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ICT in Healthcare
Nawanan Theera-Ampornpunt
March 8, 2019
2
2003 M.D. (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, Social Media
IT Management, Security & Privacy
nawanan.the@mahidol.ac.th
SlideShare.net/Nawanan
นวนรรน ธีระอัมพรพันธุ์ (Nawanan Theera-Ampornpunt)
Line ID: NawananT
Introduction
3
 The Road to Digitizing Healthcare
 What is a “Smart Hospital”?
 Toward a “Smart” Hospital
Outline
4
Health &
Health Information
5
Let’s take a look at
these pictures...
6Image Source: https://en.wikipedia.org/wiki/Industrial_robot (KUKA Roboter GmbH)
“Smart” Manufacturing
7
Image Sources: http://isarapost.net/home/?p=17760
http://www.telecomjournalthailand.com/ตอบโจทย์โมเดลทางธุรกิจ/
“Smart” Banking
8ER - Image Source: nj.com
Healthcare (On TV)
9
(At an undisclosed hospital)
Healthcare (Reality)
10
• 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”?
11
But...Are We That Different?
Input Process Output
Transfer
Banking
Value-Add
- Security
- Convenience
- Customer Service
Location A Location B
12
Input Process Output
Assembling
Manufacturing
Raw Materials Finished
Goods
Value-Add
- Innovation
- Design
- QC
But...Are We That Different?
13
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?
14
• Large variations & contextual dependence
Input Process Output
Patient
Presentation
Decision-
Making
Biological
Responses
Standardizing Healthcare
15
The World of Smart Machines
Image Sources: http://www.ibtimes.com/google-deepminds-alphago-
program-defeats-human-go-champion-first-time-ever-2283700
http://deepmind.com/
16
Digitizing Healthcare
Image Source: http://www.bloomberg.com/bw/stories/2005-03-27/cover-image-the-digital-hospital
17
“To computerize
the hospital”
“To go paperless”
“To become a
Digital Hospital”
“To Have
EHRs”
Why Adopting Health IT?
18
• “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
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?
“Smart Hospital”
23
So How is
a “Smart Hospital”
Different from a Digital or
Paperless Hospital?
24
Smart Healthcare For Policymakers?
Image Source: http://healthdata.moph.go.th/kpi/2557/ProvinceKpiTopicListAll.php?provincecode=99
25
Smart Healthcare For Health Promoters?
Image Source: http://www.hiso.or.th/hiso/picture/reportHealth/ThaiHealth2014/thai2014_3.pdf
26
Smart Healthcare For Clinicians?
Image Source: http://www.medscape.com/viewarticle/780298
27
Smart Healthcare For Patients & Consumers?
Image Source: Agence France-Presse/Getty Images
28
So What Exactly Is Smart Healthcare?
Image Source: http://cdn2.hubspot.net/hub/134568/file-1208368053-jpg/6-blind-men-hans.jpg
29
The Road to Digitizing Healthcare
 What is a “Smart Hospital”?
 Toward a “Smart” Hospital
Outline
30
Connecting People to a Healthy Future With
Personalized Care – Kaiser Permanente
https://www.youtube.com/watch?v=gxz9ZVvduGc
31
Back to
something simple...
32
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?
33
• 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
34
Information is Everywhere in Healthcare
35
“Information” in Medicine
Shortliffe EH. Biomedical informatics in the education of physicians. JAMA.
2010 Sep 15;304(11):1227-8.
36
36
WHO (2009)
Components of Health Systems
37
37
WHO (2009)
WHO Health System Framework
38
• Safe
–Drug allergies
–Medication Reconciliation
• Timely
–Complete information at point of
care
• Effective
–Better clinical decision-making
Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/
Being “Smart” in Healthcare
39
• Efficient
–Faster care
–Time & cost savings
–Reducing unnecessary tests
• Equitable
–Access to providers & knowledge
• Patient-Centered
–Empowerment & better self-care
Being “Smart” in Healthcare
40
(IOM, 2001)(IOM, 2000) (IOM, 2011)
Landmark Institute of Medicine Reports
41
• To Err is Human (IOM, 2000) reported
that:
– 44,000 to 98,000 people die in U.S.
hospitals each year as a result of
preventable medical mistakes
– Mistakes cost U.S. hospitals $17 billion to
$29 billion yearly
– Individual errors are not the main problem
– Faulty systems, processes, and other
conditions lead to preventable errors
Patient Safety
42
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
43
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
44Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital
To Err is Human 2: Memory
45
• Cognitive Errors - Example: Decoy Pricing
The Economist Purchase Options
• Economist.com subscription $59
• Print subscription $125
• Print & web subscription $125
Ariely (2008)
16
0
84
The Economist Purchase Options
• Economist.com subscription $59
• Print & web subscription $125
68
32
# of
People
# of
People
To Err is Human 3: Cognition
46
• It already happens....
