application of ict for clinical care improvement

Application of ICT for

Clinical Care Improvement

Chulalongkorn University

February 17, 2017

Nawanan Theera-Ampornpunt, M.D., Ph.D.

Department of Community Medicine

Faculty of Medicine Ramathibodi Hospital

SlideShare.net/Nawanan

2

Outline

• Health & Health Information

• Health IT & eHealth

• Health Informatics as a Discipline

• Thailand’s eHealth Situation

• Current Forces

3

Health &

Health Information

4

Let’s take a look at

these pictures...

5Image Source: Guardian.co.uk

Manufacturing

6Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3

Banking

7ER - Image Source: nj.com

Healthcare (on TV)

8

(At an undisclosed nearby hospital)

Healthcare (Reality)

9

• Life-or-Death

• Difficult to automate human decisions

– Nature of business

– Many & varied stakeholders

– Evolving standards of care

• Fragmented, poorly-coordinated systems

• Large, ever-growing & changing body of knowledge

• High volume, low resources, little time

Why Healthcare Isn’t Like Any Others

10

Image Sources: http://www.ibtimes.com/google-deepminds-alphago-

program-defeats-human-go-champion-first-time-ever-2283700

http://deepmind.com/

The World of Smart Machines

11Image Source: http://www.bloomberg.com/bw/stories/2005-03-27/cover-image-the-digital-hospital

Digitizing Healthcare

12

“To computerize

the hospital”

“To go paperless”

“To become a

Digital Hospital”

“To Have

EHRs”

Why Adopting Health IT?

13

• “Don’t implement technology just for

technology’s sake.”

• “Don’t make use of excellent technology.

Make excellent use of technology.”(Tangwongsan, Supachai. Personal communication, 2005.)

• “Health care IT is not a panacea for all that

ails medicine.” (Hersh, 2004)

Some “Smart” Quotes

14

If not a “Digital Hospital”

or a “Paperless Hospital”

Then what should we aim for?

16

Back to

something simple...

17

To treat & to care for their patients to their best abilities, given limited time & resources

Image Source: http://en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)

What Clinicians Want?

18

• Safe

• Timely

• Effective

• Patient-Centered

• Efficient

• Equitable

Institute of Medicine, Committee on Quality of Health Care in America. Crossing the quality

chasm: a new health system for the 21st century. Washington, DC: National Academy

Press; 2001. 337 p.

High Quality Care

19

Information is Everywhere in Healthcare

20

“Information” in Medicine

Shortliffe EH. Biomedical informatics in the education of physicians. JAMA.

2010 Sep 15;304(11):1227-8.

21

21

WHO (2009)

Components of Health Systems

22

22

WHO (2009)

WHO Health System Framework

23

Outline

Health & Health Information

• Health IT & eHealth



• Current Forces

24

Health IT &

eHealth

25

(IOM, 2001)(IOM, 2000) (IOM, 2011)

Landmark IOM Reports

26

• To Err is Human (IOM, 2000) reported

that:

– 44,000 to 98,000 people die in U.S.

hospitals each year as a result of

preventable medical mistakes

– Mistakes cost U.S. hospitals $17 billion to

$29 billion yearly

– Individual errors are not the main problem

– Faulty systems, processes, and other

conditions lead to preventable errors

Health IT Workforce Curriculum Version

3.0/Spring 2012 Introduction to Healthcare and Public Health in the US: Regulating Healthcare - Lecture d

Patient Safety

27

• Humans are not perfect and are bound to

make errors

• Highlight problems in U.S. health care

system that systematically contributes to

medical errors and poor quality

• Recommends reform

• Health IT plays a role in improving patient

safety

IOM Reports Summary

28Image Source: (Left) http://docwhisperer.wordpress.com/2007/05/31/sleepy-heads/

(Right) http://graphics8.nytimes.com/images/2008/12/05/health/chen_600.jpg

To Err is Human 1: Attention

29Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital

To Err is Human 2: Memory

30

• Cognitive Errors - Example: Decoy Pricing

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Ariely (2008)

16

0

84

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68

32

# of

People

# of

People

To Err is Human 3: Cognition

31

• It already happens....(Mamede et al., 2010; Croskerry, 2003;

Klein, 2005; Croskerry, 2013)

What If This Happens in Healthcare?

