence, big data as any new technology needs
support from top management and business leaders.
35
2.6.2 Cultural and Organizational changes
The first question a Data-driven enterprises asks itself is not "What do we
think?" but "What do we know?" These enterprises capture and maximize the value
of their data. Thus, it drives the organization to move away from intuition and
instinct, this also requires organizations to be more dependent on data-driven, than
they are now. Too often, we saw many business today in their reports are taking the
decisions that have been done is supported by a lots of data (McAfee et al., 2012). So
decisions and information must be in the same dimension with business goals and
technology capabilities, its integrated with each, give us the value and advantage to
an enterprise. Besides, the culture of organization must be changed from a decision-
making style based on experience and intuition only (McAfee et al., 2012).
Therefore, manage this change in the organization's culture effectively
towards a Data-driven enterprises, will be serious in order to fully appropriate Big
Data’s benefits. In fact, the biggest challenge in the BD project is to change style of
employees' thinking and degree of resistance or acceptance, in order to embrace the
new system (Dutta & Rose, 2015). At the same time as,
In hospitals, with routine rigidity, its being one of main warning factors to
achieve high levels of adoption innovation and BDM (Martinho et al., 2014).
Baroud (2008) in his thesis about adoption e-health in Palestinian hospitals,
argue that inflexibility is actually existence at the caregiver’s level. In fact, one of the
key obstacles in this project was caregivers’ lack of adoption and resistant to change
(older staff), as will as Cultural tradition and beliefs, Lack of policy, regulation, and
protocols. which is still immature in Palestine.
At the same time, It is essential to make deep changes at the organizational
and structural level of hospitals and necessary to review about the current work
system, this country has reported rigid in coordination mechanisms.(Manenti et al.,
2016) Therefore, cultural resistance is expected to be a challenge.
36
2.6.3 Skilled labor shortage
Big Data projects require a qualified team with capabilities, as creatively
look at the data, understanding how to achieve factors that generating value from
data, especially the presence of IT skills in BDM allied with clinical conception. To
turn medical data into competitive value, that contributes in treatment of patients and
facilitate their services, will needs new skills and a new management style (McAfee
et al., 2012).
With importance of having a qualified IT team, as well as the importance of
having data scientists are most crucial to deal with huge data, and many of the key
must be as statisticians is also important, they support and define with their skills
what will be programmed and obtained from statistics, this requires skills dealing
with modern techniques. Perhaps more important is the existence of are skills in
cleaning and classification huge data sets, which is a difficult task in dealing with -
structured, semi-structured and unstructured -, and IT team needs improveing in data
visualization and reporting skills that also increasing in value.
Along with data scientists, experience in understanding the relationships and
causation between data is required to connect them and gain knowledge and wisdom.
The best data scientists gives a helping to top management in reformulate
their challenges in ways that big data can contribute effectively. Not surprisingly,
individuals with these abilities are rare, but demand is much higher .(McAfee et al.,
2012) "Data Scientists" is the new term for whocapable of working with big data, As
well as associative thinking and creative IT (Davenport, et al., 2012).
Nevertheless, Reasons suggested included a lack of familiarity with the
technology, and fear that their lack of technical skills would result in a loss of power
and position. The suggested solution was to provide gradual training to increase their
awareness, encourage them to engage in the process, to reassure them about their
position, and to explain how their participation will help the organization (Baroud,
2008).
Sham (2014) pointed out in Harvard Magazine, it’s not the amount of data
that makes it a really big deal, it’s the ability to actually do something with it.
37
Assuming, that is, you can harness not only the computational power, but the data
analytics professionals required to sift through the “immensity of staff” to uncover
the relationships meaningful to your business and your customers.
the limited opportunity for health professionals to attend trainings outside and
to get familiar with new medical techniques is also negatively affecting health care
services development in Gaza Strip. (Manenti et al., 2016)
This type of projects requires multidisciplinary teams -those with software
engineers, database experts and data analysts, building network systems, statisticians
skills-, also with an important being Data Scientist. However, Palestinian hospitals
are already facing a shortage of IT staff, especially those related to Big Data.
Therefore, the shortage of skilled labor is expected to be an effective constraint.
2.6.4 High initial investment and unclear benefits
Big data will be a key strategy for both private and public sector, its requires
new methods and technologies to scale or write data fast enough to keep up with the
speed of creation, including the development of storage systems capable of housing
very huge datasets, networking that can provide the scale, machine learning
algorithms. (Kambatla et al., 2014), Additionally as mentioned above, it involves a
enterprises-wide integration and transformation.
The cost of a supported Hadoop distribution requires from organizations
which wants to adopt Big Data will have annual costs estimated at $ 4,000 per node),
so this project needs infrastructure, platforms and softwars estimated between (10 -
50 $ million). Additionally, the real cost is in the process of operation which requires
a team qualified to this mission, and even in the integration of Big Data within the
existing ecosystem .(Bantleman, 2012). In fact, case studies discussed the issue of
cost reduction annually by 3.6%, but these studies are still too early to report on cost
savings and quantitative benefits (IBM, 2013), As well, the investment required to
implement Big Data projects is high, which will lead to a deficit in the budget of
MoH, especially as the Palestinian government continues its efforts to reduce the cost
of health care. Therefore, healthcare budget are particularly sticky with the situation
of government policies which is aimed to depression the cost and expense, and the
38
benefits of this program are not perceived, so budget constrains may obstruct the
management approval. Hence, a high investment to implement such projects is
required.
2.6.5 Health data Privacy and Security
Privacy and security issues - or data protection - have become more and more
urgent in recent times, its the relationship between the stages of the data life cycle
from the beginning of data collection until visualization, and with
the legal and political issues surrounding them. So sensitivity of protection and
privacy concerns begin wherever data are collected (Feldman et al., 2012).
as, accordingly that Big Data an important and complex issue generated from
Internet, medical, financial, educational, plitical sources, so it is almost natural
security and privacy challenges are enormous (Michael & Miller, 2013).
Integrating data from different systems with different security levels, rational
things and privacy settings into one system is causing significant security and
privacy challenges that Big Data projects are facing. The Big Data project teams
need to find solutions to make sure sensitive data is only displayed to people who are
supposed to have access to it (Tankard, 2012), and healthcare sector has a variety of
reasons for placing privacy and security to healthcare big data, including:
a. The tradition legal situation of doctor-patient confidentiality and the related
convention of providers controlling or or deny access to medical records of
patients.
b. Individuals' concerns about disclosure of personal health information to
third parties - outsiders such as the media, criminals, etc.
c. Recognized by affording healthcare data protection under government
regulations that aimed to setup privacy.
On the other hand, Big Data is often associated with granting the right to use
and disclosure, especially in health research that requires the collection,
storage and use of large amounts of health information that can be classified as
personal, many of which are sensitive and perhaps embarrassing. (Mckinsey, 2011).
39
Hence, Today, security is a real concern, with an organization fear unintentional
leakage of data into unauthorized entities (Feldman et al., 2012).
2.7 Chapter Summary
This chapter addressed study literature and demonstrated efforts exerted by other
researchers in the field of adoption Big Data at Hospitals and the importants of how
to use Big Data technology to improve the quality of health care service. Hence, in
this chapter comprehensively classifies the various attributes of Big Data, including
its nature, definitions, management, analysis, challenges. This research also highlight
on Palestinian Health systems, Big Data healthcare and its benefits for hospitals.
Thereafter, development of study model was illustrated followed by detailed
explanation of model variables with elaboration on the key success factors of Big
Data.
40
Chapter 3
Previous Studies
41
Chapter 3
Previous Studies
3.1 Introduction
Scouring through university libraries and online data for the most related and
relevant studies and articles to the topic of this research, a shortage of previous
studies are overviewed, numerous articles presented and arranged in an ascending
order. This research examined related researchs to enrich the theoretical framework
of the current research i.e. constructing the questionnaire and interpreting the
resulting answers. The research clarifies the researchers' various points of view and
opinions on Big Data Management.
In terms of local studies related to Big Data Management, after an exhaustive
search, the researcher scarcely find few related reseach that near the topic of this
research. Thus, it chose to present just one local and two foreign studies that
explored related topics to Big Data Management. Most of them explore mainly the
adaption of Big Data technology and its benefits. At the end of this chapter, this
research comments on all previous studies where there is a comparison between the
current research and the previous literature as well as the most important points that
this research adds. In each of the previous studies, the most important and related
research results and recommendations are provided.
3.2 List of Relevant Previous Studies:
1- (Tesfaye, 2017) Influence of big data and analytics on management control
The purpose of this research was to investigates the influence of big data and
analytics on management control, Qualitative research methodology is designed (25)
interviews are held with five employees from five different organizations, who are
members of the management team or closely involved with data and the
developments of data in their organization. The results of this study show that the
expected impact of big data on management control is not attained in the different
organizations yet. All five organizations have realized that they have to go along
with the developments in the area of data because it is a progressive development in
42
the market, and not going along with these developments could lead to adverse
effects for the organization. The results suggest that big data does not have a
significant effect on management control, but despite the fact that big data currently
has no direct influence on management control, an indirect effect on management
control is suggested to exist. This indirect effect suggests that during the data
projects organizations may shift from the use of a coercive form of control to a more
enabling form of control.
2- (Wang and Hajli 2016) Exploring the path to big data analytics success in
healthcare.
This study proposes a big data analytics-enabled business value model in
which use the resource-based theory (RBT) and capability building view to explain
how big data analytics capabilities can be developed and what potential benefits can
be obtained by these capabilities in the health care industries. Using this model,
research investigate (109) case descriptions from major IT vendors, such as IBM,
SAP, Intel, Microsoft, EMC, CISCO, Oracle and Siemens, covering (63) healthcare
organizations to explore the causal relationships between the big data analytics
capabilities and business value and the path-to-value chains for big data analytics
success. findings provide new insights to healthcare practitioners on how to
constitute big data analytics capabilities for business transformation and offer an
empirical basis that can stimulate amore detailed investigation of big data analytics
implementation.
3- (Verma, 2016) Perceived strategic value- based adoption of Big Data
Analytics in emerging economy: A qualitative approach for Indian firms
The purpose of this study examines the factors that influence
Big Data Analytics (BDA) usage and adoption in the context of emerging
economies, A qualitative exploratory study using semi-structured interviews
collected data from (22) different enterprises in India. A theoretical model of factors
influencing BDA utilization and adoption, two independent research streams – first, the
top managers’ perceived strategic value (PSV) in BDA and second, the factors that
influence the adoption of BDA theoretically. The results showed that the major reason
behind BDA non-adoption is that the organizations did not realize the strategic value
43
(SV) of BDA, and they were not ready to make the changes because of technological,
organizational and environmental difficulties. The findings factors identified as playing a
significant role in organizations’ adoption of BDA were SV of BDA, complexity,
compatibility, IT assets, top management support, organization data environment,
perceived costs, external pressure and industry type.
4- (Almoqren and Altayar, 2016) The Motivations for Big Data Mining
Technologies Adoption in Saudi Banks
The purpose of this study is to explore the factors affecting the adoption and
implementation of data mining techniques to harness big data in Saudi banks. A
quantitative study using surveys was completed by 54 participants who work in data
processing and business intelligence in IT departments in Saudi banks, In this
research, information management, change management, human resource
management, and data coordinating are the main factors that influence the adoption
of big data mining. According to the findings, the adoption and implementation of
data mining to harness big data is affected by motivational factors including: system
quality, information quality, service quality and perceived benefits.
5- (Schaeffer et al., 2016) Big Data Management in United States Hospitals:
Benefits and Barriers
The purpose of this research was to examine the emergence of Big Data in the
U.S. healthcare; and to evaluate hospitals’ ability to effectively make use of complex
information. The methodology for this research was a literature review a total of (68)
sources were reviewed. with a semi-structured interview with an expert in Healthcare
Information Technology (HIT), the findings of this study suggest that the adoption,
implementation, and utilization of Big Data technology may have a profound positive
impact among healthcare providers. Cost containment, cost savings, and better patient
outcomes through more successful disease management are among the principal benefits
to be expected. The results also suggested that adoption of Big Data analytics has been
implemented relatively slowly due to numerous barriers, such as security and privacy
concerns, lack of connectivity between disparate HIT systems, and a shortage of
experienced health care informatics personnel.
44
6- (Jebraeily et al., 2016) Electronic Health Records: Critical Success Factors in
Implementation
The purpose of this research was to identify the key success factors of EHR. The
methodology for this research was a cross-sectional survey conducted with participation
of (340) work forces from different types of job from Hospitals. Data were collected
using a self-structured questionnaire, the findings category of critical success factors in
Implementation EHRs, the highest rate related to Project Management (4.62) and lowest
related to Organizational factors (3.98), So the success in implementation EHRs
requirement more centralization to project management and human factors. Therefore
must be Creating to EHR roadmap implementation, establishment teamwork to
participation of end-users and select prepare leadership, users obtains sufficient training
to use of system and also prepare support from maintain and promotion system.
7- (Padberg, 2015) Big Data and Business Intelligence: a data-driven strategy for
e-commerce organizations in the hotel industry
The purpose of this research to focused on creating a practical approach to become a
more data driven organization. research question was: How can an organization start
with Big Data to get more value out of the available data and optimize the Business?
Qualitative research methodology, multiple interviews were conducted. The results of
this thesis indicate that, Big Data is still considered as a new subject and research area,
an organization could start with Big Data, and Decision Making by selecting a test
department with an open-minded and data friendly manager, identifying and selecting
opportunities that can be solved with Big Data, Business Intelligence, and Decision
Making, starting an innovation process with the following steps: experimentation,
measurement, sharing, and replication, train employees about the capabilities of Big
Data, start with Big Data and learn about Big Data tools while implementing and using
them.
8- (Saxena and Sharma 2015) Integrating Big Data in “e-Oman”:
opportunities and challenges
The purpose of this research aims to integrate Big Data in e-government in Oman,
also known as “e-Oman”, wherein Big Data might be better harnessed to tackle real-
time challenges. Qualitative research methodology asserts how integration of Big
45
Data in “e-Oman” may be useful by invoking examples from four short case studies
across different sectors. Findings is the supports the integration of “e-Oman” and Big
Data wherein besides providing smooth public services, the government is
encouraged to forge inter- and intra-ministerial collaboration and public-private
partnership. And the research provides a platform for the policymakers to conceive
of a synchronized programme for integrating “e-Oman” and the Big Data generated
by it. This integration would go a long way in building upon the economy of Oman,
besides providing better public services to the individuals and businesses on a real-
time basis.
9- (Andersson, 2015) One Step Towards Creating Value From Big Data - A
Case Study on E.ON Elnät
The purpose of this study is to explore the first steps organizations can take in
creating value from Big Data. Qualitative research methodology conducted in a way
with an inductive approach so (12) interviews were held with Big Data experts and
organizations working with Big Data in E.ON Elnät company. The results of this
thesis indicate a Big Data implementation phase can be viewed as an organizational
change, where top management support, cross functional teams and the supply of
competence are essential in order for the implementation to become successful. To
create value, these have been defined as prerequisites for a Big Data solution.
Finally, organizations should develop an ethics strategy regarding the use of Big
Data in order for customers and employees to feel secure in sharing and handling the
personal data. In conclusion, Process Analytics, Customer Analytics and an ethics
strategy are value creators within the field of Big Data Analytics.
10- Aguiar, (2015) titled "Portuguese hospitals main challenges in implementing
Big Data projects for early detection of adverse events"
The objectives of this research is to understand the main barriers in applying
Big Data project for early detection of adverse events such as nosocomial infections
in Portuguese hospitals, the reseach used online surveys were distributed to
caregivers and managers, (89) answers was from caregivers and managers. And
interviews made as complement were undertaken with (8) from caregivers and
managers. The findings of the research, knowledge is low regarding Big Data, which
46
can create difficulties in understanding how to take advantage from the big data
project in the hospital, There is a shortage of “Data Scientists”, As they have critical
skills in dealing with big data project, it's found a real barrier in detection of
advantage from the investment in Big Data project, especially with the high initial
cost. The research recommended that organizational change is a prerequisite for
adapting the new system, resistance from caregivers is unacceptable and health staff
should be educated about the importance of this system. Furthermore, data security
and privacy were not real obstacles but a condition of technology.
11- (Park et al., 2015) title "The Factors of Technology, Organization and
Environment Influencing the Adoption and Usage of Big Data in Korean Firms"
This study identified and prioritized the technology-organization-
environment (TOE) factors influencing the adoption and usage of big data in Korean
firms by using the analytic hierarchy process (AHP) model, the data collected from
318 firms, investigates the adoption factors from three contexts, technological
context including technology issues relevant to innovations, organizational context
including internal resources and capabilities, and environmental context including
competitors and industrial policy, the results was: the perception of benefits from big
data and technological capability are identified as the critical determinants of the big
data adoption. The compatibility with existing system, data quality and integration,
and security and privacy are ranked highly in technology context. Management
support and financial investment competence for the implementation and utilization
of big data, and the government support and policy are identified as the adoption and
usage factors from organization and environment.
12- Thunaibat (2014) title " The Extent of Effective E-Business Technologies
Adoption in Saudi Hospitals: An Applied Research on Hospitals in Mecca
Region".
This research aimed to detect the level of E-business technologies adoption
by hospitals in Mecca region from the point of view of IT managers, and to indicate
its obstacles. The research used analytical descriptive methodology , questionnaire
were distributed to (65) hospitals in Mecca region. The research reached a number of
results, there is a low level of E-business systems and technology adopted by
government hospitals. There is a significant relationship between the dependent
47
variables (type, size, accreditation and age of the hospitals) and adoption of E-
business systems, adoption of e-business in hospitals faced a number of obstacles
including financial, administrative, technical and human resources. the research
suggests applied strategies to improve the adoption of E-business technology by the
hospitals.
13- Dweik (2010) titled: "Healthcare Information Systems and their Impact on
Administrative and Medical Decisions: An Applied Research on the European
Gaza Hospital."
The research aimed to investigate the effects of using healthcare information
systems on decision making in the European Gaza Hospital in both administrative
and medical decisions, it also aimed to investigate the use of computerized healthcare
information systems in the European Gaza Hospital in both medical and
administrative activities, also aimed to find out the barriers reducing these effects.
The research used analytical descriptive methodology, questionnaire was a tool to
collect research data, and were distributed to (140) individuals. The research showed
that there are barriers limit the effectiveness of HIS, including: Lack of financial
support, lack of providing adequate training, lack of vision about long term planning
of E-health application. The research recommended strengthening the strategic vision
about long term planning of E-health applications, and put the E-health in high level
of the national priorities, and the necessity to build an integrated electronic health
system nationwide, and linking hospitals by computerized health information
systems.
14- Baroud, (2008) titled “How Ready are the Stakeholders in the Palestinian
Health Care System in the Gaza Strip to Adopt e-Health?”
The objectives of this research is to mexplore stakeholder readiness in the
Palestinian healthcare system in the Gaza Strip to adopt e-health, to understand the
facilitators and barriers of this process, and to know the best e-health solutions
required to meet their needs. Four healthcare facilities were selected and from each
facility a patient, a practitioner, a management member, and a member of the public
were identified for interview and five focus groups were conducted following the
interviews; at least one in each healthcare facility. The findings of the research show
that stakeholders in the Palestinian healthcare system in the Gaza Strip are ready to
48
adopt e-health. This research provides a valuable resource for those involved in
service planning by increasing understanding of the process needed to introduce e-
health to the Palestinian healthcare system in the Gaza Strip, and may demonstrate
value in other developing countries. Stakeholders in Gaza believe this understanding
will assist decision-makers at all levels to structure future e-health programs in a
meaningful and effective way.
3.3 Commentary
The following can be concluded from previous studies:
a. Big Data as a topic is still new in the Arab countries, and most of research and
articles studies took place in foreign countries.
b. There is no published paper or academic research dedicated in Palestine, which
deals with the topic of Big Data.
c. Big Data can be applied in healthcare system.
d. There have been several successful implementations of HMIS in hospitals.
e. There are critical obstacles that prevent organizations from adopting Big Data.
3.4 Chapter Summary
This chapter has listed a number of previous studies deal with adoption of BD
at healthcare, its covered several aspects of matching and mismatching between the
current study and other studies in terms of environment, methodology, variables
studied and data analysis tools used to test gathered data, then lessons learnt from
previous studies were shed light on via standing on benefits of reviewing literature.
Finally, it emphasized what makes this study distinguished.
49
Chapter 4
Methodology
50
Chapter 4
Methodology
4.1 Introduction
This chapter describes the methodology used in research, methods of data
collection and identification of the research population. As well as explaining steps
to set up search tools. Questionnaire which distributed to respondents, and The
validity and reliability of this questionnaire has been measured to ensure the safety
and clarity of its paragraph, as well as determine the statistical methods and tests,
that used in the analysis of research results, and in testing their hypotheses, and
analysis of the population characteristics, used by the statistical system SPSS. Also
interviews, used to have more complete answers and depth of the information.
4.2 Research methodology
In order to achieve the objectives of this study, the researcher uses the
descriptive analytical methodology as it has been found the dominant among other
methodologies used to study Big Data adoption, It is used to inform business
decisions, policy formation, communications and research. Used quantitative and
qualitative research methods for testing and proving hypotheses approach as survey
and interview and focus group is of the most effective tools in IS researches (Sequist
et al., 2007). And it was found that mixed guestionaiaire and interview as study tool
is the most dominant design (used in 12 papers, questionnaires only in 3 papers and
only interview in 8 other papers). This design is best for assessing new technology
adoption.
The researcher reviewed a number of previous studies, papers and articles in
order or identify studied areas and stand on the best variables to address in this
research. Furthermore, the researcher developed a questionnaire and interview as a
data collection tools to survey and analyze attitudes of IT staff toward the adoption
of big data, the interviewer may explain the concept of Big Data and clarify
misunderstandings.
51
Wright (1995) states that “By combining qualitative methods to quantitative
methods, the resulting research will be much more meaningful and will have a
greater probability of being valid, of actually measuring what it purports to
measure”. So in this reseach, both qualitative and quantitative approaches were
adopted at different stages of research process.
4.3 Research tools
After reviewing the literature and interviewing the specialists, the questionnaire
and interviews is the most appropriate tools for this research
4.3.1 Questionnaire Design and Content
This questionnaire comprised two main parts, part-I covered the demographic
traits of the respondent such as age, sex, specialization, experience…etc. while part-
II covered the measurement of all study variables. Seven-degrees Likert-type attitude
scale together with a set of 68 paragraphs were used to draw attitudes of respondents
toward the five study variables. Likert scale is a psychometric scale that has multiple
categories from which respondents choose to indicate their opinions, attitudes, or
feelings about a particular issue.