(Mamede et al., 2010; Croskerry, 2003; Klein,
2005; Croskerry, 2013)
What If This Happens in Healthcare?
47
Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;330(7494):781-3.
“Everyone makes mistakes. But our reliance on
cognitive processes prone to bias makes
treatment errors more likely than we think”
Cognitive Biases in Healthcare
48
• Medication Errors
–Drug Allergies
–Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• Failure to follow clinical practice guidelines
Common Errors
49
Being Smart #3:
“To Err is Human”
50
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
51
Example of “Alerts & Reminders”
Reducing Errors through “Alerts & Reminders”
52
Documented Values of Health IT
• Guideline adherence
• Better documentation
• Practitioner decision making or
process of care
• Medication safety
• Patient surveillance &
monitoring
• Patient education/reminder
53
Being Smart #4:
Link IT Values to
Quality (Including Safety)
54
Use of information and communications
technology (ICT) in health & healthcare
settings
Source: The Health Resources and Services Administration, Department of Health
and Human Service, USA
Slide adapted from: Dr. Boonchai Kijsanayotin
Health IT
55
Use of information and communications
technology (ICT) for health; Including
• Treating patients
• Conducting research
• Educating the health workforce
• Tracking diseases
• Monitoring public health.
Sources: 1) WHO Global Observatory of eHealth (GOe) (www.who.int/goe)
2) World Health Assembly, 2005. Resolution WHA58.28
Slide adapted from: Mark Landry, WHO WPRO & Dr. Boonchai Kijsanayotin
eHealth
56
eHealth  Health IT
Slide adapted from: Dr. Boonchai Kijsanayotin
eHealth & Health IT
57
Health
Information
Technology
Goal
Value-Add
Tools
Health IT: What’s in a Word?
58
Hospital Information System (HIS) Computerized Physician Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
Picture Archiving and
Communication System
(PACS)
Various Forms of Health IT
59
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
60
Strategic
Operational
ClinicalAdministrative
LIS
Health Information ExchangeBusiness
Intelligence
Word
Processor
Social
Media
PACS
4 Quadrants of Hospital IT
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
61
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
62
Hospital A Hospital B
Clinic C
Government
Lab Patient at Home
Health Information Exchange
63
My Life-Long Dream...
64
65WHO & ITU
Achieving Health Information Exchange (HIE)
66
Myths
• We don’t need standards
• Standards are IT people’s jobs
• We should exclude vendors from this
• We need the same software to share data
• We need to always adopt international
standards
• We need to always use local standards
Theera-Ampornpunt (2011)
Myths & Truths on Standards
67
Being Smart #5:
Go for Systems that Use
Standards, Not a Unified,
Conquer-the-World System
Image Source: http://www.denofgeek.com/movies/avengers/37236/why-loki-was-cut-from-avengers-age-of-ultron
68
The Road to Digitizing Healthcare
What is a “Smart Hospital”?
 Toward a “Smart” Hospital
Outline
69
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/
70Image Source: socialmediab2b.com
Another Smart Machine: IBM’s Watson
71Image Source: englishmoviez.com
Rise of the Machines?
72
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
73
Being Smart #6:
Don’t Replace
Human Users.
Use ICT to Help Them
Perform Smarter & Better.
74
Some Risks of Clinical Decision Support Systems
• Alert Fatigue
Unintended Consequences of Health IT
75
Workarounds
Unintended Consequences of Health IT
76
Being Smart #7:
Health IT Also Have
Risks &
Unintended Consequences
77
Balanced Focus of Informatics
Technology
ProcessPeople
78
Being Smart #8:
Balance Your Focus
(People, Process, Technology)
79
2003 M.D. (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, Social Media
IT Management, Security & Privacy
nawanan.the@mahidol.ac.th
SlideShare.net/Nawanan
นวนรรน ธีระอัมพรพันธุ์ (Nawanan Theera-Ampornpunt)
Line ID: NawananT
Q & A

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ICT in Healthcare (March 8, 2019)

  • 1. ICT in Healthcare Nawanan Theera-Ampornpunt March 8, 2019
  • 2. 2 2003 M.D. (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, Social Media IT Management, Security & Privacy nawanan.the@mahidol.ac.th SlideShare.net/Nawanan นวนรรน ธีระอัมพรพันธุ์ (Nawanan Theera-Ampornpunt) Line ID: NawananT Introduction
  • 3. 3  The Road to Digitizing Healthcare  What is a “Smart Hospital”?  Toward a “Smart” Hospital Outline
  • 5. 5 Let’s take a look at these pictures...
