Presented at the Department of Medical Services, Ministry of Public Health on January 18, 2022

1. Digital Health Transformation
for Health Executives
นพ.นวนรรน ธีระอัมพรพันธุ์
18 ม.ค. 2565
www.SlideShare.net/Nawanan

2. 2003 M.D. (First-Class Honors)
2011 Ph.D. (Health Informatics), Univ. of Minnesota
Deputy Dean for Operations
Lecturer, Department of Clinical Epidemiology & Biostatistics
Faculty of Medicine Ramathibodi Hospital
Mahidol University
Interests: Health IT for Quality of Care, Social Media
IT Management, Security & Privacy
nawanan.the@mahidol.ac.th
SlideShare.net/Nawanan
นพ.นวนรรน ธีระอัมพรพันธุ์ Dr. Nawanan Theera-Ampornpunt
Introduction

3. What ideas come to mind?
Digital Health
Transformation
When you think of

4. https://medium.com/@marwantarek/it-is-the-perfect-storm-ai-cloud-bots-iot-etc-4b7cbb0481bc

5. http://www.ibtimes.com/google-deepminds-alphago-program-defeats-human-go-champion-first-time-ever-2283700
http://deepmind.com/ http://socialmediab2b.com
An Era of Smart Machines

6. englishmoviez.com
Rise of the Machines?

7. Digitizing Healthcare?
http://www.bloomberg.com/bw/stories/2005-03-27/cover-image-the-digital-hospital

8. “Big data is like teenage sex:
everyone talks about it,
nobody really knows how to do it,
everyone thinks everyone else is doing it,
so everyone claims they are doing it...”
-- Dan Ariely @danariely (2013)
Substitute “Big data” with “AI”, “Blockchain”, “IoT”
of your choice.
-- Nawanan Theera-Ampornpunt (2018)

9. A B C D
Artificial
Intelligence
Blockchain Cloud Big Data

10. Hype vs. Hope
Jeremy Kemp via http://en.wikipedia.org/wiki/Hype_cycle
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp

11. Gartner Hype Cycle 2017
https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/

12. “Smart” Machines?
https://www.bbc.com/news/business-47514289
https://www.standardmedia
.co.ke/article/2001318679/e
thiopian-airlines-crash-
investigators-reach-
conclusion

13. A Real-Life Personal Story of
My Failure (as a Doctor and as
a Son) in Misdiagnosing
My Mom
Would AI Help?

14. • Nothing is certain in medicine &
health care
• Large variations exist in patient
presentations, clinical course,
underlying genetic codes, patient &
provider behaviors, biological
responses & social contexts
Why Clinical Judgment Is Still Necessary?

15. • Most diseases are not diagnosed by
diagnostic criteria, but by patterns of
clinical presentation and perceived
likelihood of different diseases given
available information (differential
diagnoses)
• Human is good at pattern
recognition, while machine is good at
logic & computations
Why Clinical Judgment Is Still Necessary?

16. • Machines are (at best) as good as
the input data
–Not everything can be digitized or
digitally acquired
–Not everything digitized is accurate
(“Garbage In, Garbage Out”)
• Experience, context & human touch
matters
Why Clinical Judgment Is Still Necessary?

17. Health &
Health Information

18. Image Source: https://en.wikipedia.org/wiki/Industrial_robot (KUKA Roboter GmbH)
“Smart” Manufacturing

19. Image Sources: http://isarapost.net/home/?p=17760
http://www.telecomjournalthailand.com/ตอบโจทย์โมเดลทางธุรกิจ/
“Smart” Banking

20. ER - Image Source: nj.com
Healthcare (On TV)

21. (At an undisclosed hospital)
Healthcare (Reality)

22. • 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 (Yet) “Smart”?

23. “To computerize
the hospital”
“To go paperless”
“To become a
Digital Hospital”
“To Have
EHRs”
Why Adopting Health IT?

24. • “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

25. Digitization 
Digital Transformation

26. Being Smart #1:
Stop Your
“Drooling Reflex”!!

27. Being Smart #2:
Focus on Information &
Process Improvement,
Not Technology

28. If not “Digital Hospital”
or “Paperless Hospital”
Then What Should We
Aspire to Be?

29. “Smart Hospital”

30. https://www.youtube.com/watch?v=gxz9ZVvduGc
Connecting People to a Healthy Future With
Personalized Care – Kaiser Permanente

31. Back to
something simple...

32. 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?