The questionnaire was first designed based on tested and validated measures
inherited from previous studies, top management support paragraphs for example
were extracted from Mir et al. (2014), Sivarajah et al. (2016), and Andersson, (2015).
Similarly, paragraphs of culture and organizational factors where extracted from
Safdari et al. (2015), Aguiar, (2015), and Sivarajah et al. (2017). IT skills staffe
paragraphs, were drawn from Jebraeily et al., (2016), Martinho et al., (2014). and
Aguiar, (2015), and questions Security and Privacy were taken from Halaweh et al.
(2015). Finally, paragraphs of cost constrainswere drawn from Park et al., (2015)
and Aguiar, (2015). These measuring paragraphs were then amended and customized
to fit with the nature and position of the current study. Next, the developed
questionnaire was presented to 8 experts to criticize and comment on its paragraphs
before being, comments and recommendations were implemented. Thereafter, the
final version of the questionnaire was eventually produced. The questionnaire was
initially designed in English (see Appendix A), then it was translated into Arabic (see
52
Appendix C) to overcome any miscommunication with the target sample. The
questionnaire is provided with a cover letter which explains the purpose of this
research, the aim of the research and the privacy of the information in order to
encourage high response. The questionnaire is composed of three parts as follows:
First Part: General Personal Information, which consists of (7) items.
Second Part: The adoption of big data technology, which consists of (11) items.
Third Part: consist of five sections as the follows:
First section: Top management support of the big data technology, It
consists of (10) items.
Second section: Cultural and organization with big data technology. It
consists of (12) items.
Third section: Skills of IT staff gaza health care. It consists of (11) items.
Fourth section: Security and Privacy effectiveness in adoption of big data.
Itconsists of (9) items.
Fifth section: Cost of big data adoption. It consists of (8) items.
This questionnaire was drawn up, consideration was given to the formulation of
questions covering all aspects of literature review, and to meeting all the
requirements and variables that affect the hypotheses of the research, taking into
account that most of the questions are clear, easy and quick to answer, and easy to
analyze. The questionnaires were distributed personally to the population.
4.3.2 Qualitative complement- interview Design and Content:
A qualitative complement- interview as a data collection tool from Data Scientist,
sited to enrich the assessment of Big Data’s challenges and opportunities, and to
provides more complete answers and depth of the information, its designed in the
English language (see Appendix D), where extracted from Khan, et al. (2014) and
Baroud, (2008), were then amended and customized to fit with the nature and
position of the current study. The interview was approximately half hour in length,
and was audio-taped for later transcription, its composed of three parts as follows:
First section: describes participant’s background (i.e.,age, gender, education,
profession, and numbers of years at work).
53
Second section: asked about main challenges and barriers to dopte Big Data, relating
to data managmet, top management support, culturel organizational, IT-skills,
security/privacy and Budget constrain.
Third section: asked participant’s to ranking main barriers to setup big data project
within hospitals, and the most challenge in hospitals data.
4.3.3 Research Stages & Procedure:
The research approch was executed as three phases:
Phase one: includes the literature review, completion the objectives of the
study, identification of the variables and development of the theoretical framework,
carried out the exploratory research to identify the nature of the data required for the
research and tried to define the problem more exactly.
Phase two: includes tools setup, identify main fields of the questionnaire and
items for each field, identify main question in preparing interview for use in
collection data. Then, distributed tools to the referees, and prepare the final form of
the questionnaire. Then, distribute the (questionnaire), and enter the data using SPSS
statistical software to analyze their data statistically and get results. Then, interview
was did with Data Scientists to have more complete answers and depth of the
information.
Firstly , Identify main fields of the questionnaire and items for each field, and
then prepare a preliminary questionnaire for use in the data and information
collection. Second, identify main question in preparing interview for use in collection
data. Then, take into account the rules of scientific research from objectivity and
comprehensiveness in the preparation of this (questionnaire- interview). Then, show
(questionnaire- interview) to the supervisor, in order to test their suitability for data
collection, and then modify the questionnaire primarily according to the vision of the
supervisor. Then, distribute the questionnaire to the referees, the population consists
of (10) referees working in management and IT fields inside hospitals. (see appendix
D). Then, prepare the final form of the questionnaire according to the vision of the
referees, see (Appendix C).
Rechearcher obtain the formal book from the Islamic Univercity of Gaza to
facilitate the task of the researcher in the distribution of questionnaires, and conduct
54
the study on the research population. Then, distribute the (questionnaire) to (114) in
the duration from 28 September to 24 October 2017. Questionnaire were retrieved
from (82), in addition to that there are some of research population members who did
not fill the questionnaire that were distributed to them, because some of them have
heavy work and they have no enough time for filling questionnaire. Then, enter the
data of retrieved questionnaires from the respondents and discharged in the computer
using SPSS statistical software to analyze their data statistically and get results.
To have more complete answers and depth of the information, the researcher
done (7) interview made with Data Scientists.
Phase three : Finaly, writing thesis report. result, recommendations and
future research.
.
45
4.3.4 Study Design Stages
Figure (4): Research design- procedure
51
4.4 Data Collection
In order to test the Hypotheses, primary data was collecte through surveys
and structured interviews. The questionnaire designed via stages in selecting question
field and contents, formulation, measurement scales, and the sequence of questions.
Then by participation in conferences, discussions and in depth interview with the IT
specialist in healthcare and the literature support had given the researcher the vision
to translate the factors into Questionnaire with the clear ideas.
4.5 Population and sample size
The population of research selected all of the members of IT staff in
(Information Systems Development Unit, Information Systems Unit and the IT staff
in major hospitals has information system "EL-sheaf-Nasser-European Gaza
Hospitals"), their number is (87), and (55) senior positions, who have relations to the
information system fields at Hospitals are included in this research, so the total
population of research is (142).
Table (3.1) : Research Population
Population Sample Retrieved Questionnaire
Vaild Questionnaire
Percentage
%
Elshefa Hospital 25 20 14 10 70%
European Gaza Hospital 25 25 23 22 92%
Nasser Hospital 27 25 21 20 84%
Information Unit 10 10 5 4 50%
IT development unit 55 25 19 16 76%
Total 142 114 82 72 71.9%
Table (3.1): show the research population includes
MoH is gradually adopting (E-hospital) HIS system which is currently
implemented at (13) hospitals. Targeted population of this study is the IT staff who
working with big data in hospitals, and the researcher choose the the main three
hospitals "EL-sheaf, Nasser and European Gaza Hospitals" as there case study that
implemented HIS and deal with big data, and choose IT development and
Information units from MoH because it’s role as a central units in HIS project
management in MoH hospitals, so its has a key role in data management of the three
chosen hospitals. The population is selected according to the research variables.
52
Where (114) questionnaires are distributed, (82) are retrieved; In SPSS analysis
stage, (10) questionnaires were eliminated due to seven of them incomplete
information and three of them were filling with (4) neither disagree nor agree. The
final questionnaires that vaild to analysis was (72), as the result, the percentage of
responses is (72%). And interviews structured format, being all person-to-person.
Overall, (7) interviews made to Data Scientists.
4.6 Data Measurement
In order to be able to select the appropriate method of analysis, the level of
measurement must be understood. For each type of measurement, there is an
appropriate method that can be applied rather than others. In this research, Ordinal
scale is a ranking or a rating data that normally uses integers in ascending or
descending order. The numbers assigned to the agreement degree (1,2,3,4,5,6,7), do
not indicate that the interval between scales are equal, nor do they indicate absolute
quantities.
They are merely numerical labels. Based on Likert scale we have the
following table (3.2).
Table (3.2 ): Primary quantitative data will collected through surveys.
Respondent strongly
agree
somewhat
agree
agree neither
disagree nor
agree
Disagree somewhat
disagree
strongly
disagree
Degree 7 6 5 4 3 2 1
4.7 Test of Normality,Validity and Reliability of Research Tool
To achieve the research goal, The research used data analysis both qualitative
and quantitative data analysis methods. The Data analysis made utilizing (SPSS 22).
the following statistical did To test Normality and Validity of research tool:
1) Kolmogorov-Smirnov test of normality, the p-value for each field is
greater than 0.05 level of significance and closer to 1, then the distribution for each
field is normally distributed. the total p-values (Sig.) is 0.293 and statistic test is
0.97. (see Appendix A1) for more detail
53
2) Pearson correlation coefficient for Validity, through measured the
correlation coefficients between each paragraph in one field and the whole field, the
p-values (Sig.) are less than 0.05, so the correlation coefficients of all field are
significant at α = 0.05, so it can be said that the paragraphs of this fields are
consistent and valid to measure what it was set for. (see Appendix A2)
3) Cronbach's Alpha for Reliability Statistics, this test is used to measure
the reliability of the questionnaire between each field and the mean of the whole
fields of the questionnaire. The normal range of Cronbach’s coefficient alpha value
between 0.0 and + 1.0, and the higher values reflects a higher degree of internal
consistency.
For the fields, values of Cronbach's Alpha were in the range from 0.746 and
0.894. This range is considered high; the result ensures the reliability of each field of
the questionnaire..
4.8 Chapter Summary
This chapter discussed and elaborated on the research design and
methodology followed by the researcher in conducting this study. It also expanded
on study population and sample and illustrated tools and instruments used in data
gathering. Questionnaire and interview tools design was presented in details and
investigation on questionnaire validity and reliability were also thoroughly discussed.
54
Chapter 5
Data Analysis and Result
55
Chapter 5
Data Analysis and Result
5.1 Introduction
This chapter addresses with different stages of data analysis. It explains the
responses of the target population, and at this stage, the collected data are reviewed
using SPSS computer software this chapter will explore the detailed descriptive
statistical analysis for the data acquired from the qstionnaire. Further, the chapter
explore, analysis, and discusses the reseach wich also from interviews. Finally,
shows the result of the test hypothesis, and compare them with previous studies
5.2 Sample characteristics
The first part of questionnaire is demographic variables which contain the
population characteristics, was determined in order to identify the characteristics of
the respondents in terms of the structure of scientific, practical and social. The
repeatability distributions of some of these variables are presented to the following
arrangement: Gender, Qualification, Age, Type of Position, Position, Years of
Experience.
Table (5.1): Illustrates Sample characteristics Gender Frequency Percent
Male 58 80.6
Female 14 19.4
Age Frequency Percent
Below 30 years 11 15.3
From 30 – below40 39 54.2
From40 –below50 19 26.4
Above 50 years 3 4.2
Qualification Frequency Percent
Bachelor 54 75.0
Master 18 25.0
P.H.D 0 0.0
Type of Position Frequency Percent Frequency Percent
Administrative 32 44.4
IT spicalist 40 55.6
56
Position Frequency Percent %
Director 5 6.9
Head of Department 38 52.8
Programmer 19 26.4
Computer Engineers 8 11.1
Other,.. 2 2.8
Work site Frequency Percent %
Information Unit in MoH 4 5.6
Unit of development IT in MoH 16 22.2
Elshefa Hospital 10 13.9
European Gaza Hospital 22 30.6
Nasser Hospital 20 27.8
Years of Experience Frequency Percent %
Less than 5 years 8 11.1
5 – Less than 10 years 18 25.0
10 years to 15 29 40.3
More than 15 years 17 23.6
The table (5.1) shows that folloing:
a) The majority of responders are males at (80,6%) and (19.4%) of the responders
are females.
b) (15.3%) of the responders are Less than (30) years old, (54.2%) are between
(30) to (40), And (26.4%) are between (40) to (50), and (4.2%) are of (50) years
and Older.
c) the majority of responders is Bachelor holders at (75.0%), and (25.0%) of are
Master holders.
d) (44.4%) of the responders are administrative, and (55.6%) are IT spicalist.
e) (6.9%) of the responses Director, (52.8%) are Head of Department, (26.4%) are
Programmer, (11.1%) are Computer Engineers, (2.8%) are the others thay are
Heads of supdepartment.
f) (5.6%) are working in units of Information, and (22.2%) are working in
development IT unit, and (13.9%) of the responses from Elshefa Hospital,
(30.6%) from European Gaza Hospital, and (27.8%) from Nasser Hospital
g) (11.1%) of the responses are Less than (5) years in their Experience, (25%) are
between (5) to (10), and (40.3%) are of (10) to (15), and (23.6%) has years in
their Experience more than (15) years.
57
5.3 Big Data Adoption Level
To achieve the research goal, the researcher would use data analysis both
qualitative and quantitative data analysis methods. The data analysis was made
utilizing (SPSS21). The researcher would utilize the following statistical tools:
i. Parametric Tests (One-sample T test)
ii. Frequency and Descriptive analysis.
These tests are considered appropriate in the case show that the distribution
of the data follow a normal distribution.
Testing paragraphs of each research variables about the average score equal
to answer neutrality (degrees approval medium).
5.3.1 The Readiness to Adoption Big Data Technology
This field is used to know in general to what ready Hospitals in MoH to adopt
Big Data technology in its operations. So the T test is used to know if the mean of
respondent degree reached to medium degree of agree, which it's 4 or not. The results
are shown in the table (5.2).
Table (4.2):Means and Test values for “The Adoption of Big Data”
N
#
Paragraph Mean *Mean
(%)
t- test P-value
(Sig.)
Rank
1. Big Data technology is an attractive
technological option to MoH and to its Hospitals.
5.59 %79.8 13.52 .000 3
2. Big Data technology is an attractive economic
option to MoH.
4.30 %61.4 2.94 .004 8
3. The MoH Focuses on new IT system projects,
which aim to increase the efficiency and quality
of services provided for the patients.
5.54 %79.1 12.65 .000 4
4. The hospitals has a database suitable for all
administrative, medical and technical purposes.
5.25 %75 6.99 .000 6
5. The hospital adopting a local network that allows
all staff to access files in the database and share
data that it possesses.
5.46 %78 8.00 .000 5
6. The hospital adopting techniques that help to
maintain and share knowledge among doctors
and exchange experiences.
3.55 %50.7 -2.06 .043 10
58
N
#
Paragraph Mean *Mean
(%)
t- test P-value
(Sig.)
Rank
7. The MoH Focuses on new IT system projects,
which aim to increase patients satisfaction.
4.53 %64.7 2.91 .004 9
8. The adoption of Big Data technology in hospitals
operations will support quality in health care.
4.83 %69 4.65 .000 7
9. The adoption of Big Data technology in hospitals
operations will bitter support The diagnostic
process.
6.18 %88.2 17.58 .000 2
10. The adoption of big data technology in IT
operations will support the decision-making
process of MoH and hospitals.
6.29 %89.8 18.45 .000 1
Total 5.15 73.5% 11.23 .000
* Mean (%): is calculated as mean/7 where 7 is the upper boundary of the used scale
Table (5.2) show the following:
a. The mean of paragraph “The adoption of big data technology in IT operations
will support the decision-making process of MoH and hospitals” equals 6.29
(89%), Test-value = 18.45 and psitive, P-value = 0.000 which is smaller than the
level of significance α = 0.05. the result conclude that the respondents strongly
agreed to this paragraph.
b. The mean of paragraph “The adoption of Big Data technology in hospitals
operations will bitter support The diagnostic process.” equals 6.18 (88%), Test-
value = 2.91, and P-value = 0.04 which is smaller than the level of significance α
= 0.05 . The sign of the test is positive, so the result conclude that the
respondents strongly agreed to this paragraph.
c. The mean of paragraph “The hospital adopting techniques that help to maintain
and share knowledge among doctors and exchange experiences.” equals 3.56
(50%), Test-value = -2.06, and P-value = 0.043 which is smaller than the level of
significance α = 0.05 . The sign of the test is nigative, so the result conclude that
the respondents disagreed to this paragraph.
d. The mean of the field “Readinss to adoption Big Data Technology” equals 5.15
(73.5%), Test-value = 11.23, and P-value= 0.043 which is smaller than the level
of significance α = 0.05. The sign of the test is positive, so the result conclude
that the respondents agreed to field of “The Adoption of Big Data Technology ".
59
According to statistical analysis, the research is reached to the following conclusions,
There is (89%) of the respondents in MoH, who are senior and ITspicalist, haveing
knowledge about the adoption of big data technology in hospitals operations will
support the decision-making process and will bitter support the diagnostic process,
which is the one of facilitators to adopt Big Data. There is (79%) of respondents see
that Big Data technology is an attractive technological option to MoH and to its
Hospitals. In general, there is (78.7%) of respondents see that MoH ready and care
to adopt the idea of the Big Data technology. On other hand, The MoH has a weak
efforts on adoption tecnological tools that shareing knowledge among doctors and
exchange experiences, where there is an approval by (51%) of respondents that see
the current projects weak in shareing knowledge. In this context, participation argue
that: "…the minister and his agent are supporters, interesting in data management,
But on a general managers levels, there is a minor encouragement towards the
strengthening of information systems. Therefore, in each ministry council, our taske
was to present reports about the role and importance of indicators and information
systems,...so HMIS developments to reached all Hospitals to improve health services,
this has been in the last two years." Alwhadi, H. (2017, Oct 19). Personal interview.
These results came in line with (Saxena and Sharma 2015) research argued
that the government is encouraged to forge inter- and intra-ministerial collaboration
and public-private partnership. And with (Park et al., 2015) saied the government
support and policy are identified as the adoption and usage factors from organization
and environment. And we differs with the findings of (Aguiar, 2015) research, that
knowledge is low regarding Big Data, which can create difficulties in understanding
how to take advantage from the big data project in Portugal's hospitals.
In general, the major respondents see that MoH hospitals ready and thay care
to adopt the idea of the Big Data technology, haveing knowledge about the adoption
of big data technology in hospitals operations will support the decision-making
process and will bitter support the diagnostic process, that agreed with (McAfee et
al., 2012) saied; the first question a data-driven enterprises asks itself is not "What do
we think?" but "What do we know?" These enterprises capture and maximize the
value of their data.
60
5.3.2 Top management support of the Big Data technology.
Table (5.3):Means and Test values for “Top management support of the Big
Data technology”
N
#
Paragraph Mean Mean
(%)
t- test P-value
(Sig.)
Rank
1. Top management informed of ongoing
developments of Big Data technology and the
importance of its use.
4. 8 68.5% 5.98 .000 1
2. Top management concerns to provide the staff with
the needed trainings and skills for any new
technology so as to keep up with development.
3.65 %52.1 -1.48 .096 10
3. Top management develops plans which are flexible
enough to accommodate any changes required by the
adoption of Big Data technology
3.80 %54.3 -1.27 .052 9
4. Top management supports the new technologies
which serve healthcare system.
4.50 %64.4 6.26 .000 2
5. There is a support from top management in IT field
to adopt everything new such as Big Data
technology.
4.35 %62.1 3.29 .002 4
6. Top Management has a future plan to adopt Big
Data Management via its IT tools, and its uses in
operations.
4.22 %60.3 2.42 .018 5
7. Top management has plans to get rid of obstacles
that hinder the use of any new technology at the
Ministry of Health such as Big Data technology.
3.900 %55.7 -.384 .561 8
8. Top management provides the support and the
needed requirements to adopt Big Data technology.
4.06 %58 .290 .773 6
9. The adoption of Big Data technology is included in
Strategic Plan for Ministry of Health.
4.50 %64.4 2.17 .033 3
10. Top management supports a shift policy in all or
some of the IT operations towards Big Data
technology
3.902 %55.7 -.305 .099 7
Total 4.09 58.4% 3.46 .001
61
Table (5.3) show the following:
a. The mean of paragraph #1 “Top management informed of ongoing
developments of Big Data technology and the importance of its use.” equals
4. 8 (68.5%), Test value = 5.98, and P-value = 0.000 which is smaller than
the level of significance α = 0.05. conclude that the respondents agreed to
this paragraph.
b. The mean of paragraph #4 “Top management supports the new technologies
which serve healthcare system..” equals 4.50 (64.4%), Test-value = 6.26, and
P-value = 0.000 which is smaller than the level of significance α = 0.05 . The
sign of the test is positive, conclude that the respondents agreed to this
paragraph.
c. The mean of paragraph #2 “Top management concerns to provide the staff
with the needed trainings and skills for any new technology so as to keep up
with development..” equals 3.65 (52.1%), Test-value = -1.48, and P-value
=0.096 which is more than the level of significance α=0.05 . The sign of the
test is positive, so conclude that the respondents disagreed to this paragraph.
d. The mean of paragraph #3 “Top management develops plans which are
flexible enough to accommodate any changes required by the adoption of
Big Data technology” equals 3.80 (54.3%), Test-value = -1.27, and P-value =
0.052 which is more than the level of significance α = 0.05 . The sign of the
test is positive, so conclude that the respondents disagreed to this paragraph.
e. The mean of the field “Top management support in MoH ready to adoption
the Big Data technology” equals 4.09 (58.4%), Test-value = 3.46, and P-
value=0.001 which is smaller than the level of significance α = 0.05. The
sign of the test is positive, so conclude that the respondents neither disagree
nor agree to field of “Top management support in MoH ready to adoption
the Big Data technology ".
According to statistical analysis, the research is reached to the following conclusions,
there is (68.5%) of the respondents see that top management informed of ongoing
developments of Big Data technology and the importance of its use, and (64.4%) of
respondents view that top management supports the new technologies which serve
62
healthcare system. On other hand, there is a disapproval among (54.3%) of
respondents top management concerns to provide the staff with the needed trainings
and skills for any new technology so as to keep up with development. In addition,
there is (53%) of respondents don't see that top management develops plans which
are flexible enough to billet any changes required by the adoption of Big Data
technology. In general, there is a medium approval among (58.4 %) of respondents
that top management support in MoH ready to adoption the Big Data technology”
For further discussion, the research asked participants in interviews about top
management support, which was “What are the Challenges and Barriers for Gaza
Hospitals to adopt Big Data? Relating to top management support.” Thus,
participants described this issue, and the research concentrates on most related
comment, conseders in the following:
A participant added a different perspective, describing why there is weakness
to adopt Big Data technology from top management in MoH, as is apparent in the
opinion of respondents from survey, argue that: "MoH has its slogan "patient first"
not the data first, and I,m completely agreed with it, we promote the patient problem,
any attention should be on the patient and then to the logistics units, Decision
makers considers Clinical Units as essential, thus their attention towards medical
side." Freja, L. (2017, Oct 19). Personal interview, another participant attributed
this issue to lack of clear vision to development, also that appears busing to meet
medical needs as a priority, said that: "The most important thing for the development
of information technology in our health system, that is there a vision and a strategy?.
