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1
Telemedicine
Nawanan Theera-Ampornpunt, M.D., Ph.D.
December 26, 2019
www.SlideShare.net/Nawanan
2
2003 M.D. (First-Class Honors)
2011 Ph.D. (Health Informatics), Univ. of Minnesota
Deputy Dean for Operations
Former Assistant Dean for Informatics
Lecturer, Section for Clinical Epidemiology & Biostatistics
Faculty of Medicine Ramathibodi Hospital
Mahidol University
Interests: Health IT for Quality of Care, Social Media
IT Management, Security & Privacy
SlideShare.net/Nawanan
นวนรรน ธีระอัมพรพันธุ์ (Nawanan Theera-Ampornpunt)
Line ID: NawananT
Introduction
3
What words come to mind when you hear...
Digital Health
Transformation
4https://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
6englishmoviez.com
Rise of the Machines?
7
Controlling Artificial Intelligence
(Like Killer Robots)
http://mashable.com/2017/08/20/ai-weapons-ban-open-letter-un
8
Digitizing Healthcare?
http://www.bloomberg.com/bw/stories/2005-03-27/cover-image-the-digital-hospital
9
“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)
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
• “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
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?
19
Why Aren’t We Talk About These Words?
http://hcca-act.blogspot.com/2011/07/reflections-on-patient-centred-care.html
20
The Goal of Health Care
The answer is already obvious...
“Health”
“Care”
21
• 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
22
(IOM, 2001)(IOM, 2000) (IOM, 2011)
Landmark Institute of Medicine Reports
23
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
24Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital
To Err is Human 2: Memory
25
• Medication Errors
–Drug Allergies
–Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• Failure to follow clinical practice guidelines
Common Errors
26
Being Smart #3:
“To Err is Human”
27
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
28
Example of “Alerts & Reminders”
Reducing Errors through Alerts & Reminders
29
• 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)
30
Health IT
Health
Information
Technology
Goal
Value-Add
Means
31
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
32
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)
33
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
34
Healthtech Startup Ecosystem
www.facebook.com/HealthTechThailand/ No endorsements implied
35
What About
Telemedicine?
36
• Telemedicine (โทรเวชกรรม)
• tele- (to or at a distance) + medicine/health
• “The use of telecommunications technology for
medical diagnostic, monitoring and therapeutic
purposes when distance and/or time separates the
participants.”
• Some use telehealth to indicate care beyond
provided in medical encounters (e.g. health
education, health-related websites)
Telemedicine & Telehealth
Hersh et al (2006)
37
• Patient-Provider Communication
• Provider-Provider Communication
Broad Types of
Healthcare Communication through IT
Hersh et al (2006)
38
• Store-and-forward telemedicine
– Collect data then transmit them for subsequent interpretation
• Home-based telemedicine
– Used by health professionals to monitor physiology, test results,
images and sounds, usually collected in a patient’s home or a
nursing facility
• Office/hospital-based telemedicine
– Usually real-time clinician-patient interactions that conventionally
would require face-to-face encounters between a patient and a
health professional
3 Main Types of Telemedicine
Hersh et al (2006)
39
State of TeleHealth
Dorsey & Topol (2015)
40
• Current Trends
–Change from using telehealth to increase access to
health care to providing convenience and reducing
cost
–Expansion of telehealth to chronic conditions
–Migration of telehealth from hospitals and clinics to