32

Mamede S, van Gog T, van den Berge K, Rikers RM, van Saase JL, van Guldener C,

Schmidt HG. Effect of availability bias and reflective reasoning on diagnostic accuracy

among internal medicine residents. JAMA. 2010 Sep 15;304(11):1198-203.

Cognitive Biases in Healthcare

33

Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them.

Acad Med. 2003 Aug;78(8):775-80.


34Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr

2;330(7494):781-3.

“Everyone makes mistakes. But our

reliance on cognitive processes prone to

bias makes treatment errors more likely

than we think”


35

• Medication Errors

– Drug Allergies

– Drug Interactions

• Ineffective or inappropriate treatment

• Redundant orders

• Failure to follow clinical practice guidelines

Common Errors

36

Why We Need ICT

in Healthcare?

#1: Because information is

everywhere in healthcare

37

Why We Need ICT

in Healthcare?

#2: Because healthcare is

error-prone and technology

can help

38

Why We Need ICT

in Healthcare?

#3: Because access to

high-quality patient

information improves care

39

Why We Need ICT

in Healthcare?

#4: Because healthcare at

all levels is fragmented &

in need of process

improvement

40

Use of information and communications

technology (ICT) in health & healthcare

settings

Source: The Health Resources and Services Administration, Department of

Health and Human Service, USA

Slide adapted from: Dr. Boonchai Kijsanayotin

Health IT

41

Use of information and communications

technology (ICT) for health; Including• Treating patients

• Conducting research

• Educating the health workforce

• Tracking diseases

• Monitoring public health.

Sources: 1) WHO Global Observatory of eHealth (GOe) (www.who.int/goe)

2) World Health Assembly, 2005. Resolution WHA58.28

Slide adapted from: Mark Landry, WHO WPRO & Dr. Boonchai Kijsanayotin

eHealth

42

eHealth Health IT


eHealth & Health IT

43

HIS

All information about health

eHealthHMIS

mHealth

Tele-

medicine

Slide adapted from: Karl Brown (Rockefeller Foundation),

via Dr. Boonchai Kijsanayotin

More Terms...

44

Health

Information

Technology

Goal

Value-Add

Tools

Health IT: What’s in a Word?

45

All components are essential

All components should be balanced


eHealth Components: WHO-ITU Model

46

Hospital Information System (HIS) Computerized Provider Order Entry (CPOE)

Electronic

Health

Records

(EHRs)

Picture Archiving and

Communication System

(PACS)Screenshot Images from Faculty of Medicine Ramathibodi Hospital, Mahidol University

Various Forms of Health IT

47

mHealth

Biosurveillance

Telemedicine &

Telehealth

Images from Apple Inc., Geekzone.co.nz, Google, HealthVault.com and American Telecare, Inc.

Personal Health Records

(PHRs) and Patient Portals

Still Many Other Forms of Health IT

48

• Guideline adherence

• Better documentation

• Practitioner decision making or

process of care

• Medication safety

• Patient surveillance & monitoring

• Patient education/reminder

Documented Values of Health IT

49

• Master Patient Index (MPI)

• Admit-Discharge-Transfer (ADT)

• Electronic Health Records (EHRs)

• Computerized Physician Order Entry (CPOE)

• Clinical Decision Support Systems (CDS)

• Picture Archiving and Communication System (PACS)

• Nursing applications

• Enterprise Resource Planning (ERP)

Some Hospital IT - Enterprise-wide

50

• Pharmacy applications

• Laboratory Information System (LIS)

• Radiology Information System (RIS)

• Specialized applications (ER, OR, LR,

Anesthesia, Critical Care, Dietary

Services, Blood Bank)

• Incident management & reporting system

Some Hospital IT - Departmental Systems

51

The Challenge - Knowing What It Means

Electronic Medical

Records (EMRs)

