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
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?
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
23. 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?
24. Why Aren’t We Talk About These Words?
http://hcca-act.blogspot.com/2011/07/reflections-on-patient-centred-care.html
25. The Goal of Health Care
The answer is already obvious...
“Health”
“Care”
26. • 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. • 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
32. 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. 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
35. Hospital A Hospital B
Clinic D
Policymakers
Patient at
Home
Hospital C
HIE Platform
Health Information Exchange (HIE)
36. 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
37. 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)
38. 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
39. • 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)
40. Being Smart #6:
Don’t Replace
Human Users.
Use ICT to Help Them
Perform Smarter & Better.
41. Some Risks of Clinical Decision Support Systems
• Alert Fatigue
Unintended Consequences of Health IT