Yes, computing processes was adopted, but we haven't a clear vision and a strategy
to development our technique..,that refer to Top Management relevances to medicine
rather than IS. Therefore, this is reflected on MoH decision to adopte Bigdata
technology." Eelzeer, R. (2017, Oct 25). Personal interview. We can say there is no
clear-cut vision to adopt the Big Data on the near future, that associated with the
financial crisis characterized as a reason.
On other hand, another participants disagreed with the survey result about top
management supports, thay pointed there is interest from MoH leaders, and argues
that there is misconception or misunderstanding from intermediate management level
63
in MoH, so Mosa saied: "...there is strong support from the ministry and there is a good
shift in this framework, but the barrier is the financial conditions." Mosa, K. (2017, Oct
22). Personal interview, and Younis confirms this issue, said: "We have agood job in our
system, and have supports from Ministry in projects that come from donors, for example, the
setup of chronic diseases system, was interest from donors, so we requested Hardware for
this process. Therefore, the leadership in the ministry directed us to these projects and there
is support for this issue and facilitating the obstacles, but the problems exist in the financial
level and this limits our expansion.." Younis, H. (2017, Oct 24). Personal interview. And
Alaqad pointed there is interest from MoH leadership on the way to adoption IT tools
to deals with all operations healthcare big data, he said: "... so there is an effort from
the top management to adopt tools that managements huge data" ALaqad, I. (2017,
Oct 22). Personal interview.
These results came in line with (verma,2016) argues that, the major reason
behind BDA non-adoption is that the organizations did not realize the strategic value
(SV) of BDA, and with (Andersson, 2015) result indicates a Big Data
implementation phase can be viewed as an organizational change, where top
management support, cross functional teams and the supply of competence are
essential in order for the implementation to become successful. And illustrate that,
Chen et al. (2012) defines top management support as a key success factor in
business intelligence and analysis of huge data, and thay deriving the maximum
value is desired from data analysis needs to determine the policies for configuring
and customizing the implementation of analyzes, so that they meet the objectives of
the organization, and therefore this requires the full support from decision-makers.
From the comments of the participants, the research concludes and explain some
points, that relating to top management, wich: There is supporters and they're
interested in HMIS, especially in internal development, great effort was made to
develop healthcare management information system by MoH, but did not give
greater attention or priority to acquisition new technologies, there is't a clear-cut
vision to adopt the Big Data on the near future.
64
5.3.3 Cultural and organizational with the Big Data technology.
Table (5.4):Means and Test values for “Cultural and organizational with the
Big Data technology,,.
NO Paragraph Mean Mean
(%)
t- test P-value
(Sig.)
Rank
1. The adoption of huge information technology is
of interest to the Ministry and the General
Directorate of Hospitals
4.88 %69.6 4.108 .000 6
2. Larger, more complex health systems have
proven particularly receptive to the introduction
of technological innovation
5.13 %73.2 6.031 .000 5
3. The attitude of doctors towards the techniques of
big data management is the subject of the
attention of technology experts and the design of
health systems.
5.40 %77.2 6.511 .000 3
4. Health care team familiar to a certain way of
practicing medicine (based on practice,
experience and intuition) this creates a negative
attitude towards big data
5.58 %79.8 10.534 .000 1
5. The organizational structure of Public Hospitals
allows the exchange of information Easily
4.71 %67.3 4.151 .010 8
6. There is mystery of the future vision to adoption
new technology to management big data.
4.58 %65.5 2.662 .000 9
7. Routine actions in health care delay the
transition to big data management
5.28 %75.4 8.616 .001 4
8. Big data management technology can be seen as
a direct attack on doctors' values
4.72 %67.4 3.462 .000 7
9. The use of big data technology has negative
effects on physician time to his patient.
2.72 %38.9 -5.941 .000 12
10. There is an incentive system at the MoH to
speed up the implementation and use the big
data management system
3.03 %43.3 -4.991 .625 11
11. The system of procedures, transactions and
methods used in hospitals is compatible with the
big data technology
3.92 %56.0 -0.491 .000 10
12. There is Lack of awareness in the importance of
applying IT Tools to management hospitals
data.
5.50 %78.6 10.72 .000 2
Total 4.62 %66 6.78 .000
65
Table (5.4) show the following:
a. The mean of paragraph #4 “Health care team familiar to a certain way of
practicing medicine (based on practice, experience and intuition) this creates
a negative attitude towards big data” equals 6.29 (89%), Test-value = 10.534,
and P-value = 0.000 which is smaller than the level of significance α = 0.05 .
conclude that the respondents strongly somewhat agreed to this paragraph.
b. The mean of paragraph #12 “There is Lack of awareness in the importance
of applying IT Tools to management hospitals data.” equals 6.18 (88%),
Test-value = 10.72, and P-value = 0.000 which is smaller than the level of
significance α = 0.05 . The sign of the test is positive, so conclude that the
respondents somewhat strongly agreed to this paragraph.
c. The mean of paragraph #11 “There is an incentive system at the MoH to
speed up the implementation and use the big data management system”
equals 3.03 (43.3%). and P-value = 0.625 which is more than the level of
significance α = 0.05, that indicate this paragraph is not statistically
significant at the level of significance, indicating that the average response to
this paragraph is not fundamentally different from the degree of neutrality 4.
d. The mean of paragraph #9, #10 “The use of big data technology has
negative effects on physician time to his patient."," There is an incentive
system at the MoH to speed up the implementation and use the big data
management system". Respectively for #9, #10 equals 2.72 (38.9%) and 3.03
(43.3%), Test-value = -5.941 and -4.991, and P-value = 0.000 for each
paragraphs, which is smaller than the level of significance α = 0.05. The
sign of the test is negative, so conclude that the respondents disagreed to this
paragraphs.
e. The mean of the field “Cultural and organizational with the Big Data
technology” equals 4.62 (66%), Test-value = 6.78, and P-value =0.000
which is smaller than the level of significance α = 0.05. The sign of the test
is positive, so conclude that the respondents agreed to field of “Cultural and
organizational factor in MoH ready to adoption Big Data technology ".
66
According to statistical analysis, the research is reached to the following
conclusions, there is (79%) of the respondents see that Health care team familiar to a
certain way of practicing medicine (based on practice, experience and intuition) this
creates a negative attitude towards big data. (78%) of the respondents see that there is
lack of awareness in the importance of applying IT Tools to management hospitals
data. And there is an disapproval among, (61.1%) of respondents on the use of big
data technology has negative effects on physician time to his patient.
In this context, The senior of IS in MoH Alwhadi, (2017), argue that
" ...doctors seems hesitant and unwilling to accept healthcare IS applications during
their work practices. In many hospitals, doctors often writes clinical notes on
paper,… the reason related to the doctor-patient time and the doctors wants clinical
data from the system e.g. Lab.Data., that helps in diagnosis. Therefore,
understanding what leads doctors' to accepted, and motivate them to use (IS) is our
interest." Alwhadi, H. (2017, Oct 19). Personal interview. Also the head of
Programming Department, Younis,(2017) emphasized that the technical staff address
the hesitant of the medical staff in using HIS, he shows "…our systems have been
built on (sixi) and persistence frameworks and Web technologies, system can be
integrated to any third party systems transparently., and why we went to the web
Applications? to directed some informations to citizens through which the citizen will
receive his medical evidence - his account number is the ID number. Doctor and
nurse will works on android applications, this is considered the best way to
contribute medical notes writing and insertting into the system via smart tablets and
phones, rather than the process of entering through the computer, its ll solves the
problem of inserting doctor's notes." Younis, H. (2017, Oct 24). Personal interview.
And for example about the resistance to change from any new system, ALaqad,
(2017) explans this situation by an example, he saied: "when we started in linking
digital scan images directly to IS, and the X-ray image visualizes on computer. In the
beginning, we found opposition from the medical staff, but in present day, the
medical staff has asked our IT team to solve problems when the digital X-ray service
breakdown. Where, the past way,was heavy in filming the X-ray imges." ALaqad, I.
(2017, Oct 22). Personal interview.
67
The results shows, (43.3%) of respondents who agree that there is an
incentive system at the MoH to speed up the implementation and use the big data
management system, in this perspective, Freja, (2017) saied that: "There isn't
incentive system directed to caregiver staff, that make paperless..., In this problem,
when an Italian specialist team visit MoH in Gaza and noted the size of achievement
in information system. thay tell us that Italian HIS in hospitals was suffering in
motivate doctors to inters clinical note via system. Thus, thay do it by linking the
salary at the rate of full data -with accuracy- entry into the system." Freja, L. (2017,
Oct 19). Personal interview., and other participants Eelzeer,(2017) argues the
paragraph #2 "Larger, more complex health systems have proven particularly
receptive to the introduction of technological innovation", she saied:
...Organizational structure is so large and all IT teams is spread in the ministry's
institutions and hospitals. There is a great tricky situation rooted to the nature of the
work…,and there is resistance to change especially from caregivers, when moving
from one system to another, it's expected to appear within any new system." Eelzeer,
R. (2017, Oct 25). Personal interview.
In general, there is (60.85%) of respondents see that Cultural and organizational will
facilitator the Big Data technology ". These results came in line with (Andersson,
2015) indicates, Organizations should develop an ethics strategy regarding the use of
Big Data, and the result agree with Aguiar, (2015) the research recommended that
organizational change is a prerequisite for adapting the new system, resistance from
caregivers is unacceptable and health staff should be educated about the importance
of this system, and (Dutta & Rose, 2015) argue that, the biggest challenge in the BD
project is to change style of employees' thinking and degree of resistance or
acceptance, in order to embrace the new system, also (Martinho et al., 2014) in
hospitals, with routine rigidity, its being one of main warning factors to achieve high
levels of adoption innovation.
From the comments of the participants, the researcher concludes and explain some
points, that relating to cultural and organizational factor, wich: There is resistance to
change especially from caregivers, when moving from one system to another, there
isn't incentive system directed to caregiver staff, that make paperless, caregivers
68
culture may hamper to use MIS, a lack of time describes as a reason for not making
use of effectiveness data, weak of motivation to learn and train on adopte Big Data,
the nature of the work at Ministry of Health (working under pressure continuously),
that makes IT teams dealing to solves problems thay received, rather than thinking in
creativity and development, and from the point view of Hospitals, the centralization
of IT teams' work and their relation to the Information Systems Development Unit,
that leds to slowing the hospital problems resolve. and from another perspectivethis,
Information Systems Development Unit, see centralization in it work united the
efforts and ideas to establishing an integrated system for all Ministry hospitals.
5.3.4 IT skills team with the Big Data technology.
Table (5.5):Means and Test values for “IT skills team with the Big Data
technology"
N
#
Paragraph Mean Mean
(%)
t- test P-value
(Sig.)
Rank
1. Big Data technology helps on the
development of IT staff abilities and skills
5.85 %83.5 14.469 .000 2
2. Training provided to staff in the field of IT
enough, and makes them sophisticated and
look forward to some extent to the latest
technology.
4.19 %59.8 .948 .347 8
3. Big Data technology helps on the
development of the spirit of creativity and
innovation.
5.76 %82.2 15.409 .000 3
4. IT staff realize the importance of the
adopting of Big Data at MoH.
5.56 %79.4 11.609 .000 4
5. There is low confidence of HR staff in their
ability to Use of IT applications
4.67 %66.7 3.972 .000 6
6. There is Fear of HR staff from increasing
tasks And administrative burdens when they
using IT system.
4.60 %65.7 3.449 .001 7
7. Hospitals have enough qualified personnel to
develop software and data management
systems.
3.58 %51.1 -1.808 .0051 10
8. MoH has a sufficient number of qualified
personnel to develop the infrastructure of
networks and means of communication.
3.36 %48 -2.819 .006 11
69
N
#
Paragraph Mean Mean
(%)
t- test P-value
(Sig.)
Rank
9. MoH has a performance assessment system
that points a clear criteria for staff ability to
deal with big data tools.
3.78 %54 -1.247 .217 9
10. The staff dissatisfaction and disability to
change is one of the challenges that hinder
the adoption of any new technology (such as
Big Data)
5.35 %76.4 7.909 .000 5
11. IT staff needs training in the Big Data. 6.31 %90.1 23.020 .000 1
Total 4.81 68.8% 10.050 .000
Table (5.5) show the following:
a. The mean of paragraph #11 “IT staff needs training in the Big Data.” equals
6.31 (90.1%), Test-value = 23.02, and P-value = 0.000 which is smaller than
the level of significance α = 0.05. conclude that the respondents strongly
somewhat agreed to this paragraph.
b. The mean of paragraph #1 “Big Data technology helps on the development
of IT staff abilities and skills.” equals 5.85 (83.5%) Test-value = 14.469, and
P-value = 0.000 which is smaller than the level of significance α = 0.05 . The
sign of the test is positive, conclude that the respondents somewhat agreed to
this paragraph.
c. The mean of paragraph #8, #7 “ MoH has a sufficient number of qualified
personnel to develop the infrastructure of networks and means of
communication.”.“Hospitals have enough qualified personnel to develop
software and data management systems.”. Respectively for #8, #7 equals
3.46 (48 %) and 3.58 (51.1%), Test-value = -2.819 and -1.808, and P-value =
.006 and .0051, for each paragraphs, which is more than the level of
significance α = 0.05 . The sign of the test is negative, conclude that the
respondents disagreed to this paragraphs.
d. The mean of the field “Top management support in MoH to adoption the
Big Data technology” equals 4.81 (68.8%), Test-value= 10.050, and P-
value=0.000 which is smaller than the level of significance α = 0.05. The
sign of the test is positive, so conclude that the respondents agreed to field of
“IT skilled labor to adoption Big Data technology in Palestinian hospitals ".
70
According to statistical analysis, the research is reached to the following conclusions,
there is (90.1%), (83,5%) sequents of the respondents see that "IT staff needs training
in the Big Data" and "Big Data technology helps on the development of IT staff
abilities and skills." Also there is a approval among (76.4%) of respondents that The
staff dissatisfaction and disability to change is one of the challenges that hinder the
adoption of any new technology such as Big Data.
In this context, The head of Programming in ITdU Yonis, (2017), argue that "…each
hospital has a set of ecosystems ..., and the number of staff working in IS unit in
hospitals is very low, so setuation is a major problem to us. I got an education
about the subject of Big Data and worked on it, but this opportunity was not
available to our team ... , If it become a job requirement ,we will automatically learn
because our team have a high readiness to acquire skills and training." Younis, H.
(2017, Oct 24). Personal interview. And Eelzeer, illustrates about IT staff, she saied:
"The team did not receive training in Bigdata, ..but when the ministry decided to
move on in building a new system that deals with the Web and Android, our team has
overcome obstacles ,and gained knowledge from internal training, ITd teams
developes new systems (e-hospital) based on (sixi) languge, and connected (e-
hospital) with e-goverment system, that now can deals on tablets and smartphones
and then will spread among the internal public and citizens, the idea of shifting
based on Self-efforts ... our team has a great effort, so our team has ability to learn
big data technology, If there is an opportunity.." Eelzeer, R. (2017, Oct 25). Personal
interview.
Only (51.1%) of the respondents see that Hospitals have enough qualified
personnel to develop software and data management systems, Also There is a (48. %)
of respondents see that MoH has a sufficient number of qualified personnel to
develop the infrastructure of networks and means of communication. In this context,
some participants commented, there is shortage in IT staff, Freja saied: "… there is a
significant shortage in programmers, Currently we hope to hire more programmers
for work…". Freja, L. (2017, Oct 19). Personal interview. And Alaqad show that:
"In terms of big data technology, we dont have skills and training, For example, in
Gaza European Hospital there are 2 programmers and 2 working with networks
71
infrastructure, so we working on processing and follow-up hospital requests. The
hospital runs in emergency condition, The team always works under endless
pressure, we divide the work according to priorities and importance,.." ALaqad, I.
(2017, Oct 22). Personal interview.
In general, there is a intermediate approval of (68.8%) of respondents that the
IT staff at MoH has skills to adopt Big Data. Thus, Alwhadi conclusion this issue,
shows: "We needs training, development, and motivation, although we have our
special teams thay are self-educated, in situations, did,n allow them to have external
participation to gains big data skills, ... information circulation and the new entry
system, it’s a wonderful and proud of our self-effort." Alwhadi, H. (2017, Oct 19).
Personal interview.
These results came in line with (Jebraeily et al., 2016) the research result
shows, the success in implementation EHRs requirement establishment teamwork to
participation of end-users and select prepare leadership, users obtains sufficient
training to use of system and also prepare support from maintain and promotion
system, and agreed with the finding of (Aguiar, 2015) research in this pint, There is a
shortage of “Data Scientists”, As they have critical skills in dealing with big data
project. And (Manenti et al., 2016) argue that, the limited opportunity for health
professionals to attend trainings outside and to get familiar with new medical
techniques is also negatively affecting health care services development in Gaza
Strip. Also Sham (2014) pointed out in Harvard Magazine, it’s not the amount of
data that makes it a really big deal, it’s the ability to actually do something with it.
Assuming, that is, you can harness not only the computational power, but the data
analytics professionals required to sift through the “immensity of staff” to uncover
the relationships meaningful to your business and your customers.
From the results and comments of the participants, the research concludes
and explain some points, that relating to IT skilled staff factor, wich: There is a need
in Big Data knowledge, MoH IT team learns quickly and gets achievement under
pressure, they are haveing self-development to achieve the current HIMS e-hospitals,
and they need to be strengthened in amounts, MoH doesn’t provide the necessary
72
training for the staff on using Big Data, caregivers doesn't plays an active role in
terms of formulating requirements in the development of technical solutions.
5.3.5 Security and privacy with the Big Data technology.
Table (5.6):Means and Test values for “security and privacy with the Big Data
technology"
N
#
Paragraph Mean Mean
(%)
t- test P-value
(Sig.)
Rank
1. The data security is the biggest challenges facing
MoH to adopt any new technology.
6.23 %89 19.189 .000 2
2. The strength of data security depends on the
strength of service provider in terms of security
5.95 %85 14.581 .000 4
3. It can be considered a contract agreement
between MoH and the service provider as a
safety and reliability of the data.
5.25 %75 8.530 .000 7
4. Information security is one of the biggest
challenges to E-hospital.
6.30 %90 19.194 .000 1
5. I expect increased spending on information
security when the adoption of Big data
Management technology in hospitals.
5.90 %84.2 10.447 .000 5
6. The services and applications of Big Data
provided by service providers companies (e.g.
IBM, SAP, Oracle,...) are difficult to hack and
piracy.
4.88 %69.7 5.579 .000 8
7. Commitment to data protection and storage is
essential to successful IT transformation.
6.18 %88.2 20.376 .000 3
8. Security and fear of data breaches is the most
common barrier to expanding mobility.
5.63 %80.4 8.329 .000 6
Total 5.72 %81.7 22.091 .000
Table (4.6) shows the following:
a. The mean of paragraph #5,#1 “Information security is one of the biggest
challenges to E-hospital.” and “The data security is the biggest challenges facing
the Ministry of Health to adopt any new technology.” Respectively equals 6.30
(90. %), 6.23 (89%) Test-value =19.194, Test-value =19.189 and P-value =
73
0.000, for each which is smaller than the level of significance α = 0.05 . conclude
that the respondents is strongly agreed to this paragraphs.
b. The mean of the field “Top management support in MoH to adoption the Big
Data technology” equals 5.72 (81.7%), Test-value = 22.091 and P-value =0.000
which is smaller than the level of significance α = 0.05. The sign of the test is
positive, So conclude that the respondents somewhat agreed to field of “Security
and privacy with the Big Data technology".
According to statistical analysis, the research is reached to the following
conclusions, there is (90%) of the respondents see that the Information security is
one of the biggest challenges to E-hospital and Big Data Management. In general,
there is an approval among (81.7%) of respondents that Security and privacy is
challenges in the adoption of Big Data. The participants commented on the role of
security and privacy issue with big data manage, whether is it a barriers to adoption.
Thus, participants argue this issue, and the research concentrates on most related
comment, in the following, "...there is a difference between security and a privacy,
another look that it does not happen to penetrate and destroy the data. So in the
security issue we seek to non-Hacking data. Therefore, there is a written policy for
the handling of information outside and within MOH." Freja, L. (2017, Oct 19).
Personal interview, " MoH bought a security router from a foreign country and its
delayed receipt to 6 months because the refusal of the Israeli occupation to enter,
and its purchased for security from hacking." Freja, L. (2017, Oct 19). Personal
interview.Form further discussion Alaqad saied: "… there is a second type which is
unintended to give the owner password for more than one person, The information is
leaked...", also he shows:"… There is a written protocol to data governance in terms
of -storage, archiving and retrieval, standards and procedures for use and who has
permission to obtain or carry out specific information and the level of access to
information-." ALaqad, I. (2017, Oct 19). Personal interview.
security is a real concern, Mosa shows that: ".. Security and privacy considered as a
high challenge so we have policies and procedures for accessing and carrying out
the data.". Mosa, K. (2017, Oct 22). Personal interview. And Younis a grees with
Mosa, he saied "Its one of the basics of our work, and we have a policy approved by
74
the Ministry to follow up protection,… but the culture of employees in data
protection are indifferent especially user account and password among employees,
this in the developed countries is a crime, this is like official seals, the users are
responsible.." Younis, H. (2017, Oct 24). Personal interview.
These results agreed with the finding of (Schaeffer et al., 2016) the reseach shows
that, adoption of Big Data analytics has been implemented relatively slowly due to
numerous barriers, such as security and privacy concerns, and agreed with (Aguiar,
2015) research that shows, data security and privacy were not real obstacles but a
condition of technology. Also agreed with (Park et al., 2015) security and privacy
are ranked highly in technology context. as, accordingly that Big Data an important
and complex issue generated from medical sources (Feldman et al., 2012) argues that
it is almost natural security and privacy challenges are enormous, Hence, security is a
real concern, with an organization fear unintentional leakage of data into
unauthorized entities .
From the results and commented of the participants, the research concludes
and explain some points, that relating to security and privacy factor, wich, Security
and Privacy is constrains and challenge, its condition of technology, from IT team
point view, they did a great effort in this matter, considered as the basis, and the
problem in keeping account and password between MoH employees eachother, this
is reflected on confidential of data.
75
4.3.6 Budget constraints and undiscovered business value with the Big
Data technology.