Computer-Based

Patient Records

(CPRs)

Electronic Patient

Records (EPRs)

Electronic Health

Records (EHRs)

Personal Health

Records (PHRs)

Hospital

Information System

(HIS)

Clinical Information

System (CIS)

EHRs & HIS

52

Computerized Provider Order Entry (CPOE)

53

Health IT for Medication Safety

Ordering Transcription Dispensing Administration

CPOEAutomatic

Medication

Dispensing

Electronic

Medication

Administration

Records

(e-MAR)

Barcoded

Medication

Administration

Barcoded

Medication

Dispensing

54

Values

• No handwriting!!!• Structured data entry: Completeness, clarity,

fewer mistakes (?)

• No transcription errors!

• Streamlines workflow, increases efficiency

Computerized Provider Order Entry (CPOE)

55

• The real place where most of the

values of health IT can be achieved

– Expert systems

• Based on artificial intelligence,

machine learning, rules, or

statistics

• Examples: differential

diagnoses, treatment options

(Shortliffe, 1976)

Clinical Decision Support Systems (CDS)

56

– Alerts & reminders

• Based on specified logical conditions

• Examples:

– Drug-allergy checks

– Drug-drug interaction checks

– Reminders for preventive services

– Clinical practice guideline integration

Clinical Decision Support Systems (CDS)

57

Examples of “Reminders”

58Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html

Some Other CDS - Infobuttons

59Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm

Some Other CDS - Order Sets/Checklists

60Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html

Some Other CDS - Abnormal Lab Highlights

61

External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

Working

Memory

CLINICIAN

Elson, Faughnan & Connelly (1997)

Clinical Decision Making & CDS

62Image Source: socialmediab2b.com

IBM’s Watson

63Image Source: englishmoviez.com

Rise of the Machines

64Image Source: amazon.com

Smart Phones, Dumb People?

65

Smart Hospital,

Dumb...?

66

• CDS as a replacement or supplement of

clinicians?– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)

The “Greek Oracle” Model

The “Fundamental Theorem” Model

Friedman (2009)

Wrong Assumption

Correct Assumption

Proper Roles of CDS

67

The “Human Factor”

• Alert fatigue

Unintended Consequences of Health IT

68

Workarounds

69

Hospital A Hospital B

Clinic C

Government

Lab Patient at Home

The Big Picture: Health Information Exchange (HIE)

70

Outline


Health IT & eHealth



• Current Forces

71

Health Informatics

as a Discipline

72

M/B/H Informatics As A Field

(Shortliffe, 2002)

73(Hersh, 2009)

M/B/H Informatics As a Discipline

74

Biomedical/Health

Informatics

Computer & Information

Science

Engineering

Cognitive &

Decision Science

Social Sciences

(Psychology, Sociology, Linguistics,

Law & Ethics)

Statistics &

Research Methods Medical

Sciences & Public Health

Management

Library Science,

Information Retrieval,

KM

And More!