  • 6. 6Image Source: https://en.wikipedia.org/wiki/Industrial_robot (KUKA Roboter GmbH) “Smart” Manufacturing
  • 8. 8ER - Image Source: nj.com Healthcare (On TV)
  • 9. 9 (At an undisclosed hospital) Healthcare (Reality)
  • 10. 10 • 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”?
  • 11. 11 But...Are We That Different? Input Process Output Transfer Banking Value-Add - Security - Convenience - Customer Service Location A Location B
  • 12. 12 Input Process Output Assembling Manufacturing Raw Materials Finished Goods Value-Add - Innovation - Design - QC But...Are We That Different?
  • 13. 13 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?
  • 14. 14 • Large variations & contextual dependence Input Process Output Patient Presentation Decision- Making Biological Responses Standardizing Healthcare
  • 15. 15 The World of Smart Machines Image Sources: http://www.ibtimes.com/google-deepminds-alphago- program-defeats-human-go-champion-first-time-ever-2283700 http://deepmind.com/
  • 16. 16 Digitizing Healthcare Image Source: http://www.bloomberg.com/bw/stories/2005-03-27/cover-image-the-digital-hospital
  • 17. 17 “To computerize the hospital” “To go paperless” “To become a Digital Hospital” “To Have EHRs” Why Adopting Health IT?
  • 18. 18 • “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
  • 19. 19 Being Smart #1: Stop Your “Drooling Reflex”!!
  • 20. 20 Being Smart #2: Focus on Information & Process Improvement, Not Technology
  • 21. 21 If not “Digital Hospital” or “Paperless Hospital” Then What Should We Aspire to Be?
  • 23. 23 So How is a “Smart Hospital” Different from a Digital or Paperless Hospital?
  • 24. 24 Smart Healthcare For Policymakers? Image Source: http://healthdata.moph.go.th/kpi/2557/ProvinceKpiTopicListAll.php?provincecode=99
  • 25. 25 Smart Healthcare For Health Promoters? Image Source: http://www.hiso.or.th/hiso/picture/reportHealth/ThaiHealth2014/thai2014_3.pdf
  • 26. 26 Smart Healthcare For Clinicians? Image Source: http://www.medscape.com/viewarticle/780298
  • 27. 27 Smart Healthcare For Patients & Consumers? Image Source: Agence France-Presse/Getty Images
  • 28. 28 So What Exactly Is Smart Healthcare? Image Source: http://cdn2.hubspot.net/hub/134568/file-1208368053-jpg/6-blind-men-hans.jpg
  • 29. 29 The Road to Digitizing Healthcare  What is a “Smart Hospital”?  Toward a “Smart” Hospital Outline
  • 30. 30 Connecting People to a Healthy Future With Personalized Care – Kaiser Permanente https://www.youtube.com/watch?v=gxz9ZVvduGc
  • 32. 32 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?
  • 33. 33 • 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
  • 35. 35 “Information” in Medicine Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
  • 37. 37 37 WHO (2009) WHO Health System Framework
  • 38. 38 • Safe –Drug allergies –Medication Reconciliation • Timely –Complete information at point of care • Effective –Better clinical decision-making Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/ Being “Smart” in Healthcare
  • 39. 39 • Efficient –Faster care –Time & cost savings –Reducing unnecessary tests • Equitable –Access to providers & knowledge • Patient-Centered –Empowerment & better self-care Being “Smart” in Healthcare
  • 40. 40 (IOM, 2001)(IOM, 2000) (IOM, 2011) Landmark Institute of Medicine Reports
  • 41. 41 • To Err is Human (IOM, 2000) reported that: – 44,000 to 98,000 people die in U.S. hospitals each year as a result of preventable medical mistakes – Mistakes cost U.S. hospitals $17 billion to $29 billion yearly – Individual errors are not the main problem – Faulty systems, processes, and other conditions lead to preventable errors Patient Safety
  • 42. 42 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
  • 43. 43 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
  • 44. 44Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital To Err is Human 2: Memory
  • 45. 45 • Cognitive Errors - Example: Decoy Pricing The Economist Purchase Options • Economist.com subscription $59 • Print subscription $125 • Print & web subscription $125 Ariely (2008) 16 0 84 The Economist Purchase Options • Economist.com subscription $59 • Print & web subscription $125 68 32 # of People # of People To Err is Human 3: Cognition
  • 46. 46 • It already happens.... (Mamede et al., 2010; Croskerry, 2003; Klein, 2005; Croskerry, 2013) What If This Happens in Healthcare?