33. Why Aren’t We Talk About These Words?
http://hcca-act.blogspot.com/2011/07/reflections-on-patient-centred-care.html

34. The Goal of Health Care
The answer is already obvious...
“Health”
“Care”

35. • 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

36. 3 Sciences for Education of 21st Century
Clinicians
Health
Systems
Science
Clinical
Science
Basic
Science
Skochelak, SE, Hawkins, RE, et al.,
Eds. (2017). Health Systems
Science. New York, NY, Elsevier.

37. 3 Sciences for Education of 21st Century
Clinicians
Skochelak, SE, Hawkins, RE, et al.,
Eds. (2017). Health Systems
Science. New York, NY, Elsevier.
Health
Systems
Science
Clinical
Science
Basic
Science
• Health Care Delivery System
• Value in Health Care
• Patient Safety
• Quality Improvement
• Teamwork & Team Science
• Leadership in Health Care
• Clinical Informatics

38. (IOM, 2001)
(IOM, 2000) (IOM, 2011)
Landmark Institute of Medicine Reports

39. • 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
Summary of These Reports

40. Information Is Everywhere in Healthcare

41. Wisdom
Knowledge
Information
Data
Data-Information-Knowledge-Wisdom (DIKW) Pyramid
Value of Data & Information

42. 42
42
WHO (2009)
Components of Health Systems

43. WHO Six Building Blocks of Health Systems
WHO (2009)

44. Proposed Modified WHO Health Systems
Framework
https://healthsystemsglobal.org/news/a-new-era-for-the-who-health-system-building-blocks/ (2014)

45. Hospital Information System (HIS) Computerized Physician Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
Picture Archiving and
Communication System
(PACS)
Various Forms of Health IT

46. m-Health
Health Information
Exchange (HIE)
Biosurveillance
Telemedicine &
Telehealth
Images from Apple Inc., Geekzone.co.nz, Google, PubMed.gov, and American Telecare, Inc.
Personal Health Records
(PHRs)
Health IT Beyond Hospitals

47. Ordering Transcription Dispensing Administration
Computerized
Physician
Order Entry
(CPOE)
Automatic
Medication
Dispensing
Electronic
Medication
Administration
Records
(e-MAR)
Barcoded
Medication
Administration
Barcoded
Medication
Dispensing
Health IT for Medication Safety

48. • Safe
–Drug allergies
–Medication Reconciliation
• Timely
–Complete information at point of
care
• Effective
–Better clinical decision-making
Being Smart in Healthcare

49. • Efficient
–Faster care
–Time & cost savings
–Reducing unnecessary tests
• Equitable
–Access to providers & knowledge
• Patient-Centered
–Empowerment & better self-care
Being Smart in Healthcare

50. 50
• Perception errors
Image Source: interaction-dynamics.com
To Err Is Human 1: Perception

51. 51
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 2: Attention

52. 52
Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University
To Err Is Human 3: Memory

53. 53
• 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 4: Cognition

54. 54
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”
Cognitive Biases in Healthcare

55. 55
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making

56. 56
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Possible Human Errors
Possibility of
Human Errors

57. 57
CLINICAL DECISION
SUPPORT SYSTEMS
(CDS)

58. 58
• Clinical Decision Support (CDS) “is a
process for enhancing health-related
decisions and actions with pertinent,
organized clinical knowledge and patient
information to improve health and healthcare
delivery” (Including both computer-based &
non-computer-based CDS)
(Osheroff et al., 2012)
What Is A CDS?