M/B/H Informatics & Other Fields

75

Outline


Health IT & eHealth

Health Informatics as a Discipline


• Current Forces

76

Thailand’s

eHealth Situation

77

eHealth in Thailand: The current status. Stud Health Technol Inform

2010;160:376–80, Presented at MedInfo2010 South Africa

Thailand’s eHealth: 2010

78Slide adapted from: Dr. Boonchai Kijsanayotin

Thailand: Unbalanced Development

79

eHealth Applications

Enabling Policies & Strategies

Foundation Policies & Strategies

• Services

• Applications

• Software

• Standards & Interoperability

• Capability Building

• Leadership & Governance

• Legislation & Policy

• Strategy & Investment

• Infrastructure


eHealth Development Model

80Slide adapted from: Dr. Boonchai Kijsanayotin

Thailand’s eHealth Development

81

Silo-type systems

Little integration and interoperability

Mostly aim for administration and management

40% of work-hours spent on managing reports and documents

Lack of national leadership and governance body

Inadequate HIS foundations development

Slide adapted from: Boonchai Kijsanayotin

Thailand’s eHealth Situation

82

Section 1 Hospital Profile

Section 2 IT Adoption & Use

Profile

Section 3 Respondent’s

Information

Thailand’s Health IT Adoption

83

• 4 of 1,302 hospitals ineligible

• Response rate 69.9%Characteristic Overall Responding

Hospitals

Non-

Responding

Hospitals

N of eligible hospitals 1,298 908 390

Bed size** 106.9 117.5 82.9

Public status**

Private

Public

24.0%

76.0%

17.4%

82.6%

39.2%

60.8%

Geography*

Central

East

North

Northeast

South

West

33.4%

7.5%

11.1%

27.1%

15.3%

5.6%

31.1%

7.8%

13.5%

26.9%

14.9%

5.8%

39.0%

6.7%

5.4%

27.7%

16.2%

5.1%

*p < 0.01, **p < 0.001.

Nationwide Survey Results

84Pongpirul et al., 2004

Vendor/Product Distribution (2004)

85

Vendor/Product Distribution (2011)

Theera-Ampornpunt, 2011

86

Estimate (Partial or Complete Adoption) Nationwide

Basic EHR, outpatient 86.6%

Basic EHR, inpatient 50.4%

Basic EHR, both settings 49.8%

Comprehensive EHR, outpatient 10.6%

Comprehensive EHR, inpatient 5.7%

Comprehensive EHR, both settings 5.3%

Order entry of medications, outpatient 96.5%

Order entry of medications, inpatient 91.4%

Order entry of medications, both settings 90.2%

Order entry of all orders, outpatient 88.6%

Order entry of all orders, inpatient 81.7%

Order entry of all orders, both settings 79.4%

Health IT Adoption Estimates

87

• High IT adoption rates

• Drastic changes in adoption landscape

• Local context might play a role

– Supply Side

– Demand Side

• International Comparison

– Relatively higher adoption

THAIS: Discussion

88

Outline


Health IT & eHealth



• Current Forces

89

Current Forces

90

International

• Technology Trends

• Standards & Interoperability Trends

• eHealth Successes & Failures– UK NHS

– US Meaningful Use

– Nordic Countries

• International eHealth Networks– International Medical Informatics Association (IMIA)

– American Medical Informatics Association (AMIA)

– Asia eHealth Information Network (AeHIN)

Current Forces

91

URGES Member States:

(1) to consider, as appropriate, options to collaborate with

relevant stakeholders, including national authorities, relevant ministries,

health care providers, and academic institutions, in order to draw up a

road map for implementation of ehealth and health data standards at

national and subnational levels;

(2) to consider developing, as appropriate, policies and

legislative mechanisms linked to an overall national eHealth strategy, in

order to ensure compliance in the adoption of ehealth and health data

standards by the public and private sectors, as appropriate, and the

donor community, as well as to ensure the privacy of personal clinical

data;

http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R24-en.pdf

World Health Assembly Resolution WHA66.24 (2013) on

eHealth Standardization & Interoperability

92

(3) to consider ways for ministries of health and public

health authorities to work with their national representatives

on the ICANN Governmental Advisory Committee in order to

coordinate national positions towards the delegation,

governance and operation of health-related global top-level

domain names in all languages, including “.health”, in the

interest of public health;

http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R24-en.pdf

World Health Assembly Resolution WHA66.24 (2013) on

eHealth Standardization & Interoperability

93

Domestic

• Thailand’s Health Insurance Trends

• Increased Hospital IT Adoption

• Demands for Data & Information Exchange

in Thailand’s Healthcare

• Thailand’s e-Transaction Trends

• Consumer IT Behavior Trends

Current Forces

94

Outline


Health IT & eHealth



Current Forces

95Image Source: http://twinstrivia.com/2013/05/20/the-road-to-minnesota-is-long-and-hard/

The Journey Beyond:

A Long and Winding Road

http://twinstrivia.com/2013/05/20/the-road-to-minnesota-is-long-and-hard/