Presented at the Healthcare Technology Summit 2019 in Pathum Thani, Thailand on April 25, 2019

1. Teenage Sex of The 21st Century:
AI, Blockchain, Cloud and Big Data;
Everyone Talks About It!!!!
Nawanan Theera-Ampornpunt, M.D., Ph.D.
April 25, 2019
www.SlideShare.net/Nawanan

2. Overview การปฏิรูปประเทศเรื่อง Health IT
Intra-Hospital IT
•Digital Health Records
(Electronic Health Records)
•Digital Transformation
•AI, Data Analytics
•Hospital IT Quality
Improvement (HA-IT)
Inter-Hospital IT
•Health Information
Exchange (HIE)
Extra-Hospital IT
•Personal Health
Records (PHRs) Patient
at Home

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

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

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

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

12. Digitization 
Digital Transformation

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. Standard view
▪ With uncertainties around new technology,
“scientific evidence counsels caution and
prudence.”
▪ Evidence & reason determine appropriate level
of caution
▪ If such systems improve care at acceptable
cost in time & money, there’s an obligation to
use it
▪ Follows evolving evidence and standards of
care
Goodman & Miller. Chapter 10: Ethics and Health Informatics: Users, Standards, and Outcomes.
In Shortliffe (3rd Edition).
Appropriate Use of Health IT

18. Standard view
▪ For computer-assisted clinical diagnosis CDS,
human cognitive processes are more suited to
complex task of diagnosis than machine, and
should not be overridden or trumped by
computers.
▪ When adequate CDS tools are developed, they
should be viewed and used as supplementary
and subservient to human clinical judgment
Goodman & Miller. Chapter 10: Ethics and Health Informatics: Users, Standards, and Outcomes.
In Shortliffe (3rd Edition).
Appropriate Use of Health IT

19. Fundamental Theorem of Informatics
(Friedman, 2009)

20. Standard view
▪ Practitioners have obligation to use tools
responsibly, through adequate training &
understanding the system’s abilities &
limitations
▪ Practitioners must not ignore their clinical
judgment reflexively when using CDS.
Goodman & Miller. Chapter 10: Ethics and Health Informatics: Users, Standards, and Outcomes.
In Shortliffe (3rd Edition).
Appropriate Use of Health IT

21. “Smart Hospital”

22. So What Exactly Is Smart Healthcare?
Image Source: http://cdn2.hubspot.net/hub/134568/file-1208368053-jpg/6-blind-men-hans.jpg

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

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

25. • 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

26. 26
26
WHO (2009)
Components of Health Systems

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

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

29. My Life-Long Dream...

30. 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

31. 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)

32. 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

33. Healthtech Startup Ecosystem
www.facebook.com/HealthTechThailand/ No endorsements implied

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

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

36. 36
My Plea...
Less Fancy Roofs
More Enabling Foundations

37. 37
#LessHype
#MoreHope
My Plea...