59. 59
• The real place where most of the values
of health IT can be achieved
• There are a variety of forms and nature
of CDS
Clinical Decision Support
Systems (CDS)

60. 60
• Expert systems
–Based on artificial
intelligence, machine
learning, rules, or
statistics
–Examples: differential
diagnoses, treatment
options
CDS Examples
Shortliffe (1976)

61. 61
• Alerts & reminders
–Based on specified logical conditions
• Drug-allergy checks
• Drug-drug interaction checks
• Drug-lab interaction checks
• Drug-formulary checks
• Reminders for preventive services or certain actions
(e.g. smoking cessation)
• Clinical practice guideline integration (e.g. best
practices for chronic disease patients)
CDS Examples

62. 62
Example of “Reminders”

63. 63
• Reference information or evidence-
based knowledge sources
–Drug reference databases
–Textbooks & journals
–Online literature (e.g. PubMed)
–Tools that help users easily access
references (e.g. Infobuttons)
CDS Examples

64. 64
Infobuttons
Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html

65. 65
• Pre-defined documents
–Order sets, personalized “favorites”
–Templates for clinical notes
–Checklists
–Forms
• Can be either computer-based or
paper-based
CDS Examples

66. 66
Order Sets
Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm

67. 67
• Simple UI designed to help clinical
decision making
–Abnormal lab highlights
–Graphs/visualizations for lab results
–Filters & sorting functions
CDS Examples

68. 68
Abnormal Lab Highlights
Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html

69. 69
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Abnormal lab
highlights

70. 70
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Order Sets

71. 71
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Drug-Allergy
Checks

72. 72
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Drug-Drug
Interaction
Checks

73. 73
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Clinical Practice
Guideline
Alerts/Reminders

74. 74
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Integration of
Evidence-Based
Resources (e.g.
drug databases,
literature)

75. 75
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Diagnostic/Treatment
Expert Systems

76. Being Smart #3:
“To Err is Human”

77. Being Smart #4:
Link IT Values to
Quality (Including Safety)

78. “Ten Commandments” for Effective CDS
• Speed is Everything
• Anticipate Needs and Deliver in Real Time
• Fit into the User’s Workflow
• Little Things (like Usability) Can Make a Big Difference
• Recognize that Physicians Will Strongly Resist Stopping
• Changing Direction Is Easier than Stopping
• Simple Interventions Work Best
• Ask for Additional Information Only When You Really Need It
• Monitor Impact, Get Feedback, and Respond
• Manage and Maintain Your Knowledge-based Systems
(Bates et al., 2003)

79. Health IT
Health
Information
Technology
Goal
Value-Add
Means

80. ภาพรวมของงานด้าน Health IT
Intra-Hospital IT
• Electronic Health Records &
Health IT for Quality & Safety
• Digital Transformation
• AI, Data Analytics
• Hospital IT Quality
Improvement (HA-IT)
Inter-Hospital IT
• Health Information
Exchange (HIE)
Extra-Hospital IT
• Patients: Personal
Health Records (PHRs)
• Public Health: Disease
Surveillance & Analytics
Patient
at Home

81. Strategic
Operational
Clinical
Administrative
LIS
Health Information Exchange
Business
Intelligence
Word
Processor
Social
Media
PACS
Personal Health Records
Clinical Decision Support Systems
Computerized Physician Order Entry
Electronic Health Records
Admission-Discharge-Transfer
Master Patient Index
Enterprise Resource Planning
Vendor-Managed Inventory
Customer Relationship Management
4 Quadrants of Hospital IT

82. ภาพรวมของงานด้าน Health IT
Intra-Hospital IT
• Electronic Health Records &
Health IT for Quality & Safety
• Digital Transformation
• AI, Data Analytics
• Hospital IT Quality
Improvement (HA-IT)
Inter-Hospital IT
• Health Information
Exchange (HIE)
Extra-Hospital IT
• Patients: Personal
Health Records (PHRs)
• Public Health: Disease
Surveillance & Analytics
Patient
at Home

83. Hospital A Hospital B
Clinic D
Policymakers
Patient at
Home
Hospital C
HIE Platform
Health Information Exchange (HIE)

84. https://www.youtube.com/watch?v=MuoDaJAqQ6c

85. WHO & ITU
Achieving Health Information Exchange (HIE)

86. My Personal Journey

87. A Humble Beginning

88. An Innocent First Encounter

89. Journey as a Medical Student
2nd Year

90. 3rd Year
Journey as a Medical Student

91. Extracurricular Activities

92. A Confused Medical Student
Image Sources: (Left) http://static.brazencareerist.com/brazenlife/2012/10/confused-250x250.jpg
(Right) http://www.nmsu.edu/~counsel/career/images/bw-confused-student.jpg