Table (5.7):Means and Test values for “Budget constraints and undiscovered
business value with the Big Data technology,,.
N
#
Paragraph Mean Mean
(%)
t- test P-value
(Sig.)
Rank
1. MoH focuses on modern IT system projects, which
aim to reduce costs.
5.03 71.8 5.325 .000 8
2. The service of Big Data provided ( SAP, BM .. is
less expensive than the old system.
5.31 75.8 7.398 .000 6
3. Not knowing whether the benefits are worth the cost 5.46 78 6.942 .000 4
4. The cost is too high for outsourcing analysis or
operations
5.54 79.1 8.960 .000 3
5. For MoH that are currently using big data, the cost
of IT infrastructure is the main constraint
5.59 79.8 9.286 .000 2
6. The limited budget is it largest barrier to expansion
to big data technology.
5.44 77.7 6.444 .000 5
7. When to adopt Big Data Technology, the cost is
greatly reduced and capital expenditure is converted
in the IT operations to ongoing expenses.
5.03 71.85 5.888 .000 7
8. There is weak financial support for research and
studies in IT development Software and applications
and system designing
5.62 80.28 8.368 .000 1
Total 5.37 76.7 12.994
From Table (5.7) shows the following:
a. The mean of paragraph #8 “There is weak financial support for research and
studies in IT development Software and applications and system designing”
equals 5.62 (80.28%), Test-value = 8.368, and P-value = 0.000 which is smaller
than the level of significance α = 0.05 . We conclude that the respondents
somewhat agreed to this paragraph.
b. The mean of paragraph #5 “For MoH that are currently using big data, the cost
of IT infrastructure is the main constraint.” equals 5.59 (79.8%), Test-value
76
=9.286, and P-value = 0.000 which is smaller than the level of significance α
=0.05 . The sign of the test is positive, so We conclude that the respondents
somewhat agreed to this paragraph.
c. The mean of the field “Budget constraints with the Big Data technology” equals
5.37 (76.7%), Test-value = 12.994, and P-value=0.000 which is smaller than the
level of significance α = 0.05. The sign of the test is positive, So we conclude
that the respondents agreed to field of “Budget constraints and undiscovered
business value with the Big Data technology”.
According to statistical analysis, the research is reached to the following
conclusions, there is (80.3%) of the respondents see that the There is weak in
financial support for research and studies in IT development Software and
applications and system designing, as voiced, (80.24%) of respondents agree that the
the cost of IT infrastructure is the main constraint, and there is an approval (78%) of
respondents see that the system based on NoSQL or Hadoop cluster is claiming cost
In general, there is an approval among (76.7%) of respondents that there is
Budget constraints and undiscovered business value with the Big Data technology”.
So the participants in interviews described budget constraints and undiscovered
business value with the Big Data, when asked about is it challenges and barrier?.
Thus, participants argue this issue, and the research concentrates on most related
comment, in the following, Alwhadi saied: "…Our movement in such projects
depends on the international donor, because the lack of financial resources, and
therefore our interest to provide medicine and attention to health care programs, and
the absence of a local study to review the benefits versus the cost of implementing
Big data management projects." Alwhadi, H. (2017, Oct 19). Personal interview.
And Freja argues that, he saied: "Look, at the infrastructure level, we hope to find
supporters, for example from 2008 until the day If we adopted upon financial
coverage from government, we did not reached this current achievement. The most of
the funding came from donors and supporters." Freja, L. (2017, Oct 19). Personal
interview. And younis assured that, he shows: "..the government in a difficult
financial situation, We depend on donors to implements these projects and try as
much as possible to provide some of hospitals needs through these projects, for
77
example the automation of cancer patients in MIS, we provided several
requirements in the infrastructure was very important to us, and also when mother
and child care file done , its was very important (its cost was high).." Younis, H.
(2017, Oct 24). Personal interview.
These results agreed with the finding of (Verma, 2016) one of the main
factors identified as playing a significant role in organizations’ adoption of BDA
perceived costs, and (Schaeffer et al., 2016) suggest that the adoption,
implementation, and utilization of Big Data technology may have a profound
positive impact among Cost containment, cost savings, and better patient outcomes
through more successful disease management are among the principal benefits to be
expected. Also athe findings of Aguiar, (2015) a real barrier in detection of
advantage from the investment in Big Data project, especially with the high initial
cost. And Thunaibat (2014) saied in his study: adoption of e-business in hospitals
faced a number of obstacles including financial.
From the comments of the participants, the research concludes and explain
some points, that relating to budget constraints and undiscovered business value,
wich: High initial cost of Big Data implementation, with lack of capital resources to
invest in Big Data, MoH is exhausted in meeting the medical needs to hospitals, -
this is in the critical crisis on Gaza Strip by the siege of occupation - the medical
needs to be first priority, lack of feasibility local-studies that show the benefits versus
costs of implementing and using Healthcare Data Management Tools.
78
5.4 Analyzing Hypotheses:
In order to test the fields of research tool (questionnaire), and paragraphs
analysis, parametric tests were used (One-sample T test, Independent Samples T-test,
Analysis of Variance- ANOVA ). These tests are considered appropriate in the case
show that the distribution of the data follow a normal distribution.
5.4.1 Main Hypothesis Test:
The hypothesis stated that there is a significant effect between independent
variables (Top Management Suport, IT skilled labor, Cultural and organizational,
Budget constraints, Security and Privacy.), and the adoption of Big Data in MoH
Hospitals (at level of significance α= 0.05).
By using Stepwise regression the following results were obtained: R Square
=0.767, this means (76.7%) of the variation in the adoption of Big Data in MoH
hospitals is explained by "Top management, Cultural and organizational, Team
IT-skills, Security and Privacy, Cost constraints."
Table (5.8): Stepwise regression
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .881a .767 .743 .327
a. Predictors: (Constant), Top Management Suport, IT skilled labor, Cultural and organizational, Budget
constraints, Security and Privacy.
Table (5.8) shows the Analysis of Variance for the regression model. Sig.
=0.000, so there is a significant relationship between the dependent variable
"adoption of Big Data in MoH hospitals" and independent variables "Top
Management Suport, IT-skilled labor, Cultural and organizational, Budget
constraints, Security and Privacy ".
there is a strong correlation (.881) exist between top management suport, IT
skilled labor, Cultural and organizational, Budget constraints, Security and Privacy,
and adoption of Big Data. Furthermore 76.7% variance is explained by between top
management suport, IT skilled labor, Cultural and organizational, Budget
constraints, Security and Privacy in Big data adoption. Also this effect is significant
also.
79
Table (5.9): ANOVA for Regression No Paragraph Sum of
Squares
Df Mean
Square
F Sig.
Regression 21.551 3 7.184 30.289 .000b
Residual 16.128 68 .237
Total 37.679 71
Table (5.9) shows the regression coefficients and their P-values (Sig.). Based
on the Standardized Coefficients, the significant independent variable is "Top
Management Suport, IT skilled labor, Cultural and organizational, Security and
Privacy, Budget constraints " .
Table (5.10) shows the Analysis of Variance for the regression model. Unstandardized
Coefficients
Standardized
Coefficients
T Sig.
B Std. Error Beta
(Constant) 3.899 .772
5.049 .000
Top Management Suport .334 .051 .595 6.529 .000
Cultural and organizational .189 .079 .217 2.410 .022
IT skilled labor .476 .099 .539 4.809 .000
Security and Privacy -.338- .139 -.260- -2.428- .021
Budget constraints -.335- .093 -.342- -3.600- .001
a. Independent Variable: Top Management Suport, Cultural and organizational, IT skilled labor,
Security and Privacy, Budget constraints .
The regression equation is:
The adoption of Big Data in MoH = 3.899 + 0.334* (Top Management
Support) + 0.189*(Cultural and organizational) + 0.476* (IT skilled labor) -
0.338*(Security and Privacy) - 0.335*( Budget constraints).
Equation shows that one unit change in adoption of Big Data will create
0.334 units change in Top Management Support and 0.189 units change in Cultural
and organizational and 0.476 change in IT skilled labo, Interpretation is true if other
things remain constant. Positive B (0.334, 0.189 & 0.476) values also indicate that
there is a positive relationship among top management support, organizational
culture, IT skilled labor and adoption of big data in MoH implementation, and
negative sign (-0.338 &-0.335) values also indicate that Security and Privacy, Budget
is a barriers to adoption of big data in MoH implementation.
80
5.4.2 Test the hypotheses of the research :
Test hypotheses about the relationship between two variables of the research
variables
a. Null hypothesis: There is no statistically significant relationship
between two variables of the research variables.
b. The alternative hypothesis: There is statistically significant
relationship between two variables of the research variables.
If the Sig.(P-Value) > 0.05 (Sig. greater than 0.05), (according to SPSS21
program results), that It cannot reject the null hypothesis, so in this case there is no
statistically significant relationship between two variables of the research variables.
On other hand, if the Sig.(P-Value) <0.05 (Sig. less than 0.05), that it can reject the
null hypothesis, and accept the alternative hypothesis that there is statistically
significant relationship between two variables of the research variables.
H1: There is a relationship between the availability of top management support
and the adoption of Big Data projects in Palestinian hospitals. (at level of
significance α= 0.05).
Table (5.11):Correlation coefficient between Top management support and the
adoption of Big Data.
P-Value (Sig.) Pearson Correlation Hypothesis
.000 0.726**
There is a relationship between the availability
of top management support and the adoption of
Big Data projects in Palestinian hospitals.
Table (5.11): shows that the correlation coefficient between top management
support and the adoption of Big Data equals 0.726 and the p-value (Sig.) equals
0.000. The p-value (Sig.) is less than 0.05, so the correlation coefficient is
statistically significant at α =0.05. We conclude there exists a significant relationship
between Top management support and the adoption of Big Data.
Hypothesis H1 have addressed the impact of top management support on
adoption Big Data projects in Palestinian hospitals. This emphasizes the great
81
importance of top management support in healthcare data intelligence and analysis,
to determine the policies for configuring and customizing the implementation of big
data, so that they meet the objectives of the hospitals, and therefore this requires the
full support from decision-makers, so top management support is one of the most
positive influences on the success of newly diffusing IT systems. This result agreed
with the results of previous study of These results came in line with Andersson,
(2015) results indicates a Big Data implementation phase can be viewed as an
organizational change, where top management support is essential in order for the
implementation to become successful, and with verma,(2016) argues that, the major
reason behind BDA non-adoption is that the organizations did not realize the
strategic value (SV) of BDA.
H2: There is a relationship between the availability of Cultural and
organizational elasticity and the adoption of Big Data projects in Palestinian
hospitals. (at level of significance α= 0.05).
Table (5.12):Correlation coefficient between Cultural and Organizational
Factors and the adoption of Big Data Adoption .
P-Value
(Sig.) Pearson Correlation Hypothesis
.000 0.116
There is a relationship between the availability of
Cultural and organizational elasticity and the
adoption of Big Data projects in Palestinian
hospitals.
Table (5.12) shows that the correlation coefficient between Cultural and
Organizational Factors and the adoption Big Data equals 0.116 and the p-value (Sig.)
equals 0.000. The p-value (Sig.) is less than 0.05, so the correlation coefficient is
statistically significant at α = 0.05. We conclude there exists a significant relationship
between Cultural and Organizational Factors and the adoption of Big Data.
Hypothesis H2 have addressed the impact of Cultural and organizational
elasticity on adoption Big Data projects in Palestinian hospitals. This investigation
resulted in proving the existence of statistically significant positive impact of
cultural and organizational factors in adoption big data. On the other hand, this study
concluded that There is resistance to change especially from caregivers, when
82
moving from one system to another, there isn't incentive system directed to caregiver
staff, that make paperless, caregivers culture may hamper to use MIS, and a lack of
time describes as a reason for not making use of effectiveness data. These results
came in line with (Andersson, 2015) indicates, Organizations should develop an
ethics strategy regarding the use of Big Data, and the result agree with Aguiar,
(2015) the research recommended that organizational change is a prerequisite for
adapting the new system, resistance from caregivers is unacceptable and health staff
should be educated about the importance of this system.
H3: There is a relationship between the availability of IT skilled labor and the
possibility of implementing Big Data projects in Palestinian hospitals. (at level
of significance α= 0.05).
Table (5.13):Correlation coefficient between skills of IT human resources and
the adoption of Big Data.
P-Value (Sig.) Pearson Correlation Hypothesis
.014 .289
There is a relationship between the availability
of IT skilled labor and the adoption of Big Data
projects in Palestinian hospitals.
Table (5.13) shows that the correlation coefficient between IT skills of
humane resource and the adoption of Big Data equals 0.289 and the p-value (Sig.)
=0.000. The Pvalue (Sig.) is less than 0.05, so the correlation coefficient is
statistically significant at α = 0.05. We conclude there exists a significant relationship
between skills of IT human resources and the adoption of Big Data.
Hypothesis H3 have addressed the impact of IT skilled labor and the
possibility of adoption of Big Data projects in Palestinian hospitals. This
investigation resulted in proving the existence of statistically significant positive
impact of IT skilled labor on adoption of big data. On the other hand, this study
concluded that There is resistance to change especially from caregivers, when
moving from one system to another, there isn't incentive system directed to caregiver
staff, that make paperless, caregivers culture may hamper to use MIS, and a lack of
time describes as a reason for not making use of effectiveness data. These results
came in line with (Andersson, 2015) indicates, Organizations should develop an
83
ethics strategy regarding the use of Big Data, and the result agree with Aguiar,
(2015) the research recommended that organizational change is a prerequisite for
adapting the new system, resistance from caregivers is unacceptable and health staff
should be educated about the importance of this system.
H4: There is a relationship between Security and Privacy and the adoption of
Big Data projects in Palestinian hospitals. (at level of significance α= 0.05).
Table (5.14): Correlation coefficient between Security and Privacy and
Adoption Big Data.
P-Value
(Sig.)
Pearson Correlation Hypothesis
.293 -.018- There is a relationship between Security and
Privacy and the adoption of Big Data projects in
Palestinian hospitals
Table (5.14) shows that the correlation coefficient between Security and
Privacy and the adoption of Big Data equals .-0.18 and the p-value (Sig.) =0.293.
The pvalue (Sig.) is More than 0.05, So the correlation coefficient is ont statistically
significant at α = 0.293. We conclude there isn't relationship between Security and
Privacy Constrains and the adoption of Big Data. about negative signal it means that
means that participants with high scores in one variable have low scores in the other
variable.
Hypothesis H4 have addressed the impact of Security and Privacy and the
possibility of adoption of Big Data projects in Palestinian hospitals. This
investigation resulted in proving the existence of statistically significant negative
impact of Security and Privacy factor on adoption big data. That means Security and
Privacy consider as condition of technology. These results agreed with the finding of Aguiar,
(2015) research that shows, data security and privacy were not real obstacles but a condition
of technology. Also agreed with (Park et al., 2015) security and privacy are ranked highly in
technology context. as, accordingly that Big Data an important and complex issue generated
from medical sources.
84
H5: There is a relationship between the availability Budget and business value
and the adoption of Big Data projects in Palestinian hospitals. (at level of
significance α= 0.05).
Table (5.15):Correlation coefficient between Budget constraints and Adoption
Big Data.
P-Value
(Sig.)
Pearson Correlation Hypothesis
.446 . -.126-
There is a relationship between the availability Budget
constrain and the adoption of Big Data projects in
Palestinian hospitals.
Table (5.15) shows that the correlation coefficient between cost constraints
and the adoption of Big Data equals - 0.126 and the p-value (Sig.) equals 0.446. The
p-value (Sig.) is More than 0.05, so the correlation coefficient is statistically
significant at α = 0.05. We conclude there isn't a significant relationship between
Cost constraints and the adoption of Big Data.
Hypothesis H4 have addressed the impact of Budget constrain and the
possibility of adoption of Big Data projects in Palestinian hospitals. This
investigation resulted in proving the existence of statistically significant negative
impact of Budget constrain value on adoption big data. That means Budget constrain
consider as condition of technology. These results agreed with the finding of (Verma, 2016)
one of the main factors identified as playing a significant role in organizations’ adoption of
BDA perceived costs, Also agree with the findings of Aguiar, (2015) a real barrier in
detection of advantage from the investment in Big Data project, especially with the high
initial cost
5.4.3 Relation of Research Variables
According to statistical analysis, the research is reached to the following conclusions:
1. There is a statistical relation between Top management support and Big Data
Adoption (at the level of significance α= 0.05).
2. There is a statistical significant relation between Culture and Organizational
factors and the adoption of Big Data (at the level of significance α= 0.05).
85
3. There is a statistical significant relation between skills of IT human resources and
the adoption of Big Data (at the level of significance α= 0.05).
4. There is a statistical significant relation between security ad Praivacy and the
adoption of Big Data (at the level of significance α= 0.05).
5. There is a statistical significant relation between cost constrains and the adoption
of Big Data (at the level of significance α= 0.05).
5.5 Chapter Summary
This chapter addressed the data analysis process and concluded study results
and compared results to previous studies conclusions to inspect the degree of
matching among the study outcome and what other previous studies compiled. The
chapter described the demographic characteristics of study sample and discussed
their attitudes towards study variables to explore the degree of agreement with the
conception of study variable and the extent to which they believe conception factors
are true. Thereafter, proposed study model was tested for validity and reliability, both
measurement and structural models were evaluated for consistency and indicator
reliabilities, convergent and discriminant validities, collinearity, coefficient of
determination and path coefficients. Hypotheses testing was then handled followed
by discussion of concluded results and comparison with previous studies.
86
Chapter 6
Recommendations
87
Chapter 6
Recommendations
6.1 Introduction
The aims of this research are to explore the main barriers and opportunities in
the Palestinian healthcare system in Gaza Strip to adopt Big Data, and to know the
best Big Data Management solutions required to meet their needs, the thesis
concentration on measuring the effects of the top management support, culutar and
organizational, It skills of Team, Security and Praivacy, and Cost constrains.
The findings of applied and field research were obtained through collected
questionnaires field research and interview, acceptance operations, conduct
appropriate statistical hypothesis testing, and extraction and presentation of results.
Then make the necessary recommendations and suggestions that would help MoH to
take advantage of Big Data Technology to improve and develop their Hospitals.
Finally, setting of proposals for future studies that could be conducted.
6.2 Recommendations:
Based on previous results, which revealed that there are challenges and opportunities
to adoption of Big Data technology at MoH hospitals; however, there are some of the
recommendations can be formulated to adopt Big Data technology at gaza hospitals
operations, as the following:
1. It's necessary for Top management to have more action in supports the new
technologies which mange data, to turn healthcare information into wisdom.
2. Top management should have a future Project Plans which are flexible enough to
adoption of Big Data technology.
3. It's necessary for Top management to provide the support and the needed
requirements to adopt Big Data Managmant, wich attractive option in achieveing
watchword "Patient First"
88
4. MoH should adopt Big Data in its operations, which it is an attractive
technological option to the hospitals, that will support the decision-making
process and bitter support the diagnostic process.
5. Ensure Top management are committed to the Big Data project and overcoming
the barriers associated with change.
6. MoH should send IT staff to scientific missions to take advantage of
technological developments surrounding Big Data, thay core part of the process.
7. The hospitals should have a performance assessment system that points a clear
criteria for IT staff ability to deal with Big Bata. and that performance system
construe to an incentive system.
8. Engage with caregivers and other end-users. It is fundamental that users can see
its benefits or they will not use it.
9. MoH can purchase tools that reinforce Data security and Praivacy.
10. MoH should marketing Big Data Project to international donor, to covering the
limitation in Budget.
11. MoH should knowing that Big Data Technology greatly reduced the cost and
capital expenditure is converted in the IT operations to ongoing expenses.
6.3 A roadmap for adoption Big Data in Palestinian Hospitals
In this section, research provides a roadmap for the adoption of Big Data in
MoH Hospitals. The roadmap identifies different tasks/activities that need to be
taken up by various stakeholders to adopt Big Data, the required from top
management for addressing the above mentioned challenges, assessing hospital’s
readiness to change:
Steps Activities metric
Formation a technical
committee including:
Technologists,
caregivers and
administrators
this factor is essential to any project's
success and it is not limited to big data, to
setup a clear vision of the objectives of
implementing big data analytics. And
assessing factors, (Policy and regulations,
data qulity, Standards & interoperability,
ICT infrastructure) and to address the fitting
tools to adoption a big data.
Reports
Big Data
proposal
the availability of IT
staff with the required
Scholarship three technologiest to scientific
missions to take advantage of technological
Scholarship
workshops
89
Steps Activities metric
skillset and
competencies in Big
Data
developments surrounding Big Data.
Implementing a workshops with IT staff
about Big Data technology.
the availability of
knowledge regarding
Big Data and its
benefits
Implementing educational lectures to
caregivers and administrators in hospitals
about Big Data.
Publish brochures about Big Data Project
and its tools and benefits.
Number of
participants
Marketing Big Data
Project to international
donor, to covering the
limitation in Budget.
Marketing team from MoH to convinces
donors.
Meeting
ICT infrastructure
Setup
Creation of basic ICT infrastructure
Creation of national secure health net
Creation of storage and exchange
Use of free and open-source software
Tools setup
Setup Policy &
regulations for privacy
security
Purchase of information security equipment
Formation a technical committee
compliance to laws and regulations that
govern individuals or communities’ privacy
and security
Reports
6.4 Future Research
The researcher felt that there is a rare research about the Big Data technology
in the Arab world in general and Palestine in particular, this is because the Big Data
is a new topic in the IT field. So the door is open for more academic research about
this technology. The researcher suggested the following topics which may provide
good research ideas:
1. Conduct a research to measure the Quality of Big Data in Gaza Hospitals.
2. Conduct a research about setup the roadmap for the adoption of Big Data in
Gaza healthcare process.
3. Conduct a research about Integrating Palestinian hospitals through using Big
Data by MoH.
4. Conduct a research about Factors relating to effectiveness data use in
healthcare management.
5. MoH hospitals Big Data Maturity, an approach to assess progress and identify
necessary initiatives.
51
6.5 Conclusion
This research identifies the characteristic of Big Data in healthcare, and
highlights on its opportunities in health system analytics to promote better use and
improve diagnostic process. In addition, the research discusses the major challenges
facing data management in healthcare, integrating diverse data sources, managing
digital privacy and security risks, and acquiring large talent and data tools. Big Data
can effectively address the challenges of current health care systems, but there is
Barriers to the implementation of such projects in the hospitals.