home and mobile devices
State of TeleHealth
Dorsey & Topol (2015)
41
• Limitations of Telehealth
–Reimbursement
• Limited & fragmented insurance coverage of telehealth
• Potential for excess health care utilization
–Clinical issues
• Patient-physician relationship
• Quality of physical examination
• Quality of care with remote visits than with in-person
visits
State of TeleHealth
Dorsey & Topol (2015)
42Dorsey & Topol (2015)
• Limitations of Telehealth
–Clinical issues (continued)
•Potential for abuse (e.g. overprescribing of
narcotics)
•Fragmentation of care among multiple providers
–Legal issues (e.g., state licensure, liability)
–Social issues (digital divide)
State of TeleHealth
43
Legal Issues
• Privacy & Security Issues
• Liability due to Risks of Medical Errors
▪ Misdiagnosis
▪ Delayed Diagnosis & Treatment
▪ Miscommunication & Information Reliability
• Legal Compliance
▪ พรบ.สถานพยาบาล, พรบ.วิชาชีพ, พรบ.ยา, พรบ.เครื่องมือ
แพทย์ ฯลฯ
▪ Legal Documentation
44
Laws Related to Telemedicine
45
Laws Related to Telemedicine
46
Laws Related to Telemedicine
47
Laws Related to Telemedicine
48
Laws Related to Telemedicine
49
Laws Related to Telemedicine
50
Laws Related to Telemedicine
51
Thailand’s Telemedicine Efforts
52
Thailand’s Telemedicine Efforts
53
• Financing & Governance
• Medical Practice & Care Delivery
• Data Privacy & Integrity
• Legal Issues, Liability & Ethics
Key Issues on Telemedicine
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
54
• Telemedical Consultation (โทรเวชกรรมการปรึกษา)
• เป็นการให้คาปรึกษาแนะนาแก่ผู้ให้การบริบาลซึ่งเป็นผู้ประกอบวิชาชีพเวชกรรม
• Telemedical Direction (โทรเวชกรรมการอานวยการ)
• เป็นการให้คาสั่งการบริบาลแก่ผู้ให้การบริบาล ซึ่งเป็นบุคลากรสาธารณสุขที่ไม่ได้
เป็นผู้ประกอบวิชาชีพเวชกรรม
• Telemedical Care (โทรเวชกรรมการบริบาล)
• เป็นการให้การบริบาลแก่ผู้รับการบริบาลโดยตรงหรือให้แก่ผู้ให้การบริบาล ซึ่งไม่ได้
เป็นบุคลากรสาธารณสุข
• แต่ละประเภทมี accountability ตามบริบทนั้นๆ ทานองเดียวกับ
conventional medical practice
A Way to Classify Telemedicine
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
55
Telemedicine Regulatory Stakeholders
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
56
Telemedicine Conceptual Framework
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
57
Telemedicine Conceptual Framework
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
58
Telemedicine
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
59
Telemedicine
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
60
Telemedicine
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
61
• Disease-specific & specialty-specific telemedicine
medical practice guidelines (review of evidence &
balancing risk-benefits)
• Medical records documentation guidelines
• Security, privacy & identification/authentication
standards
• Harmonization among regulators
• Professional telemedicine training/certification?
Next Steps on Telemedicine
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
62
• ความจาเป็นในการแก้ไข พรบ.วิชาชีพเวชกรรม หรือกฎหมาย
อื่นๆ ที่เกี่ยวข้อง หรือออก พรบ. Telemedicine ยังไม่ชัดเจน
แต่มีความพยายามที่จะหารือกันระหว่างหน่วยงานต่างๆ
• Telemedicine Providers เริ่มเปิดให้บริการแล้ว ต้อง
monitor case ร้องเรียน หรือปัญหาที่เกิดขึ้น
Next Steps on Telemedicine
คณะอนุกรรมการพิจารณาแนวทางการใช้สื่อสารสนเทศทางการแพทย์ของผู้ประกอบวิชาชีพเวชกรรม แพทยสภา (2560-2561) (ยังไม่ใช่ Official Position)
63
Intelligent &
helpful robots
Intelligent
humanistic robots
in a human world
Machines that
replace humans for
a “better” world
HAL 9000 Data David NS-5
Dangerous killer
machines
Which Future Lies Ahead?
6464
#LessHype
#MoreHope
My Plea...
6565https://www.youtube.com/watch?v=gxz9ZVvduGc
Connecting People to a Healthy Future With
Personalized Care – Kaiser Permanente

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