1. IT for Hospitals
Nawanan Theera‐Ampornpunt, MD, PhD
Oct. 22, 2013
For Ramathibodi Hospital Administration School
SlideShare.net/Nawanan

2. A Few Words About Me...
2003 M.D. (1st-Class Honors) Ramathibodi
2009 M.S. (Health Informatics) University of Minnesota
2011 Ph.D. (Health Informatics) University of Minnesota
Currently
• Deputy Executive Director for Informatics (CIO), Chakri Naruebodindra
Medical Institute, Faculty of Medicine Ramathibodi Hospital
Contacts
nawanan.the@mahidol.ac.th
SlideShare.net/Nawanan
www.tc.umn.edu/~theer002
groups.google.com/group/ThaiHealthIT
2

3. Outline
Healthcare & Health IT
Adopting Health IT
Health IT Applications in Hospitals
IT Management
3

4. Health care &
Health IT
4

5. Manufacturing
5
Image Source: Guardian.co.uk

6. Banking
6
Image Source: Cablephet.com

7. Health care
7
ER ‐ Image Source: nj.com

8. Why Health care Isn’t Like Any Others?
 Life‐or‐Death
 Many & varied stakeholders
 Strong professional values
 Evolving standards of care
 Fragmented, poorly‐coordinated systems
 Large, ever‐growing & changing body of
knowledge
 High volume, low resources, little time
8

9. Why Health care Isn’t Like Any Others?
 Large variations & contextual dependence
Input
Output
Patient
Presentation
9
Process
Decision‐
Making
Biological
Responses

10. But...Are We That Different?
Banking
Input
Process
Output
Transfer
Location A
Location B
Value‐Add
‐ Security
‐ Convenience
‐ Customer Service
10

11. But...Are We That Different?
Manufacturing
Input
Process
Output
Raw
Materials
Assembling
Finished
Goods
Value‐Add
‐ Innovation
‐ Design
‐ QC
11

12. But...Are We That Different?
Health care
Input
Process
Output
Sick Patient
Patient Care
Well Patient
Value‐Add
‐ Technology & medications
‐ Clinical knowledge & skills
‐ Quality of care; process improvement
‐ Information
12

13. Why Adopting Health IT?
“To Go paperless”
“To Computerize”
“To Get a HIS”
“Digital Hospital”
“To Have EMRs”
“To Share data”
13
“To Modernize”

14. Some Quotes
 “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)
14

15. What Clinicians Want?
To treat & to
care for their
patients to their
best abilities,
given limited
time &
resources
15 Image Source: http://en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)

16. High Quality Care
Safe
Timely
Effective
Efficient
Equitable
Patient‐Centered
16
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.
IOM (2001)

17. Information is Everywhere in Health Care
17

18. Achieving Quality Care with Information
 Safe
 Drug allergies
 Medication Reconciliation
Timely
 Complete information at point of care
Effective
 Better clinical decision‐making
18 Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/

19. Achieving Quality Care with Information
 Efficient
 Faster care
 Time & cost savings
 Reducing unnecessary tests
Equitable
 Access to providers & knowledge
Patient‐Centered
19
 Empowerment & better self‐care

20. That’s Where Health IT
Plays A Role...
20

21. The Anatomy of the Word “Health IT”
Health
Information
Technology
21
Goal
Value‐Add
Tools

22. Various Forms of Health IT
Hospital Information System (HIS)
Computerized Provider Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
22
Picture Archiving and
Communication System
(PACS)

23. Still Many Other Forms of Health IT
Health Information
Exchange (HIE)
m‐Health
Biosurveillance
Personal Health Records
(PHRs)
Information Retrieval
23
Telemedicine &
Telehealth
Images from Apple Inc., Geekzone.co.nz, Google, PubMed.gov, and American Telecare, Inc.