93. Journey as a Medical Student
5th Year
Image Source: (Right) http://www.flickr.com/photos/99979595@N00/236508120

94. อ. นพ.ยงยุทธ ขจรธรรม
อดีตอาจารย์ภาควิชาเวชศาสตร์ชุมชน รามาธิบดี
“ครูผู้จุดประกาย” ของผม
Image Source: http://www.flickr.com/photos/99979595@N00/236508120

95. Medical Informatics (Now Known As
Biomedical and Health Informatics)
• “The field that is concerned with the
optimal use of information, often aided
by the use of technology, to improve
individual health, health care, public
health, and biomedical research”
(Hersh, 2009)
The Eye-Opening Field
Shortliffe et al. (2001)

96. ศ.คลินิก นพ.พงษ์ศักดิ์ โค้วถิิยย์
อาจารย์ภาควิชากุมารเวชศาสตร์ รามาธิบดี
อดีตอาจารย์ผู้ดูแล นศพ. ของภาควิชา
Externship: A Critical Junction
Image Source: http://www.flickr.com/photos/99979595@N00/236508120

97. One Chapter Ends, Another Begins
M.D. Commencement (2003)

98. A Journey in the New Direction
2011 Ph.D. (Health Informatics) Commencement

99. • As the second formally-trained M.D., Ph.D. in
Health Informatics in Thailand, I am driven
and socially obligated...
• To promote personal & population health
through establishment of sustainable
foundations for eHealth and strengthening of
the field of Biomedical and Health Informatics
in Thailand before my end of life.
My “Mission in Life”

100. https://www.hfocus.org/content/2016/02/11783
https://www.hfocus.org/content/2016/03/11968
https://www.hfocus.org/content/2016/09/12671

101. ข้อเถนอของถมาคมเวชถารถนเทศไทย ย่อ รมว.ถธ.
(ศ. นพ.รัชยะ รัชยะนาวิน) เมื่อ พ.ศ. 2557

102. ข้อเถนอของถมาคมเวชถารถนเทศไทย ย่อ รมว.ถธ.
(ศ. นพ.รัชยะ รัชยะนาวิน) เมื่อ พ.ศ. 2557

103. ข้อเถนอของถมาคมเวชถารถนเทศไทย ย่อ รมว.ถธ.
(ศ. นพ.รัชยะ รัชยะนาวิน) เมื่อ พ.ศ. 2557

104. Areas of Health Informatics
Patients &
Consumers
Providers &
Patients
Healthcare
Managers, Policy-
Makers, Payers,
Epidemiologists,
Researchers
Copyright  Nawanan Theera-Ampornpunt (2018)
Clinical
Informatics
Public
Health
Informatics
Consumer
Health
Informatics

105. Incarnations of Health IT
Clinical
Informatics
Public
Health
Informatics
Consumer
Health
Informatics
HIS/CIS
EHRs
Computerized Physician
Order Entry (CPOE)
Clinical Decision
Support Systems
(CDS) (including AI)
Closed Loop
Medication
PACS/RIS
LIS
Nursing
Apps
Disease Surveillance
(Active/Passive)
Business
Intelligence &
Dashboards
Telemedicine
Real-time Syndromic
Surveillance
mHealth for Public
Health Workers &
Volunteers
PHRs
Health Information
Exchange (HIE)
eReferral
mHealth for
Consumers
Wearable
Devices
Social
Media
Copyright  Nawanan Theera-Ampornpunt (2018)

106. Where We Are Today...
Copyright  Nawanan Theera-Ampornpunt (2018)
Clinical
Informatics
Public
Health
Informatics
Consumer
Health
Informatics
Technology that
focuses on the sick,
not the healthy
Silos of data
within hospital
Poor/unstructured
data quality
Lack of health data
outside hospital
Poor data
integration across
hospitals/clinics
Poor data integration
for monitoring &
evaluation
Poor data quality (GIGO)
Finance leads
clinical outcomes
Poor IT change
management
Cybersecurity
& privacy risks
Few real examples
of precision
medicine
Little access
to own
health data
Poor patient
engagement
Poor accuracy
of wearables Lack of evidence
for health values
Health literacy
Information 
Behavioral
change
Few standards
Lack of health IT
governance

107. • 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
Clinical Decision Support Systems (CDS)