  • 47. 47 Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;330(7494):781-3. “Everyone makes mistakes. But our reliance on cognitive processes prone to bias makes treatment errors more likely than we think” Cognitive Biases in Healthcare
  • 48. 48 • Medication Errors –Drug Allergies –Drug Interactions • Ineffective or inappropriate treatment • Redundant orders • Failure to follow clinical practice guidelines Common Errors
  • 49. 49 Being Smart #3: “To Err is Human”
  • 50. 50 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  • 51. 51 Example of “Alerts & Reminders” Reducing Errors through “Alerts & Reminders”
  • 52. 52 Documented Values of Health IT • Guideline adherence • Better documentation • Practitioner decision making or process of care • Medication safety • Patient surveillance & monitoring • Patient education/reminder
  • 53. 53 Being Smart #4: Link IT Values to Quality (Including Safety)
  • 54. 54 Use of information and communications technology (ICT) in health & healthcare settings Source: The Health Resources and Services Administration, Department of Health and Human Service, USA Slide adapted from: Dr. Boonchai Kijsanayotin Health IT
  • 55. 55 Use of information and communications technology (ICT) for health; Including • Treating patients • Conducting research • Educating the health workforce • Tracking diseases • Monitoring public health. Sources: 1) WHO Global Observatory of eHealth (GOe) (www.who.int/goe) 2) World Health Assembly, 2005. Resolution WHA58.28 Slide adapted from: Mark Landry, WHO WPRO & Dr. Boonchai Kijsanayotin eHealth
  • 56. 56 eHealth  Health IT Slide adapted from: Dr. Boonchai Kijsanayotin eHealth & Health IT
  • 58. 58 Hospital Information System (HIS) Computerized Physician Order Entry (CPOE) Electronic Health Records (EHRs) Picture Archiving and Communication System (PACS) Various Forms of Health IT
  • 59. 59 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
  • 60. 60 Strategic Operational ClinicalAdministrative LIS Health Information ExchangeBusiness Intelligence Word Processor Social Media PACS 4 Quadrants of Hospital IT 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
  • 61. 61 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
  • 62. 62 Hospital A Hospital B Clinic C Government Lab Patient at Home Health Information Exchange
  • 64. 64
  • 65. 65WHO & ITU Achieving Health Information Exchange (HIE)
  • 66. 66 Myths • We don’t need standards • Standards are IT people’s jobs • We should exclude vendors from this • We need the same software to share data • We need to always adopt international standards • We need to always use local standards Theera-Ampornpunt (2011) Myths & Truths on Standards
  • 67. 67 Being Smart #5: Go for Systems that Use Standards, Not a Unified, Conquer-the-World System Image Source: http://www.denofgeek.com/movies/avengers/37236/why-loki-was-cut-from-avengers-age-of-ultron
  • 68. 68 The Road to Digitizing Healthcare What is a “Smart Hospital”?  Toward a “Smart” Hospital Outline
  • 69. 69 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/
  • 70. 70Image Source: socialmediab2b.com Another Smart Machine: IBM’s Watson
  • 72. 72 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
  • 73. 73 Being Smart #6: Don’t Replace Human Users. Use ICT to Help Them Perform Smarter & Better.
  • 74. 74 Some Risks of Clinical Decision Support Systems • Alert Fatigue Unintended Consequences of Health IT
  • 76. 76 Being Smart #7: Health IT Also Have Risks & Unintended Consequences
  • 77. 77 Balanced Focus of Informatics Technology ProcessPeople
  • 78. 78 Being Smart #8: Balance Your Focus (People, Process, Technology)
  • 79. 79 2003 M.D. (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, Social Media IT Management, Security & Privacy nawanan.the@mahidol.ac.th SlideShare.net/Nawanan นวนรรน ธีระอัมพรพันธุ์ (Nawanan Theera-Ampornpunt) Line ID: NawananT Q & A