24. Value of Health IT (in Literature)
Guideline adherence
 Better documentation
 Practitioner decision making or
process of care
 Medication safety
 Patient surveillance &
monitoring
 Patient education/reminder
24

25. Fundamental Theorem of Informatics
25
(Friedman, 2009)
Friedman (2009)

26. Is There A Role for Health IT?
26
IOM (2000)

27. Landmark IOM Reports
IOM (2000)
27
IOM (2001)

28. Landmark IOM Reports: Summary
 Humans are not perfect and are bound to make
errors
 Highlight problems in the U.S. health care system
that systematically contributes to medical errors and
poor quality
 Recommends reform that would change how health
care works and how technology innovations can
help improve quality/safety
28

29. Summary: Why We Need Health IT
 Health care is very complex (and inefficient)
 Health care is information‐rich
 Quality of care depends on timely availability &
quality of information
 Clinical knowledge body is too large
 Short time during a visit
 Practice guidelines are put “on‐the‐shelf”
 “To err is human”
29

30. To Err Is Human
 Perception errors
30
Image Source: interaction‐dynamics.com

31. To Err Is Human
 Lack of Attention
31
Image Source: aafp.org

32. To Err Is Human
 Human Brain’s Limited Memory
32
Image Source: Dr. Suthan Srisangkaew

33. To Err Is Human
 Cognitive Errors - Example: Decoy Pricing
# of
People
The Economist Purchase Options
• Economist.com subscription
• Print subscription
• Print & web subscription
$59
$125
$125
# of
People
The Economist Purchase Options
• Economist.com subscription
• Print & web subscription
33
16
0
84
$59
$125
68
32
Ariely (2008)

34. What If This Happens in Healthcare?
It already happens....
(Mamede et al., 2010; Croskerry, 2003; Klein, 2005)
What if health IT can help?
34

35. Adopting
Health IT
35

36. Adoption of Health IT: Assumptions
Adoption
36
Use
Outcomes

37. U.S.’s Efforts on Health IT Adoption
?
“...We will make wider use of electronic records and
other health information technology, to help control
costs and reduce dangerous
medical errors.”
President George W. Bush
Sixth State of the Union Address, January 31, 2006
37
Source: Wikisource.org
Image Source: Wikipedia.org

38. Public Policy in Informatics: A US’s Case
1991: IOM’s CPR Report published
1996: HIPAA enacted
2000‐2001: IOM’s To Err Is Human &
Crossing the Quality Chasm published
2004: George W. Bush’s Executive Order
establishing ONCHIT (ONC)
2009‐2010: ARRA/HITECH Act &
“Meaningful use” regulations
38

39. U.S. Adoption of Health IT
Ambulatory (Hsiao et al, 2009)
Hospitals (Jha et al, 2010)
Basic EHRs w/ notes
Comprehensive EHRs
CPOE for medications
9.2%
2.7%
34%
• U.S. lags behind other Western countries
(Schoen et al, 2006;Jha et al, 2008)
• Money and misalignment of benefits is the biggest
reason
39

40. We Need “Change”
“...we need to upgrade our medical
records by switching from a paper to
an electronic system of record
keeping...”
President Barack Obama
June 15, 2009
40

41. The Birth of “Meaningful Use”
“...Our recovery plan will invest in
electronic health records and new technology
that will reduce errors, bring down costs,
ensure privacy, and save lives.”
President Barack Obama
Address to Joint Session of Congress
February 24, 2009
41
Source: WhiteHouse.gov

42. American Recovery & Reinvestment Act
 Contains HITECH Act
(Health Information Technology for Economic and
Clinical Health Act)
 ~ 20 billion dollars for Health IT investments
 Incentives & penalties for providers
42

43. What is in the HITECH Act?
43
(Blumenthal, 2010)

44. “Meaningful Use”
Pumpkin
44
“Meaningful Use”
of a Pumpkin
Image Source & Idea Courtesy of Pat Wise at HIMSS, Oct. 2009

45. “Meaningful Use” of Health IT
Stage 1
‐ Electronic capture of
health information
‐ Information sharing
‐ Data reporting
Stage 3
Stage 2
Use of EHRs
to improve
processes of
care
45
Better
Health
Use of
EHRs to
improve
outcomes
Blumenthal (2010)