Results: The adoption of Big Data Management was hostaged by factors
relating to research, respondents said that, (58.4 %) Top Management support
adoption of Big Data, (60.85%) Cultural and organizational will facilitator the Big
Data. (68.8%) IT staff at MoH has readiness to adopt Big Data, But a shortage of
"data scientists" has been reported. (81.7%) See that Security and privacy is
challenges in the adoption of Big Data. (76.7%) Budget Limitation arrest the
Orientation toward Big Data.
The research sets some recommendations to Palestine ministey of health that
will facilitate adopte Big Data. First, MoH shoud setup an effective big data
management strategy to address these challenges, and should build capacity for data
management and analytics. Second, Big Data project require IT skills allied with
clinical understanding, and therefore, MoH should send IT staff to scientific missions
to take advantage of technological developments surrounding Big Data. and The
hospitals should have a performance assessment system that points a clear criteria for
staff ability to deal with Big Bata, that performance system construe to an incentive
system. Finally, MoH with Limite budget is not expected to approved Big Data
project, Therefore, the researcher is advised to design and market the project to
donors, for its importance on operations, its an attractive option to the hospitals, that
will support the decision-making process and bitter support the diagnostic process,
especially MoH has IT-Team is amorous to development.
52
Reference
53
The Reference List
Almoqren, N., & Altayar, M. (2016). The motivations for big data mining
technologies adoption in saudi banks. In Information Technology (Big Data
Analysis) (KACSTIT), Saudi International Conference on (pp. 1-8). IEEE.
Aguiar, A. O. B. D. (2015). Portuguese hospitals’ main challenges in implementing
Big Data projects for early detection of adverse events (Doctoral
dissertation).
Bantleman, J. (2012). The big cost of Big Data. E. Savitz, CIO network: Insights and
ideas for technology leaders. Forbes Magazine. Retrieved October, 4, 2012.
Baroud, R. M. (2008). How Ready are the Stakeholders in the Palestinian Health
Care System in the Gaza Strip to Adopt e-Health? (doctor of philosophy),
university of calgary.
Bennani, A., Belalia, M., Oumlil, R. (2008). As ahuman factor, the attitude of
healthcare preatitioners is thr primary step for the e-health: First outcome of
an ongoing study in Morrocco. Paper presented at the communications of the
IBIMA
Blount, Marion, Ebling, Maria R, Eklund, J Mikael, James, Andrew G, McGregor,
Carolyn, Percival, Nathan, Sow, Daby. (2010) Real-time analysis for
intensive care: development and deployment of the artemis analytic system.
Engineering in Medicine and Biology Magazine, IEEE.
Borikar, Shubham, Bhagchandani, Mohan, Kochar, Raunak, Pardeshi, Ketansing, &
Gahirwal, Manisha.(2016) A Survey on Applications of Big Data Analytics
in Healthcare.
Borkar, Vinayak R, Carey, Michael J, & Li, Chen. (2012). Big data platforms:
What's next? XRDS: Crossroads, The ACM Magazine for Students.
Chen, Hsinchun, Chiang, Roger HL, & Storey, Veda C. (2012). Business intelligence
and analytics: From big data to big impact. MIS quarterly.
Clemensen, J., Rasmussen, J., Denning, A., Cragges, M. (2011). Patient
Empowerment and New Citizen Roles through Telehealth Technologies.
proceeding of conerence eTELEMED: The Third International Conference on
eHealth, Telemedicine.
Coakley, M. F., Leerkes, M. R., Barnett, J., Gabrielian, A. E., Noble, K., Weber, M.
N., & Huyen, Y. (2013). Unlocking the Power of Big Data at the National
Institutes of Health. Big Data.
Costa, F.F. . (2014). Big Data in Biomedicine. Drug Discov Today.
Creswell, John W, Hanson, William E, Clark Plano, Vicki L, & Morales, Alejandro.
(2007). Qualitative research designs: Selection and implementation. The
counseling psychologist.
Crown, William H. (2015). Potential application of machine learning in health
outcomes research and some statistical cautions. Value in Health.
54
DAMA. (2009). Data Management Body of Knowledge from
https://www.safaribooksonline.com/library/view/dama-dmbok-data-
management/9781634622479/Chapters-3.xhtml
Davenport, Thomas H, Barth, Paul, & Bean, Randy. (2012). How big data is
different. MIT Sloan Management Review, 54(1), 43.
Dutta, D., & Rose, I. (2015). Managing a Big Data project: The case of Ramco
Cements Limited. International Journal of Production Economics.
Eide, H., Eide, E., Kristjansdottir, O., Dulmen, s. (2010). Mobile health care and
health behavior change- Development of a system for virtual counseling
based on written diary questionnaires and situational feedbak. Paper
presented at the In proceedings of The International Confernce on Universal
Technologies, Oslo, Norway.
Elf, J., & Andersson, K. (2015). One Step Towards Creating Value From Big Data-A
Case Study on E. ON Elnät.
Feldman, Bonnie, Martin, Ellen M, & Skotnes, Tobi. (2012). Big data in healthcare
hype and hope. October 2012. Dr. Bonnie, 360.
Fernández, Alicia, Gómez, Álvaro, Lecumberry, Federico, Pardo, Álvaro, &
Ramírez, Ignacio. (2015). Pattern recognition in latin America in the “big
data” era. Pattern Recognition.
Gandomi, Amir, & Haider, Murtaza. (2015). Beyond the hype: Big data concepts,
methods, and analytics. International Journal of Information Management.
Gantz, John, & Reinsel, David. (2012a). The digital universe in 2020: Big data,
bigger digital shadows, and biggest growth in the far east. IDC iView: IDC
Analyze the future.
Gantz, John, & Reinsel, David. (2012b). The digital universe in 2020: Big data,
bigger digital shadows, and biggest growth in the far east. IDC iView: IDC
Analyze the future.
Groves, P, Kayyali, B, Knott, D, & Van Kuiken, S. The “Big Data” Revolution in
Healthcare, Accelerating Value and Innovation, Centre for US Health System
Reform Business Technology Office; 2013. Mckinsey & Company www.
mckinsey. com.
Groves, Peter, Kayyali, Basel, Knott, David, & Kuiken, Steve Van. (2016). The'big
data'revolution in healthcare: Accelerating value and innovation.
Hubo, Christiane, & Krott, Max. (2013). Conflict camouflaging in public
administration—a case study in nature conservation policy in Lower Saxony.
Forest Policy and Economics, 33, 63-70.
IBM. (2013). Data Driven Healthcare Organizations Use Big Data Analytics for Big
Gains. visit: ibm.com/big-data/healthcare
Ifinedo, & Princely. (2008). The mpact of business vision, top management support,
and external expertise on ERP success. Business Process Management
Journal.
55
Jawger, J., and Monteriro,E. (2005). Realizing organizational benfits with ICT in
healthcare: the challenge of integration. in proceedings of conference
continuity of care, HelsIT 2005.
Kaggal, V. C., Elayavilli, R. K., Mehrabi, S., Pankratz, J. J., Sohn, S., Wang, Y., . . .
Liu, H. (2016). Toward a Learning Health-care System - Knowledge Delivery
at the Point of Care Empowered by Big Data and NLP. Biomed Inform
Insights, 8(Suppl 1), 13-22. doi: 10.4137/BII.S37977
Kambatla, Karthik, Kollias, Giorgos, Kumar, Vipin, & Grama, Ananth. (2014).
Trends in big data analytics. Journal of Parallel and Distributed Computing,
74(7), 2561-2573.
Kaur, PankajDeep, & Monga, Awal Adesh. (2016). Managing Big Data: A Step
towards Huge Data Security. International Journal of Wireless and
Microwave Technologies, 6(2), 10-20. doi: 10.5815/ijwmt.2016.02.02
Khan, Nawsher, Yaqoob, Ibrar, Hashem, Ibrahim Abaker Targio, Inayat, Zakira, Ali,
Waleed KamaleldinMahmoud, Alam, Muhammad, . . . Gani1, Abdullah.
(2014). Big Data: Survey, Technologies, Opportunities, and Challenges.
Scientific World Journal, 2014, 18.
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage
experience and acquisition intention of big data analytics. International
Journal of Information Management.
Manenti, Ambrogio, Goyet, Claude de Ville de, Reinicke, Corinna, Macdonald,
John, & Donald, Julian. ( 2016). Report of a field assessment of health
conditions in the occupied Palestinian territory
Manyika J, Chui M, Brown B, Buhin J, Dobbs R, Roxburgh C, Byers AH. (2011).
Big Data. The Next Frontier for Innovation , Competition, and Productivity.
Margolis, R., Derr, L., Dunn, M., Huerta, M., Larkin, J., Sheehan, J.,…Green, E.D.
(2014). The National Institutes of Health’s Big Data to Knowledge (BD2K)
initiative: capitalizing on biomedical big data. . J Am Med Inform Assoc.
Marr, Bernard. (2015). Why only one of the 5 Vs of big data really matters?
Retrieved 12, 2016
McAfee, Andrew, Brynjolfsson, Erik, Davenport, Thomas H, Patil, DJ, & Barton,
Dominic. (2012). Big data:The management revolution. Harvard Bus Rev.
Michael, Katina, & Miller, Keith W. (2013). Big data: New opportunities and new
challenges [guest editors' introduction]. Computer, 46(6), 22-24.
MOSBAH, AL DEWAK. (2010). the effects of using computerized healthcare
information systems on decision making in the European Gaza Hospital.
Ohlhorst, Frank J. (2012). Big data analytics: turning big data into big money: John
Wiley & Sons.
Padberg, M. D. (2015). Big Data and Business Intelligence: a data-driven strategy for
e-commerce organizations in the hotel industry (Master's thesis, University of
Twente).
56
Raghupathi, Wullianallur, & Raghupathi, Viju. (2014). Big data analytics in
healthcare: promise and potential. Health Information Science and Systems.
Report, Joint Health Sector Assessment. (2014). Health Cluster in the occupied
Palestinian territory (2014) Gaza Strip, .
Rossum, Philips. ( 2013.). Managing Big Data. TDWi Research fourth quarter.
Russom, Philip. (2011). Big data analytics. TDWI best practices report, fourth
quarter, 1-35.
Saxena, Stuti, Saxena, Stuti, Kumar Sharma, Sujeet, & Kumar Sharma, Sujeet.
(2016). Integrating Big Data in “e-Oman”: opportunities and challenges. info,
18(5), 79-97.
Schweiger, A., Sunyaev, A., Leimeister, J., and Krcmar, H. . (2007). Information
systems and healthcare xx: toward seamless healthcare with software agents.
Paper presented at the communications of the association for information
systems CAIS.
Schaeffer, C., Haque, A., Booton, L., Halleck, J., & Coustasse, A. (2016). Big data
management in united states hospitals: Benefits and barriers.
Seri, Paolo, & Zanfei, Antonello. (2013). The co-evolution of ICT, skills and
organization in public administrations: Evidence from new European
country-level data. Structural change and economic dynamics.
Sham, Jonathan. (2014). Why “Big Data” Is a Big Deal. http://harvardmagazine.com.
Shvachko, Konstantin, Kuang, Hairong, Radia, Sanjay, & Chansler, Robert. (2010).
The hadoop distributed file system. Paper presented at the Mass storage
systems and technologies (MSST), 2010 IEEE 26th symposium on.
Sheykhotayefeh, M., Safdari, R., Ghazisaeedi, M., Mohammadzadeh, N., Khademi,
S. H., Torabi, V., ... & Farajolah, S. S. S. (2016). Hospital Information
Systems Implementation: An Evaluation of Critical Success Factors in
Northeast of Iran. Global Journal of Health Science, 9(2), 93.
Sivarajah, Uthayasankar, Kamal, Muhammad Mustafa, Irani, Zahir, & Weerakkody,
Vishanth. (2017). Critical analysis of Big Data challenges and analytical
methods. Journal of Business Research, 70, 263-286.
Talend. (2013). Four Key Pillars To A Big Data Management Solution. Talend.
Tankard, Colin. (2012). Cultural issues in security and privacy. Network security.
Tesfaye, B. (2017). What is the influence or Big Data and Analytics on Management
Control System.
Varga, Mladen. (2010). Challenges of Data Management in Always-On Enterprise
Information Systems Information Resources Management: Concepts,
Methodologies, Tools and Applications
Verma, S., & Bhattacharyya, S. S. (2017). Perceived strategic value-based adoption
of Big Data Analytics in emerging economy: A qualitative approach for
Indian firms. Journal of Enterprise Information Management, 30(3), 354-382.
57
Velthuis, EJ, Malka, ES, & Richards, MS. (2013). ‘Big Data’in Health Care. What
Does It Mean and Will It Make a Difference? Value in Health, 16(7), A479.
Wager, Karen A, Lee, Frances W, & Glaser, John P. (2017). Health care information
systems: a practical approach for health care management: John Wiley &
Sons.
Wamba, Samuel Fosso, Akter, Shahriar, Edwards, Andrew, Chopin, Geoffrey, &
Gnanzou, Denis. (2015). How ‘big data’can make big impact: Findings from
a systematic review and a longitudinal case study. International Journal of
Production Economics, 165, 234-246.
Wang, Y., & Hajli, N. (2017). Exploring the path to big data analytics success in
healthcare. Journal of Business Research, 70, 287-299.
White, Martin. (2012). Digital workplaces Vision and reality. Business information
review, 29(4), 205-214.
WHO. (2013). Universal Health Coverage: Supporting Country Needs. Survey on
Access to Health Services in the Occupied Palestinian Territories.
Zikopoulos, Paul C. (2013). Dirk deRoos, Krishnan Parasuraman, Thomas Deutsch,
David Corrigan, and James Giles. Harness the Power of Big Data—The IBM
Big Data Platform: McGraw-Hill.
Zikopoulos, Paul, & Eaton, Chris. (2011). Understanding big data: Analytics for
enterprise class hadoop and streaming data: McGraw-Hill Osborne Media.
Zillner, S, Rusitschka, S, & Skubacz, M. (2014). Big data story. Demystifying Big
Data with special focus on and examples from industrial sectors. Paper
presented at the EU BIG–European Big Data Public Private Forum.
Aguiar, A. O. B. D. (2015). Portuguese hospitals’ main challenges in implementing
Big Data projects for early detection of adverse events (Doctoral
dissertation).
58
Appendix
59
Appendix
Appendix-A: Test of Normality, Validity and Reliability of Research
Tool Test of Normality for each field:
From Table (A.1), the p-value for each field is greater than 0.05 level of
significance and closer to 1, then the distribution for each field is normally
distributed. Consequently, Parametric tests will be used to perform the statistical data
analysis.
Table (A.1): Kolmogorov-Smirnov test
No. Field No. of
Items
Kolmogorov-Smirnov
Statistic P-value
Readiness to adoption of "Big Data" 11 0.726 0.828
Top Management Support 10 0.919 0.428
Cultural and organizational 12 1.026 0.167
IT skilled labor 11 1.114 0.243
Security and Privacy 9 0.875 0.366
Budget constraints and undiscovered business 8 0.839 0.828
All paragraphs of the questionnaire 61 0.979 0.293
3.3.1 Validity of Research Tool:
It means the validity of questionnaire to measure the questionnaire questions,
which are developed to measure it. There are two methods to ensure the validity of
questionnaire
A. Validity of Referees
The initial questionnaire has been given to a group of referees (see appendix
D) to judge its validity according to its content, the clearness of its items meaning,
fitness to avoid any misunderstanding and to comfort its linkage with the research of
objectives and hypotheses.
B. Validity of Questionnaire
Validity refers to the degree which an instrument measures what it is
supposed to be measuring. Statistical validity is used to evaluate instrument validity,
which includes internal validity and structure validity, To insure the validity of the
questionnaire (internal validity and structure validity), Personal test was used to
measure the correlation coefficient between each paragraph and the whole field.
60
C. Internal Validity
Internal validity of the questionnaire is the first statistical test's that is used to
test the validity of the questionnaire. It is measured by a pilot population, which
consisted of 10 questionnaires through measuring the correlation coefficients
between each paragraph in one field and the whole field.
Table (A.2): Correlation coefficient of each paragraph of " The Adoption
of Big Data Technology " and the total of this field
No. Paragraph Pearson
Correlation
Coefficient
P-Value
(Sig.)
1. Big Data technology is an attractive technological option to the
Ministry of Health and to its Hospitals. .525** .001
2. Big Data technology is an attractive economic option to the
Ministry of Health. .431** .001
3. MoH Focuses on new IT system projects, which aim to increase the
efficiency and quality of services provided for the patients. .376** .000
4. Is MoH pursuing any Big data technologies (such as NoSQL or
Hadoop cluster. .483** .000
5. The hospitals has a database suitable for all administrative, medical
and technical purposes and maintains all the data that is handled. .777** .000
6. The hospital adopting a local network that allows all staff to access
files in the database and share data that it possesses. .464** .000
7. The hospital adopting techniques that help to maintain and share
knowledge among doctors and exchange experiences. .643** .000
8. MoH Focuses on new IT system projects, which aim to increase
patients satisfaction. .796** .000
9. The adoption of Big Data technology in hospitals operations will
support quality in health care. .654** .001
10. The adoption of Big Data technology in hospitals operations will
support bitter support The diagnostic process. .460** .000
11. The adoption of big data technology in IT operations will support
the decision-making process of MoH and hospitals. .435** .000
* Correlation is significant at the 0.05 level
Table (A.2): clarify the correlation coefficient for each paragraph of the" The
adoption of Big Data technology " and the total of the field. The p-values (Sig.) are
less than 0.05, so the correlation coefficients of this field are significant at α = 0.05,
61
so it can be said that the paragraphs of this field are consistent and valid to measure
what it was set for.
Table (A.3): Correlation coefficient of each paragraph of " Top
management support the Adoption of Big Data Technology " and the total of
this field
No.
Paragraph
Pearson
Correlation
Coefficient
P-
Value
(Sig.)
1. Top management informed of ongoing developments of Big Data
technology and the importance of its use. .607
** .000
2. Top management concerns to provide the staff with the needed
trainings and skills for any new technology so as to keep up with
development.
.472**
.000
3. Top management develops plans which are flexible enough to
accommodate any changes required by the adoption of Big Data
technology
.727**
.000
4. Top management supports the new technologies which serve
healthcare system. .705
** .000
5. There is a support from top management in IT field to adopt
everything new such as Big Data technology. .770
** .000
6. Top Management has a future plan to adopt Big Data Management
via its IT tools, and its uses in operations. .800
** .000
7. Top management has plans to get rid of obstacles that hinder the use
of any new technology at the Ministry of Health such as Big Data
technology.
.811**
.000
8. Top management provides the support and the needed requirements
to adopt Big Data technology. .834
** .000
9. The adoption of Big Data technology is included in Strategic Plan for
Ministry of Health. .834
** .000
10. Top management supports a shift policy in all or some of the IT
operations towards Big Data technology .784
** .000
* Correlation is significant at the 0.05 level
Table (A.3) clarify the correlation coefficient for each paragraph of the " Top
Management support adoption Big Data " and the total of the field. The p-values
(Sig.) are less than 0.05, so the correlation coefficients of this field are significant at
α = 0.05, so it can be said that the paragraphs of this field are consistent and valid to
be measure what it was set for.
62
Table (A.4): Correlation coefficient of each paragraph of " Culture
organizational filed to adoption Big Data" and the total of this field
No. Paragraph Pearson
Correlation
Coefficient
P-Value
(Sig.)
1. The adoption of huge information technology is of interest to
MoH and the General Directorate of Hospitals. .562
** .002
2. Larger, more complex health systems have proven particularly
receptive to the introduction of technological innovation. .601
** .000
3. The attitude of doctors towards the techniques of big data
management is the subject of the attention of technology experts
and the design of health systems.
.597**
.000
4. Health care team familiar to a certain way of practicing
medicine, this creates a negative attitude towards big data. .622
** .000
5. The organizational structure of Public Hospitals allows the
exchange of information Easily .634
** .004
6. There is mystery of the future vision to adoption new
technology to management big data. .535
** .000
7. Routine actions in health care delay the transition to big data
management. .621
** .000
8. Big data management technology can be seen as a direct attack
on doctors' values (professional independence, experience,
prestige).
.755**
.000
9. The use of big data technology has negative effects on
physician time to his patient. .496
** .000
10. There is an incentive system at the Ministry of Health to speed
up the implementation and use the big data management
system.
.642**
.000
11. The system of procedures, transactions and methods used in
hospitals is compatible with the big data technology. .252
* .033
12. There is Lack of awareness in the importance of applying IT
Tools to management hospitals data. .403
** .000
* Correlation is significant at the 0.05 level
Table (A.4) clarify the correlation coefficient for each paragraph of the
"Culture organazatonal with the adoption adoption Big Data " and the total of the
field. The p-values (Sig.) are less than 0.05, so the correlation coefficients of this
field are significant at α = 0.05, so it can be said that the paragraphs of this field are
consistent and valid to be measure what it was set for.
63
Table (A.5): Correlation coefficient of each paragraph of " IT skills team
with adoption Big Data " and the total of this field
No. Paragraph Pearson
Correlation
Coefficient
P-Value
(Sig.)
1. Big Data technology helps on the development of IT staff
abilities and skills. .294
* .012
2. Training provided to staff in the field of IT enough, and
makes them sophisticated and look forward to some extent to
the latest technology.
.408**
.000
3. Big Data technology helps on the development of the spirit of
creativity and innovation. .361
** .002
4. IT staff realize the importance of the adopting of Big Data at
the Ministry of Health. .306
** .009
5. There is low confidence of HR staff in their ability to Use of
IT applications. .570
** .000
6. There is Fear of HR staff from increasing tasks And
administrative burdens when they using IT system. .431
** .000
7. Hospitals have enough qualified personnel to develop
software and data management systems. .717
** .000
8. The hospital has a sufficient number of qualified personnel to
develop the infrastructure of networks and means of
communication.
.704**
.000
9. The Ministry of health has a performance assessment system
that points a clear criteria for staff ability to deal with big
data management tools.
.404**
000
10. The staff dissatisfaction and disability to change is one of the
challenges that hinder the adoption of any new technology
(such as Big Data Technology) .450
** .000
11. IT staff needs training in the Big Data. .478**
.000
* Correlation is significant at the 0.05 level
Table (A.5) clarify the correlation coefficient for each paragraph of the " IT
skills team with adoption Big Data " and the total of the field. The p-values (Sig.)
are less than 0.05, so the correlation coefficients of this field are significant at α =
0.05, so it can be said that the paragraphs of this field are consistent and valid to be
measure what it was set for.
