Presented at the 32th Naval Medical Department Academic Conference: Medical Challenges in Disruptive Era, Naval Medical Department, Chonburi, Thailand on September 5, 2019

1. Disruption ทางการแพทย์:
Lure of Technology or Reality of Healthcare?
กรมแพทย์ทหารเรือ
นพ.นวนรรน ธีระอัมพรพันธุ์
5 ก.ย. 2562
www.SlideShare.net/Nawanan

2. What words come to mind when you hear...
Digital Health
Transformation

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

4. 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

5. englishmoviez.com
Rise of the Machines?

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

7. “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)

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

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

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

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

12. • 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?

13. • 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?

14. • 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?

15. Health &
Health Information

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

17. • “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

18. Digitization 
Digital Transformation

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

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

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

22. “Smart Hospital”

23. Back to
something simple...

24. 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?

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

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

27. • 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

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

29. • 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

30. Being Smart #3:
“To Err is Human”

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

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

33. 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

34. Strategic
Operational
ClinicalAdministrative
LIS
Health Information ExchangeBusiness
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

35. 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

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

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

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

39. 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

40. 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)

41. 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 hospitalPoor/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

42. • 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)

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

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

45. Workarounds
Unintended Consequences of Health IT

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

47. Technology
ProcessPeople
Balanced Focus of Informatics

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

49. Envisioning a Smart Health Thailand

50. 50
#LessHype
#MoreHope
My Plea...