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

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

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

28
Image 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

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• Cognitive Errors - Example: Decoy Pricing
The Economist Purchase Options
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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.

34
Klein 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

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eHealth  Health IT
eHealth & Health IT

43
HIS
All information about health
eHealth
HMIS
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
CPOE
Automatic
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?

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

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 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

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• 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
Thailand’s eHealth Situation
• 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
Thailand’s eHealth Situation
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

Application of ICT for Clinical Care Improvement

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