64
Table (A.6): Correlation coefficient of each paragraph of " Security and
Privacy with adoption Big Data" and the total of this field
No. Paragraph Pearson
Correlation
Coefficient
P-Value
(Sig.)
1. The data security is the biggest challenges facing the Ministry
of Health to adopt any new technology. .689** .000
2. The strength of data security depends on the strength of
service provider in terms of security .458** .003
3. It can be considered a contract agreement between the
Ministry of Health and the service provider as a safety and
reliability of the data.
.667** .000
4. There is confidence in new technologies and the providers of
these services .190 .241
5. Information security is one of the biggest challenges to E-
hospital .656
** .000
6. I expect increased spending on information security when the
adoption of Big data Management technology in hospitals .588
* .014
7. The services and applications of Big Data provided by service
providers companies (e.g. IBM, SAP, Oracle,...) are difficult
to hack and piracy
.387** .000
8. Commitment to data protection and storage is essential to
successful IT transformation .706
** .000
9. Security and fear of data breaches is the most common barrier
to expanding mobility .597
** .000
* Correlation is significant at the 0.05 level
Table (A.6) clarify the correlation coefficient for each paragraph of the
Security and Privacy with adoption Big Data " and the total of the field. The p-
values (Sig.) are less than 0.05, so the correlation coefficients of this field are
significant at α = 0.05, so it can be said that the paragraphs of this field are consistent
and valid to be measure what it was set for.
65
Table (A.7): Correlation coefficient of each paragraph of " cost
constrains to Adoption of Big Data Technology " and the total of this field
No. Paragraph Pearson
Correlation
Coefficient
P-Value
(Sig.)
1. The Ministry of Health focuses on modern IT system projects,
which aim to reduce costs. .571** .000
2. The service of Big Data provided by Google Inc., (e.g. an e-
mail service - Gmail) at the Ministry of Health is less
expensive than the old system.
.537** .000
3. Not knowing whether the benefits are worth the cost. .799** .000
4. The cost is too high for outsourcing analysis or operations. .460** .003
5. For ministry of health that are currently using big data, the
cost of IT infrastructure is the main constraint. .661** .000
6. The limited budget is it largest barrier to expansion to big
data technology. .390* .014
7. When to adopt Big Data Technology, the cost is greatly
reduced and capital expenditure is converted in the IT
operations to ongoing expenses.
.641** .000
8. There is weak financial support for research and studies in IT
development Software and applications and system designing. .429** .006
* Correlation is significant at the 0.05 level
Table (A.7) clarify the correlation coefficient for each paragraph of the
"Security and Privacy with adoption Big Data " and the total of the field. The p-
values (Sig.) are less than 0.05, so the correlation coefficients of this field are
significant at α = 0.05, so it can be said that the paragraphs of this field are consistent
and valid to be measure what it was set for.
3.3.2 Structure Validity of the Questionnaire
Structure validity is the second statistical test that is used to test the validity
of the questionnaire structure by testing the validity of each field and the validity of
the whole questionnaire. It measures the correlation coefficient between one field
and all the fields of the questionnaire that have the same level of likert scale.
66
Table (A.8) clarifies the correlation coefficient for each field and the whole
questionnaire. The p-values (Sig.) are less than 0.05, so the correlation coefficients of
all the fields are significant at α = 0.05, so it can be said that the fields are valid to
measure what it was set for to achieve the main aim of the research.
Table (A.8): Correlation coefficient of each field and the whole of
questionnaire
No.
Paragraph Pearson
Correlation
Coefficient
P-Value
(Sig.)
1. The Adoption of Big Data Technology .737** .000
2. Top management support of the Big Data technology .803** .000
3. Cultural and organizationl factors .528** .001
4. Skills of IT staff .740** .000
5. Security and Praivacy in adoption of Big Data .463** .003
6. Cost constrains The Adoption of Big Data .535** .000
* Correlation is significant at the 0.05 level
3.3.3 Reliability of the Research
The reliability of an instrument is the degree of consistency which measures
the attribute; it is supposed to be measuring. The less variation an instrument
produces in repeated measurements of an attribute, the higher its reliability. IT can be
equated with the stability, consistency, or dependability of a measuring tool. The test
is repeated to the same population of people on two occasions and then the obtained
scores are compared by computing a reliability coefficient (Creswell, Hanson, Clark
Plano, & Morales, 2007)
After applying the questionnaire and treating the data by SPSS program, the
researcher calculates the reliability of the questionnaire by using Cronbach’s
coefficient alpha Method through the SPSS software.
67
3.3.4 Cronbach’s Coefficient Alpha
This method is used to measure the reliability of the questionnaire between
each field and the mean of the whole fields of the questionnaire. The normal range of
Cronbach’s coefficient alpha value between 0.0 and + 1.0, and the higher values
reflects a higher degree of internal consistency. The Cronbach’s coefficient alpha
was calculated for each field of the questionnaire.
Table (A.9): shows the values of Cronbach's Alpha for each field of the
questionnaire and the entire questionnaire.
Paragraph Cronbach's
Alpha
1. The Adoption of Big Data Technology .746
2. Top management support of the Big Data technology .894
3. Cultural and organizationl factors .706
4. Skills of IT staff .775
5. Security and Praivacy in adoption of Big Data .688
6. Cost constrains The Adoption of Big Data .870
All paragraphs of the questionnaire .797
Table (A.9) shows the values of Cronbach's Alpha for each field of the
questionnaire and the entire questionnaire. For the fields, values of Cronbach's Alpha
were in the range from 0.827 and 0.924. This range is considered high; the result
ensures the reliability of each field of the questionnaire. Cronbach's Alpha equals
0.961 for the entire questionnaire which indicates an excellent reliability of the entire
questionnaire.
68
Appendix-B: Questionnaire (English)
Questionnaire
Dear All…
The researcher puts in your hands this questionnaire prepared for the collection of
data about a research entitled:
" Big Data Management In Gaza Strip Hospitals
: Barriers And Facilitators "
Which this research be submitted in a partial fulfillment of the requirement for MBA
degree.
I hope you to cooperate and provide information to assist in the completion of this
research , that we aim to illustrate the barriers and facilitators in the adoption of big
data management in Gaza Strip Hospitals, Thus contribute to gives an insight of how
we can uncover additional value from the data generated by healthcare.
As you have the experience and professional in your work field, and also your
currently position which related to the subject of the research, the researcher request
you to see all questionnaire items in carefully ,and answer all of them in Objectively
and high professional. Your feedback and comments would be a matter of interest
and they will have great impact regarding the enrichment of this research . Please
note that its use will be limited to scientific research purposes. Moreover, the
questionnaire will be treated confidentially.
Please accept our best regards
Researcher
غــزة – الإســـــلاميــةـة ـــــــــامعـالج
شئون البحث العلمي والدراسات العليا
كـليــــــــــــــــــــة التجارة
ادارة اعمالر ـــــــماجستي
The Islamic University–Gaza
Research and Postgraduate Affairs
Faculty of commerce
Master of Business
Administration
69
- Definition of Big Data Management :
Big data are data sources with a high volume, velocity and variety of data, which
require new tools and methods to capture, curate, mange, and process them in an
efficient way.
The interest in “big data in clinical care” has dramatically increased. This is due
partly to the widespread adoption of electronic medical record (EMR) systems and
partly to the growing awareness that better data analytics are required to manage the
complex enterprise of the health care system. Failure to store, analyze, and utilize the
vast amount of data generated during clinical care has restricted both quality of care
and advances in the practice of medicine.
Big data management is about two things—big data and data management—
plus how the two work together to achieve business and technology goals (Rossum,
2013.), and Data Management is defined by DAMA Data management Association
International) as "development, execution and supervision of plans, policies,
programs and practices that control, protect, deliver and enhance the value of data
and information assets".
- Research Variables:
Independent Variables
Top management support
Cultur and organization
IT skilled stuff
Security and Privacy
Budget constraints and undiscovered business
Dependent Variables
Readiness to Adoption of Big Data
Technology
70
First: Personal demographic Information
1. Gender Male Female
2. Qualification Bachelor Master PHD
3. Age (in years) Below 30 years From 30 – below40
From40 –below50 Above 50 years
4. Type of
Position
IT Specialist Administrative
5. Position General Director Director
Head of Department Programmer
Computer Engineers
Other, Define……………....
6. Location Elshefa Hospital European Gaza Hospital
Nasser Hospital Unit of ITDevelopment
Unit of ITDevelopment
7. Years of
Experience
Less than 5 From 5 – less than 10
From10–less than 15 Above 15 year
The scale is about assessing the intensity of your belief and ranges from strongly disagree to
strongly agree (7). You have to determine first whether you agree or disagree with the
statement. Second decide about the intensity of agreement or disagreement. If you disagree
with statement then use left side of the scale and determine how much disagreement that is -
strongly disagree, somewhat disagree (2) or disagree (3) and circle the appropriate answer. If
you are not sure of the intensity of belief or think that you neither disagree nor agree then
circle (4) . If you agree with the statement, then use right side of the scale and determine how
much agreement that is – agree (5), somewhat agree(6) or strongly agree (7) and circle the
appropriate answer .
Items
(1-7)
Second Section
The Adoption of Big Data Technology.
1. Big Data technology is an attractive technological option to the Ministry of
Health and to its Hospitals.
2. Big Data technology is an attractive economic option to the Ministry of Health.
3. The Ministry of Health Focuses on new IT system projects, which aim to
increase the efficiency and quality of services provided for the patients
4. Is ministry of health pursuing any Big data technologies (such as NoSQL or
Hadoop cluster
71
Items
(1-7)
5. The hospitals has a database suitable for all administrative, medical and
technical purposes and maintains all the data that is handled.
6. The hospital adopting a local network that allows all staff to access files in the
database and share data that it possesses such as data mining techniques and
expert systems.
7. The hospital adopting techniques that help to maintain and share knowledge
among doctors and exchange experiences, such as expert systems
8. The Ministry of Health Focuses on new IT system projects, which aim to
increase patients satisfaction.
9. The adoption of Big Data technology in hospitals operations will support
quality in health care.
10. The adoption of big data technology in IT operations will support the decision-
making process of the Ministry of Health and hospitals
First: Top management support of the Big Data technology.
1. Top management informed of ongoing developments of Big Data technology
and the importance of its use.
2. Top management concerns to provide the staff with the needed trainings and
skills for any new technology so as to keep up with development.
3. Top management develops plans which are flexible enough to accommodate
any changes required by the adoption of Big Data technology
4. Top management supports the new technologies which serve healthcare system.
5. There is a support from top management in IT field to adopt everything new
such as Big Data technology.
6. Top Management has a future plan to adopt Big Data Management via its IT
tools, and its uses in operations.
7. Top management has plans to get rid of obstacles that hinder the use of any new
technology at the Ministry of Health such as Big Data technology.
8. Top management provides the support and the needed requirements to adopt
Big Data technology.
9. The adoption of Big Data technology is included in Strategic Plan for Ministry
of Health.
10. Top management supports a shift policy in all or some of the IT operations
towards Big Data technology.
Second : Cultural and organization
1. The adoption of huge information technology is of interest to the Ministry and
the General Directorate of Hospitals.
2. Larger, more complex health systems have proven particularly receptive to the
introduction of technological innovation .
3. The attitude of doctors towards the techniques of big data management is the
subject of the attention of technology experts and the design of health systems.
72
Items
(1-7)
4. Health caregivers have been accustomed to a certain way of practicing medicine
- based on practice, experience and intuition rather than on computers - this
creates a negative attitude towards big data.
5. The organizational structure of Public Hospitals allows the exchange of
information Easily.
6. There is mystery of the future vision to adoption new technology to
management big data.
7. Routine actions in health care delay the transition to big data management
8. In your opinion Big data management technology can be seen as a direct attack
on doctors' values (professional independence, experience, prestige
9. The use of big data technology has negative effects on physician time to his
patient
10. There is Poor coordination between administrative units to use it technology
applications
11. There is an incentive system at the Ministry of Health to speed up the
implementation and use the big data management system
12. The system of procedures, transactions and methods used in hospitals is
compatible with the big data technology
13. There is Lack of awareness in the importance of applying IT Tools to
management hospitals data.
14. The degree of organizational management to ensure strategic consistency (i.e. to
that individuals within organizations are working toward the common goal of
successfully utilizing the technology) is…
Third: Skills of IT staff
1. Big Data technology helps on the development of
IT staff abilities and skills
2. Training provided to staff in the field of IT enough, and makes them
sophisticated and look forward to some extent to the latest technology.
3. Big Data technology helps on the development of the spirit of creativity and
innovation.
4. IT staff realize the importance of the adopting of Big Data at the Ministry of
Health
5. There is low confidence of HR staff in their ability to Use of IT applications
6. There is Fear of HR staff from increasing tasks And administrative burdens
when they using IT system.
7. Hospitals have enough qualified personnel to develop software and data
management systems
8. The hospital has a sufficient number of qualified personnel to develop the
infrastructure of networks and means of communication
9. The Ministry has a performance assessment system that points a clear criteria
for staff ability to deal with big data management tools.
73
Items
(1-7)
10. The staff dissatisfaction and disability to change is one of the challenges that
hinder the adoption of any new technology (such as Big Data Technology)
11. Technological developments encourage positive competition among staff to
motivate them to serve the general interest
12. IT staff needs training in the Big Data.
Fourth: Security effectiveness in adoption of Big Data
1. The data security is the biggest challenges facing the Ministry of Health to
adopt any new technology.
2. We must know where the data is stored in the Big Data
3. The strength of data security depends on the strength of service provider in
terms of security
4. It can be considered a contract agreement between the Ministry of Health and
the service provider as a safety and reliability of the data.
5. There is confidence in new technologies and the providers of these services
6. The adoption and use of Big Data Technology Lead to develop a plan to protect
the security and confidentiality of the information
7. The services and applications of Big Data provided by service providers
companies (e.g. IBM, SAP, Oracle,...) are difficult to hack and piracy
8. Security and fear of data breaches is the most common barrier to expanding
mobility
Fifth: Cost Reduction Through The Adoption of Big Data.
1. The Ministry of Health focuses on modern IT system projects, which aim to
reduce costs.
2. The service of Big Data provided (SAP, IBM,..) at the Ministry of Health is less
expensive than the current system.
3. Not knowing whether the benefits are worth the cost
4. The cost is too high for outsourcing analysis or operations
5. For ministry of health that are currently using big data, the cost of IT
infrastructure is the main constraint
6. The limited budget is it largest barrier to expansion to big data technology.
7. When to adopt Big Data Technology, the cost is greatly reduced and capital
expenditure is converted in the IT operations to ongoing expenses.
8. There is weak financial support for research and studies in IT development
Software and applications and system designing
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Appendix-C: Questionnaire (Arabic)
السادة/ ................................................. المحترمون تحية طيبة وبعد:
ضع الباحث بين أيديكم هذا الاستبيان المعد لجمع البيانات حول دراسة بعنوان:ي" Big Data Management In Gaza Strip Hospitals
: Barriers And Facilitators "
للحصول على درجة الماجستير في ادارة الاعمال. وهذه الدراسة هي متطلب استكمالاً
كلي أمل بكم في التعاون وتقديم المعلومات التي تساعد في اتمام هذه الدراسة، التي نهدف من لوجيا ادارة البيانات الضخمة بمستشفيات خلالها توضيح المعيقات والتسهيلات في تبني تكنو
اضافة قيمة من قطاع غزة، وبالتالي المساهمة في اعطاء نظرة معمقة للكشف عن امكانية البيانات الناتجة في مجال الرعاية الصحية.
ونظرررا لمرررا تتمتعررون بررره مررن خبررررة ومهنيررة فررري مجررال عملكرررم، وبحكررم مررروقعكم الرروظيفي المتعلرررق ن الباحث يرجوكم بالتكرم والاطلاع على فقرات هذا الاستبيان بعناية واجابة بموضوع الرسالة، فا
ن المعلومرررات التررري تررردلون بهرررا سررروف تسرررتخدم أ جميرررع اسرررملته بموضررروعية ومهنيرررة عاليرررة، علمررراً ط.البحث العلمي فق لأغراض
وتفضلوا بقبول وافر الاحترام والتقدير الباحث
بهاءالدين جمال السر
زةــغ – ةــلاميــــــة الإســـــــــامعـالج
شئون البحث العلمي والدراسات العليا
التجـــــــــــــــــــــارةة ــــــــــــــــــــليـك
ادارة اعمــــــــــــــــالر ـــــــــــماجستي
The Islamic University – Gaza
Research and Postgraduate Affairs
Faculty of commerce
Master of Business Administration
75
إدارة البيانات الضخمة:تعريف تكنولوجيا ميرررررز بالخصرررررائ تت ،ضرررررخمة جرررررداً ومعقررررردة البيانرررررات هررررري مجموعرررررة مرررررن البيانرررررات الضرررررخمة
(Volume)وهري: الحجرم Vلأنهرا تبردأ جميعرا برالحرف، باللغرة الإنجليزيرة V)(s'5الخمر
لذا تتطلب (Value).والسرعة (Velocity)والقيمة (Veracity)ة والصح (Variety)والتنوع
قردرات تحليليرة ذات Hadoop/MapReduceوأطرر NoSQLتقنياتٌ من قبيل قواعد بيانات
شررف واسررتخلاص القيمررة والرررقى العميقررة فرري غضررون وقررت زمنرري ومعالجررة وتحويررل وك للالتقررا
.مقبول
ادارة البيانررات الضررخمة فرري مجررال الرعايررة الصررحية" بشرركل كبيررر، "يزيررد الاهتمررام فرري مررن هنررا
بسرربب الانتشررار الواسررع لررنظم السررجلات الطبيررة الالكترونيررة، والاهتمررام المتزايررد برران هنرراك حاجررة
.و ادارة المؤسسات الصحية بصورة افضلللبيانات نح أعمقالى تحليلات
المتغيرات:
المتغيرات المستقلة المتغير التابع
تبنرررررررررررررري تكنولوجيررررررررررررررا ادارة البيانررررررررررررررات
الضخمة
دعم الادارة العليا
الثقافة التنظيمية
مهارات الموظفين التكنولوجية
فعالية الامان
تخفيض التكاليف
76
أولًا/ البيانات الاساسية:
( أمام الاجابة المناسبة.يرجى التكرم بوضع اشارة )
الجنس .1
أنثى ذكر
المؤهل العلمي .2
بكالوريوس ماجستير دكتوراه
العمر .3
50اقل من – 40من 40اقل من -30سنة من 30أقل من
سنة فأكثر 50
نوع الوظيفة .4
اداري خبير تكنولوجيا معلومات
الوظيفية .5
ير عام مدير دائرة رئيس قسم مد
مبرمج مهندس حاسوب أخرى، أذكر.........
موقع العمل .6
مجمع الشفاء الطبي مجمع ناصر الطبي وحدة نظم المعلومات
غزة.-مستشفى الاوروبي
سنوات الخبرة .7 سنوات 10اقل من -5سنوات من 5اقل من
سنة 15سنة اكثر من 15اقل من -10من
وافق بدرجة عالية الى ألا :المقياس عبارة عن تقييمك لدرجة مدى الموافقة او عدم الموافقة مع العبارة وهو من تعليمات: وافق بدرجة عالية.أ( 7)
أولًا: انت بحاجة الى تحديد هل انت موافق ام لا. :افقتك او عدم الموافقةثانياً: تقرر مدى مو
لا -3وافق بدرجة متوسطة ألا -2وافق بدرجة عالية ألا -1اذا كنت غير موافق، تحدد درجة عدم الموافقة ما بين ) - وافق بدرجة قليلة(.أ
ما بين الموافقة وعدم الموافقة. -4يقع ما بين درجة الموافقة وعدم الموافقة تضع كان تقديركاذا - وافق بدرجة عالية(.أ -7وافق بدرجة متوسطة أ -6وافق بدرجة قليلة أ -5ق تحدد درجة الموافقة ما بين )اذا كنت مواف -
77
الدرجة العبارة
(1-7)
الجزء الاول
تبني تكنولوجيا ادارة البيانات الضخمة
تعتبر تقنية ادارة البيانات الضخمة بالنسبة لوزارة الصحة خيارا تكنولوجيا جذابا. .1
تبر تقنية ادارة البيانات الضخمة بالنسبة لوزارة الصحة خيارا مالياً جذابا.تع .2
تركز وزارة الصحة على مشاريع انظمة تكنولوجيا المعلومات الحديثة التي تهدف الى زيادة كفاءة الخدمات الصحية .3 التي تقدمها المستشفيات للمرضى.
