This document provides an overview of a presentation on health IT for Vichaiyut Hospital's 17th Conference in 2018. It was presented by Nawanan Theera-Ampornpunt, an assistant dean for informatics and lecturer at Mahidol University. The presentation discusses how healthcare differs from manufacturing and banking in its complexity, and argues that healthcare can still benefit from technology by focusing on information and process improvement rather than just implementing technology. It also summarizes landmark reports calling for healthcare reform and the role health IT can play in improving quality and patient safety.

1. Health IT for All
For Vichaiyut Hospital’s 17th Conference 2018
Nawanan Theera-Ampornpunt
August 9, 2018

2. 2
2003 Doctor of Medicine (First-Class Honors)
2011 Ph.D. (Health Informatics), Univ. of Minnesota
Assistant Dean for Informatics
Lecturer, Department of Community Medicine
Faculty of Medicine Ramathibodi Hospital,
Mahidol University
Interests: Health IT for Quality of Care,
IT Management, Security & Privacy
nawanan.the@mahidol.ac.th
SlideShare.net/Nawanan
Introduction

3. 3
Health &
Health Information

4. 4
Let’s take a look at
these pictures...

5. 5Image Source: https://en.wikipedia.org/wiki/Industrial_robot (KUKA Roboter GmbH)
“Smart” Manufacturing

6. 6
Image Sources: http://isarapost.net/home/?p=17760
http://www.telecomjournalthailand.com/ตอบโจทย์โมเดลทางธุรกิจ/
“Smart” Banking

7. 7ER - Image Source: nj.com
Healthcare (On TV)

8. 8
(At an undisclosed hospital)
Healthcare (Reality)

9. 9
• 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”?

10. 10
Input Process Output
Assembling
Manufacturing
Raw Materials Finished
Goods
Value-Add
- Innovation
- Design
- QC
But...Are We That Different?

11. 11
Input Process Output
Patient Care
Health care
Sick Patient Well Patient
Value-Add
- Technology & medications
- Clinical knowledge & skilled providers
- Quality of care; process improvement
- Customer service
- Information
But...Are We That Different?

12. 12
• Large variations & contextual dependence
Input Process Output
Patient
Presentation
Decision-
Making
Biological
Responses
Standardizing Healthcare

13. 13
Digitizing Healthcare
Image Source: http://www.bloomberg.com/bw/stories/2005-03-27/cover-image-the-digital-hospital

14. 14
“To computerize
the hospital”
“To go paperless”
“To become a
Digital Hospital”
“To have
EHRs”
Why Adopting Health IT?

15. 15
• “Don’t implement technology just for
technology’s sake.”
• “Don’t make use of excellent technology.
Make excellent use of technology.”
• “Health care IT is not a panacea for all that
ails medicine.” (Hersh, 2004)
Some “Smart” Quotes

16. 16
Being Smart #1:
Stop Your
“Drooling Reflex”!!

17. 17
Being Smart #2:
Focus on Information &
Process Improvement,
Not Technology

18. 18
If Not
“Paperless Hospital”
Then What?

19. “Smart Hospital”

20. 20
And How Does
“Smart Hospital”
Differ from
Paperless Hospital?

21. 21
Connecting People to a Healthy Future With
Personalized Care – Kaiser Permanente
https://www.youtube.com/watch?v=gxz9ZVvduGc

22. 22
Back to
something simple...

23. 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. 24
• 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

25. 25
Information is Everywhere in Healthcare

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

27. 27
(IOM, 2001)(IOM, 2000) (IOM, 2011)
Landmark Institute of Medicine Reports

28. 28
Summary of These Reports
• 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

29. 29
Being Smart #3:
“To Err is Human”

30. 30
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making

31. 31
Example of “Alerts & Reminders”
Reducing Errors through “Alerts & Reminders”

32. 32
Documented Values of Health IT
• Guideline adherence
• Better documentation
• Practitioner decision making or
process of care
• Medication safety
• Patient surveillance &
monitoring
• Patient education/reminder

33. 33
Being Smart #4:
Link IT Values to
Quality (Including Safety)

34. 34
Health
Information
Technology
Goal
Value-Add
Tools
Health IT: What’s in a Word?

35. 35
Hospital Information System (HIS) Computerized Physician Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
Picture Archiving and
Communication System
(PACS)
Various Forms of Health IT

36. 36
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

37. 37
Health IT for Medication Safety
Ordering Transcription Dispensing Administration
CPOE
Automatic
Medication
Dispensing
Electronic
Medication
Administration
Records
(e-MAR)
Barcoded
Medication
Administration
Barcoded
Medication
Dispensing

38. 38
Health Information Exchange (HIE)
Hospital A Hospital B
Clinic D
Policymakers
Patient
at Home
Hospital C
HIE Broker

39. 39
My Life-Long Dream...

40. 40
Search YouTube: TEDx Nawanan

41. 41
A Smart Machine: DeepMind
Image Sources: http://www.ibtimes.com/google-deepminds-alphago-
program-defeats-human-go-champion-first-time-ever-2283700
http://deepmind.com/

42. 42Image Source: socialmediab2b.com
Another Smart Machine: IBM’s Watson

43. 43Image Source: englishmoviez.com
Rise of the Machines?

44. 44
Clinical Decision Support Systems
• CDSS 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

45. 45
Being Smart #5:
Don’t Replace
Human Users.
Use ICT to Help Them
Perform Smarter & Better.

46. 46
Some Risks of Clinical Decision Support Systems
• Alert Fatigue
Unintended Consequences of Health IT

47. 47
Being Smart #6:
Health IT Also Have
Risks &
Unintended Consequences

48. 48
Q&A
SlideShare.net/Nawanan