108. Being Smart #5:
Don’t Replace
Human Users.
Use ICT to Help Them
Perform Smarter & Better.

109. Some Risks of Clinical Decision Support Systems
• Alert Fatigue
Unintended Consequences of Health IT

110. Workarounds
Unintended Consequences of Health IT

111. • “Unanticipated and unwanted effect of health IT
implementation” (ucguide.org)
• Must-read resources
– Ash JS, Berg M, Coiera E. Some unintended consequences of
information technology in health care: the nature of patient
care information system-related errors. J Am Med Inform
Assoc. 2004 Mar-Apr;11(2):104-12.
– Campbell, EM, Sittig DF, Ash JS, et al. Types of Unintended
Consequences Related to Computerized Provider Order
Entry. J Am Med Inform Assoc. 2006 Sep-Oct; 13(5): 547-556.
– Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel
SE, Strom BL. Role of computerized physician order entry
systems in facilitating medication errors. JAMA. 2005 Mar
9;293(10):1197-203.
Unintended Consequences of Health IT

112. Koppel et al. (2005)
Unintended Consequences of Health IT

113. Being Smart #6:
Health IT Also Have
Risks &
Unintended Consequences

114. Technology
Process
People
Balanced Focus of Informatics

115. Being Smart #7:
Balance Your Focus
(People, Process, Technology)

116. Fundamental Theorem of Informatics
(Friedman, 2009)

117. • Health IT ต้อง Follow และ Support Health System Vision
(Health Goals นา Technology)
• ต้องครอบคลุมทั้ง Health System (ไม่จากัดเฉพาะกระทรวง
สาธารณสุข)
• ควรพยายามทาทุกอย่างให้เป็นเรื่องเดียวกัน เช่น Health Information
Exchange (HIE), Personal Health Records (PHRs), ระบบ
สารสนเทศใน Primary Care, ระบบสารสนเทศในการแพทย์ฉุกเฉินและ
ระบบส่งต่อ ฯลฯ
• Balance Quick Wins กับ Long-Term Changes
หลักการถาหรับ National Health IT Master Plan

118. • มุ่งเน้น Digital Transformation แต่ไม่ใช่เพียง Digitization
• ไม่ยึดติดเทคโนโลยีใดเทคโนโลยีหนึ่ง เพราะทุกเทคโนโลยีมีข้อดีข้อเสีย
จึงต้องเอาโจทย์ (Health Goals) นา วิเคราะห์ Information Needs &
Information Gaps และข้อดีข้อเสียของแต่ละ Technology
Alternatives แล้วตัดสินใจ
• Policymakers กับ Academics ต้องทางานด้วยกัน
• มอง Information เป็นองค์ประกอบหนึ่งของระบบสุขภาพ (WHO Six
Building Blocks) ที่สัมพันธ์กับองค์ประกอบอื่น
หลักการถาหรับ National Health IT Master Plan

119. • ควรผลักดันการสร้าง National Digital Health Platform โดยพิจารณา
ออก พ.ร.บ.สุขภาพดิจิทัล เพื่อ
• ปลดล็อกข้อจากัดเรื่องการแลกเปลี่ยนข้อมูลของผู้ป่วย
• มีอานาจสั่งการทั้งสถานพยาบาลของรัฐ (ในและนอก สธ.) และ
เอกชน
• Ensure การบริหารจัดการที่คล่องตัวกว่าส่วนราชการ
• จัดตั้ง “ถานักงานถุขภาพดิจิทัล” (Digital Health Agency:
DHA) เป็น Governance Body
ข้อเถนอเชิงนโยบาย

120. Good
Governance
Integrated High-
Quality
Health Information
Well-Designed
Information
Systems
Vibrant
Digital Health
Ecosystem
Excellent Care Processes &
Health Outcomes
Sustainable
Economy &
Society
Envisioning Smart Health Thailand

121. ▪ An informatics doctor thinks like a doctor but
designs IT applications to help other doctors
▪ Digital Health Transformation is, first and foremost,
about Health Care, not digital technologies
▪ EHRs & CDS are examples of useful Health IT
▪ Human decisions are still necessary in healthcare,
so many health IT should work to support human
decisions as CDS
▪ The road toward Thailand’s Health IT Reform is still
unclear & uncertain
Summary