(.NoSQL or Hadoop cluster ت الضخمة )مثل وزارة الصحة قامت بشراء تقنيات ادارة البيانا .4
تمتلك المستشفى قاعدة بيانات مناسبة لجميع الاغراض الادارية والطبية والفنية تحتفظ بجميع البيانات التي يتم التعامل معها. .5
نات التي تمتلكها.توظف المستشفى شبكة محلية تتيح لجميع المنسوبين الوصول للملفات في قاعدة البيانات وتبادل البيا .6
.توظف المستشفى تقنيات تساعد على الاحتفاظ بالمعرفة ومشاركتها بين الاطباء وتبادل الخبرات بينهم مثل الانظمة الخبيرة .7
تركز المستشفى على مشاريع انظمة تكنولوجيا المعلومات الحديثة التي تهدف الى زيادة رضى المرضى. .8
انظمة تكنولوجيا المعلومات الحديثة التي تهدف الى زيادة جودة العمل.تركز المستشفى على مشاريع .9
تبني تقنية البيانات الضخمة في عمليات تكنولوجيا المعلومات سوف يدعم عملية التشخي الصحيح. .10
.صحة والمستشفياتتبني تقنية البيانات الضخمة في عمليات تكنولوجيا المعلومات سوف يدعم عملية اتخاذ القرارات بوزارة ال .11
الجزء الثاني
دعم الادارة العليا نحو تبني تكنولوجيا ادارة البيانات الضخمة
.واهمية استخدامها البيانات لإدارةالادارة العليا على اطلاع مستمر بالتطورات التقنية .1
يدة لمواكبة التطور.تهتم الادارة العليا بتزويد العاملين بالتدريب والمهارات اللازمة لأي تقنية جد .2
تضع الادارة العليا خطط تتسم بالمرونة الكافية لاستيعاب أي تغيرات تتطلبها تبني تقنية البيانات الضخمة .3
78
.تضع الادارة العليا التقنيات الحديثة التي تخدم العمليات الصحية .4
ا هو جديد مثل تقنية ادارة البيانات الضخمة.يوجد دعم من الادارة العليا في مجال تكنولوجيا المعلومات لتبني كل م .5
.يوجد خطة مستقبلية لدى الادارة العليا لتبني تقنية البيانات الضخمة واستخدامها في عمليات تكنولوجيا المعلومات .6
الضخمة.الادارة العليا لها خطط علاجية للتخل من العقبات التي تعيق استخدام أي تقنية جديدة مثل ادارة البيانات .7
توفر الادارة العليا الدعم والمتطلبات لتبني تقنية ادارة البيانات الضخمة. .8
تبني تقنية ادارة البيانات الضخمة مدرجة ضمن الخطة الاستراتيجية لوزارة الصحة. .9
.الضخمة تدعم الادارة العليا سياسة التحول في كل او بعض عمليات تكنولوجيا المعلومات نحو تقنية البيانات .10
الثقافة التنظيمية
يعتبر تبني تقانة المعلومات الضخمة محل اهتمام الوزارة والادارة العامة للمستشفيات. .1
الهيكل التنظيمي لوزارة الصحة وانتشار مستشفياتها يساعد نحو تبني تكنولوجيا ادارة البيانات الضخمة. .2
لخيار.يفضل الاطباء استخدام الاوراق لو كان لهم ا .3
الممارسة على تقوم -الطب ممارسة في معينة طريقة على الأطباء( اعتادوا فيهم الصحية )بما الرعاية في المهنيين .4 . Big Dataهذه تتسبب بتكوين موقف سلبي تجاه ال -الحواسيب على اعتماده من أكثر والحدس والخبرة
.بسهولة اتيسمح الهيكل التنظيمي للمركز الصحي بتبادل المعلوم .5
تكنولوجيا ادارة البيانات الضخمة.الرقية المستقبلية لتطبيق في غموض هناك .6
تقنية ادارة البيانات الصحية الضخمةتؤخر عملية التحول نحو في قطاع الصحة الإجراءات الروتينية .7
-باء )الاستقلالية المهنيةيمكن ان ينظر الى نظم ادارة المعلومات الضخمة انها هجوم مباشر على قيم الاط .8 المكانة المرموقة(. -الخبرة
استخدام النظم الالكترونية الخاصة بتكنولوجيا البيانات الضخمة لها اثار سلبية على وقت الطبيب بالمريض. .9
يتوفر نظام تحفيزي بوزارة الصحة يساعد على سرعة تطبيق نظام ادارة البيانات الضخمة. .10
والطريقة المستخدمة في معاملات المستشفيات تتفق مع تكنولوجيا ادارة البيانات الضخمة.نظام الاجراءات .11
79
هناك ضعف الوعي بأهمية تطبيق ادوات التنقيب في البيانات الضخمة لإدارة المستشفيات. .12
مهارات الموظفين
ومهارات موظفي تكنولوجيا المعلومات. تساعد التقنيات الحديثة مثل تكنولوجيا ادارة البيانات الضخمة بتطوير قدرات .1
التدريب المقدم للموظفين العاملين في مجال تكنولوجيا المعلومات كاف، ويجعلهم متطورين ومتطلعين الى حد ما .2 الى آخر ما توصلت اليه التكنولوجيا.
بتكار.يساعد تبني التقنيات الحديثة في ادارة البيانات الضخمة على تنمية روح الابداع والا .3
يدرك العاملون في مجال تكنولوجيا المعلومات بأهمية تبني تقنيات البيانات الضخمة في وزارة الصحة. .4
. استخدام تطبيقات الإدارة الإلكترونية ثقة موظفي الموارد البشرية بقدرتهم علىبانخفاض يوجد .5
عن استخدام هذه التقنية. يةوالأعباء الإدار خوف موظفي الموارد البشرية من زيادة المهام .6
يوجد بالمستشفيات عدد كاف من الافراد المتخصصون المؤهلين لتطوير البرمجيات ونظم المعلومات لإدارة البيانات. .7
يوجد بالمستشفى عدد كاف من الافراد المؤهلين لتطوير البنية التحتية للشبكات ووسائل الاتصالات. .8
م اداء يشير الى معايير واضحة لقدرة الموظفين على تطبيق برامج ادارة البيانات الضخمة.يوجد في المؤسسة نظام تقيي .9
. عدم رضى وقدرة الموظفين للتغيير يعتبر من التحديات التي تعيق تبني أي تقنية جديدة مثل ادارة البيانات الضخمة .10
.الضخمة وخاصة في بناء تقنيات ادارة البيانات الضخمةيحتاج موظفي تكنولوجيا المعلومات الى تدريب في ادارة البيانات .11
آمن المعلومات وتبني تكنولوجيا ادارة البيانات الضخمة
تعتبر سرية وامن البيانات من اكبر التحديات التي تواجه وزارة الصحة في تبني أي تقنية جديدة. .1
لامنية.تعتمد قوة الامن للبيانات على قوة مزود الخدمة من الناحية ا .2
يمكن اعتبار عقد الاتفاق بين الوزارة ومزود الخدمة بمثابة موثوقية وامان للبيانات. .3
...(. SAP،IBMيوجد ثقة بالتقنيات الجديدة وبمقدمي هذه الخدمات من الشركات العملاقة ) .4
.يعتبر أمن المعلومات واحدا من أكبر التحديات التي تواجه الصحة الرقمية .5
زيادة الانفاق على أمن المعلومات عند اعتماد تقنيات ادارة البيانات الضخمة في المستشفيات. أتوقع .6
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خدمات وتطبيقات ادارة البيانات الضخمة من شركات مزودي الخدمة صعبة الاختراق والقرصنة. .7
.التحول في تكنولوجيا المعلومات لبيانات الضخمة وسبل تخزينها وحمايتها أمرا ضروريا لنجاحتكنولوجيا الالتزام نحو ا .8
.الأمن والخوف من خرق البيانات هو الحاجز الأكثر شيوعا لتوسيع التنقل .9
التكاليف
تركز الوزارة على مشاريع انظمة تكنولوجيا المعلومات الحديثة لإدارة البيانات التي تهدف الى خفض التكاليف. .1
اقل تكلفة من النظام الحالي. NoSQL or Hadoop clusterتعتبر تكنولوجيا البيانات الضخمة .2
.تستحق التكلفةلا NoSQL or Hadoop clusterمن نظام الفوائد .3
. Hadoopمثل التكلفة مرتفعة جدا من أجل الاستعانة بمصادر خارجية للتحليل أو العمليات .4
.لعائق الرئيسيهي ا لتطبيق تكنولوجيا البيانات الضخمةتكلفة البنية التحتية .5
لاعتماد تقنيات ادارة البيانات الضخمة.تمثل الميزانية المحدودة أكبر حاجز .6
ت.التكلفة في عمليات تكنولوجيا المعلوما تنخفضالبيانات الضخمة، تكنولوجيا تبنيعند .7
نظم ادارة البيانات الضخمة.الإمكانيات المالية اللازمة لتطبيق في نق هناك .8
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Appendix-D: Interview transcription and Coding (English)
The interviews were organized and carried out with (7) specialists in data and
project management. Data analysis was in successive steps, which:
a. Interview transcription was from the voice recording, transcripting each
interview separately.
b. The transcription process began after the interview with no more than a
day.
c. Translation into English was done to prepare the report.
d. Re-read interviews and listen to audio recordings again, for a deeper
understanding, and make sure the translation is more accurate.
e. Take notes, and write comments about the terms related to the study
factors.
f. Read texts one after another - within each question - to extract important
elements, and classify them.
g. Highlight the important elements, and bring them out within the
classification.
h. Finally, the results of the interviews are presented in separate reports for
each case.
The results were formulated based on the duplicates, resulting from the
extracted elements, which are related to the study factors about the adoption of Big
Data management.
82
Sample Interview # (1)
" Big Data Management In Gaza Strip Hospitals: Barriers And Facilitators "
Date: 19 Oct. Place: MoH Time: 11 A.M Interviewer: Dr. Hani ALwhadi Interviewee (subject number):
Descriptive transcription Coding
I. Introductory Questions
1- What is your date of birth? no response
2- What is the highest level of education you attained? Master degree in healthcare management
3- How long you are working in healthcare? 22 years
4- What are your position and the kind of work you do? Director of Information System Unit in MoH, The unit gets up issue periodic and semi-periodic reports on the
work of MoH, timely and accurate information to decision-makers, as well as dissemination of information to
stakeholders, there are permanent and ongoing plans for the development of the Information Systems Unit,
especially archives of births, deaths, cancer, hospitals, primary care. The aim is obtaining full and accurate
information, for their importance in decision-making.
5- What do you think are three major E-hospitals/management problems? In fact, E-hospitals, it is applied to all hospitals, gave us an opportunity to access the data, it's not perfect and
complete to filling of all the data in the models within the program. Currently we have a basic requirement to
follow up the registration of basic data within the program, E-hospitals is a basic and having easy access to a lot
of information is a step along the way. For Gaza European Hospital, Care program was the first, and we are
currently working on unify the program with the rest of the hospitals.
the issue of indicators is very important, we have focused recently on 120 performance indicators,...were
manually examined, now data entry computerized that facilitate reaching to data technically. There are
indicators of the workforce and accordingly plans are drawn up, health indicators e.g. occupancy number of
Major E-hospitals Problems - Data visualization is manual.
- Needs to support for
information systems
infrastructure.
- ongoing plans about IS
development.
83
beds-Number of workforce in proportion to the community, e.g. Cancer, previously indicators were manually
examined, now data entry computerized and indicators were checked technically. The issue of getting indicators
quickly is our hope for the future, In light of the current situation, this requires substantial funding and needs
support for information systems infrastructure, there are support agencies, but in a limited degree, especially
from the National Institute of Public Health and WHO, and article 8 of the plan speaks directly about the
development of information systems in all MoH hospitals.
II. Core Questions
1- What are the Challenges and Barriers for Gaza Hospitals to adopt Big Data? Relating to: a) Top management support. The minister and his agent are supporters, interesting in data management, But on a general managers levels,
there is a minor encouragement towards the strengthening of information systems. Therefore, in each ministry
council, our taske was to present indicators and reports about the role and importance of indicators and
information systems, so HMIS developments to reached all Hospitals to improve health services, this has been
in the last two years. There is guidance and interest from MoH to bring changes in the level of data management
technology through the ministry's programming development teams, so interest in internal development but not
equal interest in purchasing data management technology from outsourcing, because of other determinants
related to the conditions we live and the impact of financial deficiencies.
There is a draft decision on data governance, who has the right to access information and degree of access, and
the circulation of information, to what extent?, to a certain extent, we also seek a policy guide and procedures
for dealing with the data.
MoH management has no strategic planning for the adoption Big Data, its focus on internal efforts.
b) Culure-Organization:
doctors seems hesitant and unwilling to accept healthcare IS applications during their work practices. In many
hospitals, doctors often writes clinical notes on paper, … the reason related to the doctor-patient time and the
doctors wants clinical data from the system e.g. Lab.Data., that helps in diagnosis. Therefore, understanding
what leads doctors' to accepted, and motivate them to use (IS) is our interest. There is no incentive to direct staff
towards the introduction of complete paper data into the system. An Italian team came to us and noted the size
of the achievement in the existence of an accurate and correct information system. And at indeed was an
assessment, Including the experience of Italian hospitals by linking the salary at the rate of full data entry into
the system.
a) Top management support.
- There is supporters and interested
in Big Data Management.
- interest in internal development.
- There isn’t interesting in
outsourcing data management
technology.
- no strategic planning for the
adoption Big Data, plans focus on
internal efforts.
b) Culure-Organization:
- Weak in dealing with IS from
doctors.
- There isn’t incentive.
c) IT-skills staff:
- our team is special and have self-
development
- there is no scholarship, we needs
training.
d) Security and Privacy:
- It's a challenge.
e) Budget constrain.
- such projects depends on the
84
c) IT-skills staff:
We needs training, development, and motivation, although we have our special teams thay are self-educated, in
situations, did,n allow them to have external participation to gains big data skills, ... information circulation and
the new entry system, it’s a wonderful and proud of our self-effort.
d) Security and Privacy:
The information caused us a challenge at MoH especially hospitals, and this result in the preservation and
storage, and access to them and such as, so security and privacy concerns.
e) Budget constrain.
Our movement in such projects depends on the international donor, because the lack of financial resources, and
therefore our interest to provide medicine and attention to health care programs, and the absence of a local study
to review the benefits versus the cost of implementing Big data management projects
international donor.
- lack of financial resources.
- our interest to provide medicine
and attention to health care
programs.
85
2- Main barriers to setup big data project within hospitals (choose up to three)
Data governance issue
Not a business management priority
Unsure of technology requirements Lack of budget Security concerns
Shortage of big data skills
Work culture
Organizational complexity
Lack of leadership and commitment
Poor quality of data
3- Ranking Challenge in big data 3nd most challenges 2nd most challenge Most challenges Challenge
Data growth
Data infrastructure
Data governance- policy
Data integration Data velocity Data variety Data compliance
Data visualization
86
Sample Interview 2
" Big Data Management In Gaza Strip Hospitals: Barriers And Facilitators "
Date: 19 Oct. Place: MoH Time: 11 A.M Interviewer: Date: Eng. Louay Freja Interviewee (subject number): (2)
Descriptive transcription Coding
I. Introductory Questions
1- What is your date of birth? -Day 27 Month 9 Year1978
2- What is the highest level of education you attained? Bachelor degree in computer science.
3- How long you are working in healthcare? 10 years
4- What are your position and the kind of work you do? Director of Development IS department in IS Unit in MoH..
5- What do you think are three major E-hospitals/management problems? E-Hospitals system in both administrative and medical, is implemented in 12 hospitals except the Gaza
European Hospital, which operates a different program, by 6/2018 everyone will be on this program.
The process of programming and application of the system spread as a model in the sections of the reception
and clinics and then gradually to all sections on all hospitals, Of course, it cost 20 thousand dollars to
accommodate the volume of data, the main problem was the storage, so we buy "NAS" network-attached
storage, its cost is (20,0000$), to accommodate the volume of data, there is a scaning patients files and X-ray,
that needs big capacity to storing, only clinical lab data from Shifa Hospital daily is 100 MB. as a result, every 6
months data was deleted. This requires tools to continuously development.
We are going to switch from Disk Top to web appellation, we are currently in the process of transitioning to
work on the program from anywhere, each hospital works as a unit on its own, with data being migrated to a
central base in the future to be used. We take a comprehensive history in the future. If a hospital network is
blocked, we return to the central database to obtain data. Resistance to change, we face it. Especially, when
doctors use paper in patient treatment can detect 50 cases, but when it comes to the computer, the number will
be reduced. Another expert from Italy works for the HSI, the system they have unified for all hospitals, the
doctor can,t receive the salary after confirming the data. Changing administrative system style is a challenge, so
Major E-hospitals Problems - The main problem was storage
- Needs infrastructure.
- ongoing to switch to web
appellation.
- Resistance to change,
especially from doctors.
- Change towards SQL is very
difficult.
87
when the administrative change that will constantly modifying IS system.( its 10% per annum).
Change towards SQL is very difficult
II. Core Questions
1- What are the Challenges and Barriers for Gaza Hospitals to dopte Big Data? Relating to: a) Top management support: MoH has its slogan "patient first" not the data first, and I,m completely
agreed with it, we promote the patient problem, any attention should be on the patient and then to the logistics
units, Decision makers considers Clinical Units as essential, thus their attention towards medical side. The
highest level in the ministry as a future vision interested in management of IS, this is pleased, but inconsistent
with the directives and orders of implementation.
b) Culure-Organization: The organizational structure is so large and all IT teams is distributed to the
ministry's institutions and hospitals. There is a great multiplicity in the nature of the work because of the
dimensions of work in the various ministry of health. We have the energy to follow the primary care program,
hospital program, procurement programs, central warehouse programs, And staff evaluation, there are almost 10
programs each program no less. Scattering of the team at all facilities causing slow, and there is resistance to
change in the, It is expected to appear with the application of any new system. There isn't incentive system
directed to caregiver staff, that make paperless, In this problem, when an Italian specialist team visit
MoH in Gaza and noted the size of achievement in information system. thay tell us that Italian HIS in
hospitals was suffering in motivate doctors to inters clinical note via system. Thus, thay do it by
linking the salary at the rate of full data -with accuracy- entry into the system. c) IT-skills staff: while ago there is a significant shortage in programmers, Currently we hope to hire more
programmers for work.
d) Security and Privacy: MoH bought a security router from a foreign country and its delayed receipt to 6
months because the refusal of the Israeli occupation to enter, and its purchased for security from hacking, there
is a difference between security and a privacy, another look that it does not happen to penetrate and
destroy the data. So in the security issue we seek to non-Hacking data. Therefore, there is a written
policy for the handling of information outside and within MOH. e) Budget constrain: Look, at the infrastructure level frankly we hope to find supporters, for example from
2008 until the day If we adopted upon financial coverage from government, we did not reached this current
achievement. The most of the funding came from donors and supporters.
a) Top management support.
- "patient first" not data.
- The highest level in the ministry
as a future vision interested in
management of electronic data.
b) Culure-Organization:
- structure is so large its distributed
efforts.
- Resistance to change
c) IT-skills staff:
- a problem in the shortage of
programmers
d) Security and Privacy:
- we seek to non-Hacking data.
e) Budget constrain.
- most of the funding came from
donors and supporters
88
2- Main barriers to setup big data project within hospitals (choose up to three)
Data governance issue
Not a business management priority
Unsure of technology requirements Lack of budget Security concerns
Shortage of big data skills
Work culture Organizational complexity
Lack of leadership and commitment
Poor quality of data
3- Ranking Challenge in big data 3nd most challenges 2nd most challenge Most challenges Challenge
Data growth
Data infrastructure
Data governance- policy
Data integration Data velocity
Data variety
Data compliance
Data visualization
89
Sample Interview 3
" Big Data Management In Gaza Strip Hospitals: Barriers And Facilitators "
Date: 22 Oct. Place: MoH Time: 11 A.M Interviewer: Eng. Issam ALaqad Interviewee (subject number): (3)
Descriptive transcription Coding
I. Introductory Questions
1- What is your date of birth? no response
2- What is the highest level of education you attained? Bachelor degree in computer engineering
3- How long you are working in healthcare? 11 years
4- What are your position and the kind of work you do? Director of Information System Unit in Gaza European Hospital Department of networks and computers
5- What do you think are three major E-hospitals/management problems?
CARE is the first computerized system in the Gaza Strip in 2002, so the Gaza European Hospital is the
first hospital in the Gaza Strip operates a computerized system. data in hospitals increases day after
day, and we move to paperless in the future - this is directed at the Ministry councils – data volum
doubling constantly, for example, in our hospital (European Gaza Hospital) the size of X-ray added at
the beginning of this year (2017) untilnow (7 months), reachs (3 TB), so these electronic files need a
huge servers and need modern processing techniques to obtain knowledge and provide them to the
stakeholders. CARE needed a customization process, with local requirements, the programmers in the
European Gaza hospitals developed the program with the needs of the hospital, so far there are
requests for adaptation in the program and modification, for example I press a button given me the
number of deaths from a particular disease. Is the data entered correctly, we run into an incomplete
input, so we force the data input to enter all the data. There is interest in the European Gaza Hospital
and hospital management and the importance of modern technology and computerization and data
management. Currently, they are working with many health organizations to computerize health care,
Major E-hospitals Problems - Data visualization is manual.
- Needs to support for
information systems
infrastructure.
- ongoing plans about IS
development.
90
because the beginning of the health service begins with primary care and this idea of the system.
II. Core Questions
1- What are the Challenges and Barriers for Gaza Hospitals to adopt Big Data? Relating to: a) Top management support.
The Department is very helpful and convinced at the time of the idea of computing. At Ministry is
exerting great effort, in automating the data of health sector and thay carrying out workshops that
about how to deal with health information systems. So there is an effort from the top management to
adopt tools that managements huge data. There is an effort to commit from the top management to the
management of large data, but the possibilities are not controlled by a network built since 2002. Good
techniques for now. Things are working, possibilities in the Gaza conditions. "Answer a medical
device for a department that contributes to the treatment of patients and saves their lives better than
upgrading the current system.
This is an existing trend .So our role is always noted that the data management system is also
important and it contributes to the speed of diagnosis through the transfer of data quickly and gives the
patient a medical record through which the doctor can determine a lot in the future about the patient.
b) Culure-Organization: Resistance to change from any new system: For example, when we started in linking digital scan
images directly to IS, and the X-ray image visualizes on computer. In the beginning, we found
opposition from the medical staff, but in present day, the medical staff has asked our IT team to solve
problems when the digital X-ray service breakdown. Where, the past way,was heavy in filming the X-
ray imges.
c) IT-skills staff:
Information technology, for example, from 10 years ago till day, there is a major development, so the IT team is
required to be constantly on the same footing with new technology. In terms of big data technology, we dont
have skills and training, For example, in Gaza European Hospital there are 2 programmers and 2 working with
networks infrastructure, so we working on processing and follow-up hospital requests. The hospital runs in
emergency condition, The team always works under endless pressure, we divide the work according to priorities
a) Top management support.
- There is supporters and interested
in Big Data Management.
- interest in internal development.
- There isn’t interesting in
outsourcing data management
technology.
- no strategic planning for the
adoption Big Data, plans focus on
internal efforts.
b) Culure-Organization:
- Weak in dealing with IS from
doctors.
- There isn’t incentive.
c) IT-skills staff:
- our team is special and have self-
development
- there is no scholarship, we needs
training.
d) Security and Privacy:
- It's a challenge.
e) Budget constrain.
- such projects depends on the
international donor.
- lack of financial resources.
- our interest to provide medicine
and attention to health care
programs.
91
and importance, and the subject of development requires us to have the technician work and he is satisfied until
he produces. The issue of data entry is important
d) Security and Privacy:
The issue of the network is not subject to an external attack located, and there is a second type which is
unintended to give the owner password for more than one person, The information is leaked.
Here we do our best to ensure that the network is safe, important privacy, feeling for users of the system that
there is accountability and a system punishable leniency and indulgence in opening the field to circulate the
password at a number of other. Here, we can only extract data after the researchers have obtained approval, and
no data is given except by authority, There is a written protocol to data governance in terms of -storage,
archiving and retrieval, standards and procedures for use and who has permission to obtain or carry out specific
information and the level of access to information.
e) Budget constrain. We suffer very much in this subject and this is subject to the situation of the Gaza Strip in general, there is
suffering in which the Palestinian suffers in various aspects, and this subject is part of this suffering, computers,
server and network and so will not be a priority.
92
2- Main barriers to setup big data project within hospitals (choose up to three)
Data governance issue Not a business management priority
Unsure of technology requirements Lack of budget
Security concerns
Shortage of big data skills
Work culture Organizational complexity
Lack of leadership and commitment
Poor quality of data
3- Ranking Challenge in big data 3nd most challenges 2nd most challenge Most challenges Challenge
Data growth
Data infrastructure
Data governance- policy
Data integration
Data velocity
Data variety
Data compliance
Data visualization
93
Sample Interview 4
" Big Data Management In Gaza Strip Hospitals: Barriers And Facilitators "
Date: 22 Oct. Place:European Gaza Hospital Time: 11 A.M Interviewer: Date: Mr. kamal Mosa Interviewee (subject number): (4)
Descriptive transcription Coding
I. Introductory Questions
1- What is your date of birth? - no
2- What is the highest level of education you attained? Master degree in Healthcare Management.
3- How long you are working in healthcare? 30 in MoH.
4- What are your position and the kind of work you do? My work nature is administrative and
financial manager, responsible for the administrative staff, and the department of Patient Services, which deals
with data, personnel and computer issues.
5- What do you think are three major E-hospitals/management problems? For the Gaza European Hospital is the first hospital where the computerization of data in the Gaza Strip, and the
program was Kair and trained technical staff and has been developing the program several times, we have gone
a long way, The challenge we faced, At the level of application of medical technical data we are currently
working for the benefit of the medical staff. In the departments the process of computation and data entry. The
rest of the data, such as the emergency department, radiation, CT and resonance sections, have been minimized
to reduce the paper side. Even at the ministry level there is development, Medical data, doctors and nursing do
not record the data we still use the file. There is a problem, after the ministry applied the e-hospital program
took all the technical staffs from the European and there are only two programmers left. Our need for great
development, training and education. The ministry is currently focusing on hospitals where this system was not
previously. This is what we have been subjected to great injustice, and currently the program is led by those
who were in the European Gaza Hospital.In terms of computer hardware, most devices are old, and support is
only available to solve problems, not support that resolves problems radically. Administrative Slot, We use
Major E-hospitals Problems - computer hardware, most
devices are old, and support is
only available to solve
problems.
- Needs to support for
information systems
infrastructure.
- medical notes are recorded
from doctors, nursing is on
paper and this is the only
problem currently.
94
paperwork, currently mostly electronic. Most pharmacies have computerized stores, and outpatient clinics as well, but
when medical notes are recorded from doctors, nursing is on paper and this is the only problem currently.
II. Core Questions
1- What are the Challenges and Barriers for Gaza Hospitals to dopte Big Data? Relating to: a) Top management support: The ministry has a great interest in MIS and has given it considerable
attention in the last four years, But they focused on other hospitals, MoH tooks the European as a model and
circulated it to the ministries,The ministry's interest is that the European hospital is integrated and has a
reputation and experience in computerization and management of data,This made the ministry's attention less
toward the hospital.There are devices older than 5 years, and there is a change but it gives us the minimum and
not the required requirement. There is strong support from the ministry and there is a good shift in this
framework, but the barrier is the financial conditions.
b) Culure-Organization: The problem of spreading the benefit of this system among the medical staff,
training and education،It is essential And this is a deficiency of us ، We agreed on the deployment of training and
education but because of lack of cadres for training ، This will be at the expense of a second job is necessary
when technical cadres, Which make adjustments and work daily. The incentive system to support the orientation
of IT in data management is weak, Especially since we get half a salary. The problem of the siege and the lack
of compensation for additional hours, and the possibilities available only discretionary at the level of the
ministry.
c) IT-skills staff: Software is always in progressing, so training and promotion are always required, We are in
contact with the Islamic University and World Health Organization, The ministry is focusing more on its
employees. When we ask the programming team, we want some factors to appear in the reports. For
example: we wants on the screen, brings together (history - age - the type of surgery –surgery classification
- the beginning of anesthesia - the end of anesthesia ) of patient. But in centralization, the follow-up
between the Information Systems Development Unit and the hospital has become slow, our team is special
and have self-development, but the team number is insufficient.
d) Security and Privacy:Surer, its challenge. Security and privacy considered as a high challenge so we have
policies and procedures for accessing and carrying out the data, each department with regard to it, and
management in relation to it and according to the powers.
e) Budget constrain.There is a problem in providing needs, needs are provided as a rescue and not as a
development, because of the financial determiner and the ministry's preoccupation with other hospitals.
a) Top management support.
- Ministry has a great interest in
MIS.
- there is a good shift in this
framework.
- There isn’t interesting in
outsourcing data management
technology.
b) Culure-Organization:
- there weak in dealing with IS
from doctors.
- The incentive system to support
the orientation of IT in data
management is weak
c) IT-skills staff:
- our team have self-development,
the team number is insufficient.
d) Security and Privacy:
- It's a challenge, and we have
There are policies and procedures
for accessing and carrying out the
data.
e) Budget constrain.
- a problem in providing Hardwar
we needs, it s as a rescue and not
as a development.
- lack of financial resources.
95
2- Main barriers to setup big data project within hospitals (choose up to three)
Data governance issue
Not a business management priority
Unsure of technology requirements Lack of budget Security concerns
Shortage of big data skills
Work culture Organizational complexity
Lack of leadership and commitment
Poor quality of data
3- Ranking Challenge in big data 3nd most challenges 2nd most challenge Most challenges Challenge
Data growth
Data infrastructure
Data governance- policy
Data integration
Data velocity
Data variety
Data compliance
Data visualization
96
Sample Interview 5
" Big Data Management In Gaza Strip Hospitals: Barriers And Facilitators "
Date: 24 Oct. Place: MoH Time: 11:30 A.M Interviewer: Date: Eng. Hafiz Younis Interviewee (subject number): (5)
Descriptive transcription Coding
I. Introductory Questions
1- What is your date of birth? - no response
2- What is the highest level of education you attained? Master degree in IT
. How long you are working in healthcare? 17 at MoH, The head of Programming since 2009.
What are your position and the kind of work you do?
Head of Programming Department.
We develop systems policies and develop them, supervise their implementation, and of course there is
training through IT development unit presented to the staff of the hospitals, and if there is bug "kind of
problem a program", so the modification is done, we get the improvement.
3- What do you think are three major E-hospitals/management problems?
The E- hospital is a hospital management system, At the current stage covering all the administrative system in
hospitals, the next phase will be launched in December 2017, the first computerization in the system, including the
doctor and nursing will start at the hospital Rantisi. The complete administrative system as well as complete
computerized patient services, the patient will be able to review his medical data soon through Android application
for smart phones, in 2018.
We have a set of systems that give us the E-hospital, Previously we had paper data that was electronically archived
and we were able to automate all the services provided by (laboratory, radiology , emergency departments,and
internal departments(
In computerizing anywhere in the institutions you find resistance to change, but we have a successful experience at
Major E-hospitals Problems - Data visualization is manual.
- Needs a large storage space - sometimes we cannot change
the device If the switch hit
Separated part full, Hardly
change it.
- Needs to support the issue of
network infrastructure - We reached to have central
database .
- The change to web and
Android App.
97
AL-REMAL Clinic (primary care clinic),were DoctorS and NurseS working paperless, It is possible to take review
there now, go down to the reception, the doctor arrives at the pharmacy and then the pharmacy all this treatment
without paper. This experience in the AL-REMAL clinic has made us go this way in hospitals and we are
completing the computerization of all transactions, this needs a large storage space, and the entire system is able
to handle these transactions.
As for the quality, we initially have a problem in (Rubbish Data), so at least the basic data has controlled, data has
become linked to the Civil Registry in the Ministry of the Interior and E-government, All demographics data
appear automatically as soon as the person's ID number is entered, and then the health insurance is linked to the
health insurance data.
So if the person has any obligations to government departments its appearing in our system. There are other data
that are entered through the Patient Services Department, which is a quality task and needs specialized review of
the data entry process by the Patient Services Department. Then with the beginning of 2017 a committee has been
formed in the ministry called "Data Quality" which includes the Information Systems Unit - Patient Services -
General Administration of Hospitals.
Data processing, In the past, if I had a statistic about "patients in internal departments," for example the names of
Intensive Care Unit(ICU) in each hospital were "intensive care, reanimation care units, intensive heart care." We
have now unified the code when I want to review intensive care data, all hospitals have been implemented, so the
quality issue goes well.
Big Data issue and management is different from HMIS we are currently working on, we are local, Here we suffer
from the problem of electricity and suffer from the issue of network infrastructure. The hardware which we
hardly change it, so the going for Big Data now is very difficult because of these determinants and there is a lack of
Team skills, but there is a look forward.
we have equipped a centeral storge that collects all health data in one place, database transferred from hospitals
online. Its started since 2014, all MoH systems are currently in one place and these systems have evolved and are
linked to the government systems.
In the future, if there are essential solutions to the issue of network and electricity, we may think that hospitals are
chang to Big Data, I worked in Europe in-Gaza hospitals since 2000 and the European is different. We have a
database different from the European. There are some restrictions in the care, We started with a European
integration that processed the data in a certain way until it reached the central database that was ready and matched.
98
II. Core Questions
1- What are the Challenges and Barriers for Gaza Hospitals to adopted Big Data? Relating to: f) Top management support.
We have agood job in our system, and have supports from Ministry in projects that come from donors, for example,
the setup of chronic diseases system, was interest from donors, so we requested Hardware for this process.
Therefore, the leadership in the ministry directed us to these projects and there is support for this issue and
facilitating the obstacles, but the problems exist in the financial level and this limits our expansion, The extent to
which this trend is translated into plans and strategy? There is a strategy for this stage that we have reached and it
was within the operational planning that we have set goals for each department in the unit of information systems
and development until, we reached this stage that we are proud of administration took care of computerization
because of the importance of getting the information on time and this helped a lot and gave us support from the
ministry to continue to serve the citizens and facilitate them.
g) Culure-Organization:
At the beginning of our work, there was no fixed system, the process was change continuously, and these are the
main obstacles, When we started E-hospitals the system , the switch area was large. Today, a system has been
written through a committee called "Automatization", for example, the prosses " Stages of receiving the patient
until his exit ". The process and procedures were identified and discussed through the committee and approved by
the ministry and then the process was computerized. Currently, the change and development is done in a deliberate
manner by the committee. If an adjustment is requested, it must be approved by the committee. So the story
solved.
our systems have been built on (sixi) and persistence frameworks and Web technologies, system can be integrated
to any third party systems transparently., and why we went to the web Applications? to directed some informations
to citizens through which the citizen will receive his medical evidence - his account number is the ID number.
Doctor and nurse will works on android applications, this is considered the best way to contribute medical notes
writing and insertting into the system via smart tablets and phones, rather than the process of entering through the
computer, its ll solves the problem of inserting doctor's notes. Five clinics (primary care) have been linked to the
system so that the health process is done first in primary care clinics and then in hospitals. This is our future goal
to connect all 56 clinics within the system. So gets the patient's history and will make it easier for the patient, And
the medical team between the hospital and primary care.
a) Top management support.
- strong support from the ministry
for MIS
- support from the ministry in
projects that come from donors.
- interest in internal development.
- There isn’t interesting in
outsourcing data management
technology.
- no strategic planning for the
adoption Big Data, plans focus on
internal efforts.
b) Culure-Organization:
- the size of MoH institutions, the
number of its employees, the
number of Patient Review, there
is a great pressure.
- Weak in dealing with IS from
doctors, so thay will work on
android applications
c) IT-skills staff:
- each hospital has a large set of
systems and effort, and the
number of staff in the information
systems deployed in hospitals is
very low.
- Our team did not have this
opportunity there is no
scholarship, we needs training.
99
h) IT-skills staff:
We have the Ministry of Health as a government alone, the size of its institutions and facilities, the number of its
employees, the number of reviewers of the whole society, and the great pressure, thus forcing you to follow in
terms of network, hardware, programming and the system as a whole, each hospital has a set of ecosystems ..., and
the number of staff working in IS unit in hospitals is very low, so setuation is a major problem to us. I got an
education about the subject of Big Data and worked on it, but this opportunity was not available to our team.
If we become a job requirement we will automatically learn because our team have a high readiness to acquire
skills and training.
i) Security and Privacy:
Its one of the basics of our work, and we have a policy approved by the Ministry to follow up protection, The
information center is a set of restrictions and technical protection systems, but the culture of employees in data
protection are indifferent especially user account and password among employees, this in the developed countries
is a crime, this is like official seals, the users are responsible.. We are constantly raising awareness
j) Budget constrain.
the government in a difficult financial situation, We depend on donors to implements these projects and
try as much as possible to provide some of hospitals needs through these projects, such as the automation
of cancer patients in MIS,we provided several requirements in the infrastructure was very important to
us, and also when mother and child care file done , its was very important (its cost was high).Thus, it is
resolved from period to period
d) Security and Privacy:
- But the culture of data protection
workers is indifferent to account
transfers and password transfers
among employees, so the user is
responsible.
e) Budget constrain.
- government in a difficult financial
situation.
- rely on raises projects from
donors.
- such projects depends on the
international donor.
100
2- Main barriers to setup big data project within hospitals (choose up to three)
Data governance issue
Not a business management priority
Unsure of technology requirements Lack of budget
Security concerns Shortage of big data skills
Work culture
Organizational complexity
Lack of leadership and commitment
Poor quality of data
3- Ranking Challenge in big data 3nd most challenges 2nd most challenge Most challenges Challenge
Data growth
Data infrastructure
Data governance- policy
Data integration
Data velocity
Data variety Data compliance
Data visualization
101
Sample Interview 6
" Big Data Management In Gaza Strip Hospitals: Barriers And Facilitators "
Date: 25 Oct. Place:Elshefa Hospital Time: 8:30 A.M Interviewer: Date: Dr. Reem elzeer Interviewee (subject number): (6)
Descriptive transcription Coding
I. Introductory Questions
1- What is your date of birth? -
2- What is the highest level of education you attained? Bachelor degree in computer science, Master
degree in healthcare management.
3- How long you are working in healthcare? 17 years
4- What are your position and the kind of work you do?
Head of patient-services department in Shefa Hospital in MoH.. Its department of Patient Services is
the focus of administrative, medical and technical with the patient, means we link and coordination
and follow-up with the patient from the administrative and technical aspects, and we are interested in
checking information, whether primary or medical information
5- What do you think are three major E-hospitals/management problems?
We are essentially paving our way through many difficulties, when moving from a manual system to a
computerized system, we started in Shifa Hospital since 2008, the steps emergency, outpatient clinics,
lab and radiology departments and then we move to the internal departments. the challenges were
greater than we expected, in terms of providing material needs for the system, hardware and network
infrastructure, for Shifa Hospital has a particular peculiarity because it is complex in spaced and old
buildings, It has no groundwork for networks. And the reconstruction has passed until we reached the
Optical fiber and Wi-Fi network at the level of hospitals in the Gaza Strip, especially the Ministry of
Health. Unifying service from one service station it was and still as a dilemma, we aim to provide
Major E-hospitals Problems - The main problem was storage
- Needs infrastructure.
- Need for standardized clinical
terminology
- Challenges of data entry by
caregivers.
- Deifficulties associated with
integration of hospitals
informations.
- switch to web appellation.
- Resistance to change,
especially from doctors.
- Complex workflow, make IT
staff to use web-based system.
- The current system in the
output of reports does not give
Scourcard or metrics.
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services to patients from one point, especially as the patient goes to more than one office to receive his
service, we wanted to unite the start of the service from registration and booking and the launch of
medical service easily. The size of our work is large and requires a working hand, which is already at
the minimum in hospitals, and this is reflected in the operation of the system.
For example, the doctor treats with 46 patients in the clinic to deal with the system will treat 20
patients, hence the waiting list will become long, with the shortage of medical service providers, this is
a problem.
Therefore, certain stages of implementation have been delayed because of the administrative obstacles
in the hospitals in terms of lack of technical staff, medical facilities and equipment, there are systems
that are ready and unable to implement them.
There is a problem in the organizational culture we felt working alone in the field, the medical staff
did not participate in the computing process. We are working to control and follow up the medical
information, if this information with the doctor and nursing are missing is here the problem.
Control of my data, and in the participation of individuals who provide and document health care we
still suffer.
One of the fundamentals of the work is that we have imposed ourselves as a hospital manager and have
found it useful to develop information systems and manage them.
current systems in the output of reports does not give scourcard or metrics, in practice we translate the
existing data to the indicators we want manually, and also its done through Information Systems Unit.
The system displays the data in reports through which we see laboratory data and scans. Thus, each
nursing station has a screen. We aim to have each doctor a computer that reviews patient data through
the national patient number.
We must first care about the issue of data input, Is it quality and accurate?, we have controls on the
system to control input. Our reports, which we extract from the system monthly, show some problems,
so we look for the problem and solve it with IT.
The Ministry should complete my previously mentioned points of blankness, before talking about the
quality and accuracy of the data.
The ministry has a lot of hard work, and the technical staff specialized in computerization and
information systems is tireless and work under pressure, but there is no strategic planning for the
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subject of Big Data, in the sense that if there is a problem we seek to resolve.
The first problem was in ELshifa hospitalis the switched local data to the Web, in a very short time.
How data usage and presentation, not related to team techniques, is related to the establishment of
capabilities and team training, we are ready to develop, and this technology will help us a lot.
So if want to display alerts for the doctor and the manager, you need a pharmacy store properly and a
nursing information store…. is correct ... if you do not have this, how will you show it?
These problems are not discussed in strategic plans.
II. Core Questions
4- What are the Challenges and Barriers for Gaza Hospitals to dopte Big Data? Relating to: a) Top management support: The most important thing for the development of information technology in our health system, that is there a
vision and a strategy?. Yes, computing processes was adopted, but we haven't a clear vision and a strategy to
development our technique. We follow the theory " If you have trouble start solving it".
IT is a management priority, but it is not among the first priorities, meaning that there are more important things
than moving to new data management techniques, Especially in light of the difficult financial situation. This is
associated with feelings in the culture of top management and their relevance to medicine rather than IS.
Therefore, this is reflected on MoH decision to adopte Bigdata technology. For example, if you have the
opportunity to send IS team to a training abroad or to a team of doctors with a specific surgery, The opinion
will be ready towards training doctors, this always happens.
The administration is saturated with the problems of providing medicine and treatment abroad and providing the
needs of family and equipment, in addition to administrative problems
b) Culure-Organization:
The organizational structure complexities is so large and all IT teams is distributed to the ministry's institutions
and hospitals.
There is a great array in the nature of the work because of the dimensions of work in the various ministry of
health. We have the energy to follow the primary care program, hospital program, procurement programs,
central warehouse programs, And staff evaluation, there are almost 10 programs each program no less.
Organizational structure is so large and all IT teams is spread in the ministry's institutions and hospitals.
There is a great tricky situation rooted to the nature of the work…,and there is resistance to change
especially from caregivers, when moving from one system to another, it's expected to appear within any new
a) Top management support.
- we haven't a clear vision and a
specific strategy to achieve this
vision.
- top management and their
relevance to medicine rather than
information technology, therefore
this is reflected in the decision.
b) Culure-Organization:
- organizational structure
complexities.
- There is a great array in the nature
of the work
- Resistance to change
c) IT-skills staff:
- E-hospitals was self-training,
based on the skills of this self-
learning team
- training and scholarship abroad
and to carry out the experiences
of others
-
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system
c) IT-skills staff:
The team did not receive training in Bigdata, ..but when the ministry decided to move on in
building a new system that deals with the Web and Android, our team has overcome obstacles ,and
gained knowledge from internal training, ITd teams developes new systems(e-hospital) based on
(sixi) languge, and connected (e-hospital) with e-goverment system, that now can deals on tablets
and smartphones and then will spread among the internal public and citizens, the idea of shifting
based on Self-efforts, our team has a great effort, so our team has ability to learn big data technology, If
there is an opportunity
- Despite the weak potential, they created something based on, it is a good data management system - from
training and scholarship abroad and to carry out the experiences of others. Then we will have a very cool
job in managing this data
d) Security and Privacy: Data security is so important and there are rules, regulations and protocols for the system, and there is a router
working to prevent hacking, as for the culture between the medical teams, the level of protection and
information paths between
e) Budget constrain: At the infrastructure level we hope to find supporters, They try, but with the average
limit that contributes to solving the problems. Currently, I am implementing a month-long system that I am
trying to implement, but because of the possibilities we are waiting for the process to be implemented.
And other example of internal partitions system is ready, but there is no potential to apply because lack of
human resources and medical secretary and lack of tools 30 computers or tablets and some other tools. So
the lack of money is impeded
d) Security and Privacy:
- Data security is important
e) Budget constrain.
- limit that contributes to solving
the problems
- the lack of money is impeded
- most of the funding came from
donors and supporters
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5- Main barriers to setup big data project within hospitals (choose up to three)
Data governance issue
Not a business management priority
Unsure of technology requirements Lack of budget
Security concerns
Shortage of big data skills Work culture
Organizational complexity Lack of leadership and commitment
Poor quality of data
6- Ranking Challenge in big data 3nd most challenges 2nd most challenge Most challenges Challenge
Data growth
Data infrastructure
Data governance- policy Data integration Data velocity
Data variety
Data compliance
Data visualization
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Appendix-E: Questionnaire Evaluation (List of Referees)
Academic and Professional Referees' Names and Titles
# Name Title
1- Dr. Wasim I. Habil
Associate Professor, Faculty of Commerce,
Islamic University of Gaza.
2- Dr. Khalid Abed Dahleez Assistant Professor, Faculty of Commerce,
Islamic University of Gaza.
3- Dr. Akram Sammour Assistant Professor, Faculty of Commerce,
University of Gaza.
4- Eng. Alaa Elshorafa Head of Information Technologt development
Unit, Minestry of Health
5- Ream Elzeer Head of control unit for patient care, at Al-
Shifa Medical Complex, Minestry of health.
6- Eng. Suhail Madoukh Deputy, Ministry of Telecom & information
technology.
7- Eng. Bassel Harara Masters in Business Analytics from Central
European University.
8- Eng. Mouhammed O. Hubi Software Engineer, Islamic University of
Gaza.