46. Adoption Studies: Descriptive Aspect
2004
2011
Theera‐
Ampornpunt (2011)
46
Pongpirul et al.
(2004)

47. Adoption Estimates
Estimate (Partial or Complete Adoption)
Basic EHR, outpatient
Basic EHR, inpatient
Basic EHR, both settings
Comprehensive EHR, outpatient
Comprehensive EHR, inpatient
Comprehensive EHR, both settings
order entry of medications, outpatient
order entry of medications, inpatient
order entry of medications, both settings
order entry of all orders, outpatient
order entry of all orders, inpatient
order entry of all orders, both settings
47
Nationwide
86.6%
50.4%
49.8%
10.6%
5.7%
5.3%
96.5%
91.4%
90.2%
88.6%
81.7%
79.4%
Theera‐Ampornpunt (2011)

48. Health IT
Applications
in Hospitals
48

49. Enterprise‐wide Hospital IT
 Master Patient Index (MPI)
 Admit‐Discharge‐Transfer (ADT)
 Electronic Health Records (EHRs)
 Computerized Physician Order Entry (CPOE)
 Clinical Decision Support Systems (CDSSs)
 Picture Archiving and Communication System (PACS)
 Nursing applications
 Enterprise Resource Planning (ERP)
49

50. Departmental IT
 Pharmacy applications
 Laboratory Information System (LIS)
 Radiology Information System (RIS)
 Specialized applications (ER, OR, LR,
Anesthesia, Critical Care, Dietary Services,
Blood Bank)
 Incident management & reporting system
50

51. EHRs & HIS
The Challenge ‐ Knowing What It Means
Electronic Health
Records (EHRs)
Hospital
Information System
(HIS)
Electronic Medical
Records (EMRs)
Electronic Patient
Records (EPRs)
Computer‐Based
Patient Records
(CPRs)
51
Personal Health
Records (PHRs)
Clinical Information
System (CIS)

52. EHR Systems
Just electronic documentation?
History
& PE
Diag‐
nosis
Treat‐
ments
Or do they have other values?
52
...

53. Functions that Should Be Part of EHR Systems
 Computerized Medication Order Entry
 Computerized Laboratory Order Entry
 Computerized Laboratory Results
 Physician Notes
 Patient Demographics
 Problem Lists
 Medication Lists
 Discharge Summaries
 Diagnostic Test Results
 Radiologic Reports
53
IOM (2003), Blumenthal et al (2006)

54. Computerized Physician Order Entry (CPOE)
54

55. Computerized Physician Order Entry (CPOE)
Values
No handwriting!!!
 Structured data entry: Completeness, clarity,
fewer mistakes (?)
 No transcription errors!
 Streamlines workflow, increases efficiency
55

56. Clinical Decision Support Systems (CDSs)
 The real place where most of the
values of health IT can be achieved
(Shortliffe, 1976)
56
 Expert systems
 Based on artificial intelligence,
machine learning, rules, or
statistics
 Examples: differential diagnoses,
treatment options

57. Clinical Decision Support Systems (CDSSs)
 Alerts & reminders
 Based on specified logical conditions
 Examples:
 Drug‐allergy checks
 Drug‐drug interaction checks
 Drug‐disease checks
 Drug‐lab checks
 Drug‐formulary checks
 Reminders for preventive services or certain actions
(e.g. smoking cessation)
 Clinical practice guideline integration
57

58. Example of “Alerts & Reminders”
58

59. CDS Examples
Reference information or evidence‐
based knowledge sources
Drug reference databases
 Textbooks & journals
 Online literature (e.g. PubMed)
Tools that help users easily access
references (e.g. Infobuttons)
59

60. Infobuttons
60
Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html

61. CDS Examples
Pre‐defined documents
 Order sets, personalized “favorites”
 Templates for clinical notes
 Checklists
 Forms
Can be either computer‐based or
paper‐based
61

62. Order Sets
62
Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm

63. CDS Examples
Simple UI designed to help clinical
decision making
Abnormal lab highlights
 Graphs/visualizations for lab results
Filters & sorting functions
63

64. Abnormal Lab Highlights
64
Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html

65. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
External Memory
Knowledge
Data
Inference
DECISION
65
Elson, Faughnan & Connelly (1997)

66. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
External Memory
Knowledge
Inference
DECISION
66
Abnormal lab
highlights
Data

67. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
External Memory
Knowledge
Inference
DECISION
67
Drug‐Allergy
Checks
Data

68. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
External Memory
Knowledge
Inference
DECISION
68
Drug‐Drug
Interaction
Checks
Data

69. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
External Memory
Knowledge
Inference
DECISION
69
Clinical Practice
Guideline
Reminders
Data

70. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
Knowledge
Inference
DECISION
70
External Memory
Data
Diagnostic/Treatment
Expert Systems

71. IBM’s Watson
71
Image Source: socialmediab2b.com

72. Rise of the Machines?
72
Image Source: englishmoviez.com

73. Clinical Decision Support Systems (CDSSs)
 CDSS as a replacement or supplement of
clinicians?
 The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
The “Greek Oracle” Model
Wrong Assumption
The “Fundamental Theorem” Model
Correct Assumption
73
Friedman (2009)

74. Clinical Decision Support Systems (CDSSs)
Some risks
 Alert fatigue
74

75. Workarounds
75

76. Health IT for Medication Safety
Ordering
CPOE
Transcription
Dispensing
Administration
Automatic
Medication
Dispensing
Electronic
Medication
Administration
Records
(e‐MAR)
Barcoded
Medication
Dispensing
76
Barcoded
Medication
Administration

77. Health Information Exchange (HIE)
Government
Hospital B
Hospital A
Lab
77
Patient at Home
Clinic C

78. Achieving HIE (or eHealth)
78
WHO & ITU

79. Myths & Truths on Standards
นวนรรน ธีระอัมพรพันธุ์. ตํานานความเชื่อและข้อเท็จจริงเกี่ยวกับมาตรฐานสารสนเทศทางสุขภาพ. ใน: Health
Data Standards Expo: From Reimbursement to Clinical Excellence; 2011 Aug 8-9; Bangkok,
Thailand. Bangkok (Thailand): Mahidol University, Faculty of Medicine Ramathibodi Hospital;
2011 Aug.
http://www.slideshare.net/nawanan/myths-and-truths-on-health-information-standards
79

80. Myths & Truths on Standards
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
80
Theera-Ampornpunt (2011)

81. Standards: Why?
 The Large N Problem
N = 2, Interface = 1
N = 3, Interface = 3
N = 5, Interface = 10
# Interfaces = N(N-1)/2
81
N = 100, Interface = 4,950

82. IT Management
82

83. Balanced Focus of Informatics
People
Process
83
Techno‐
logy

84. ความเดิมตอนที่แล้ว...
Health IT: ของดี
(อาจจะ)
(แต่ก็อาจมีโทษ)
มีประโยชน์
บริบท (local contexts) มีความสําคัญ
 ต้องมีการบริหารจัดการที่เหมาะสม
ประเด็นพิจารณา
อะไรคือบริบทที่เกี่ยวข้อง?
จะจัดการมันอย่างไร?
84

85. Context
The current location
The tailwind
The headwind
The past journey
The
direction
The destination
The speed
The sailor(s) &
people on board
85
The sail
The boat
The sea
The sailboat image source: Uwe Kils via http://en.wikipedia.org/wiki/Sailing

86. Direction & Destination
Visionรพ.มหาวิทยาลัย 900 าเตียง
เป็นโรงพยาบาลชั้นนํ ของ
ภูมิภาคเอเชียทีมีความเป็นเลิศใน
่
ด้านบริการ การศึกษา และวิจัย
86
Vision รพ.เอกชน 200High ง
เป็นโรงพยาบาล เตีย Tech
High Touch ชั้นนําของประเทศ

87. “The Sail”
Carr (2004)
87
Carr (2003)

88. 4 Quadrants of Hospital IT
Strategic
Business
Intelligence
HIE
PHRs
Administrative
CRM
Social
Media
CDSS
CPOE
Clinical
VMI
EHRs
PACS
LIS
ERP
ADT
Word
Processor
88
MPI
Operational

89. IT As A Strategic Advantage
Sustainable
competitive
Yes
advantage
Yes
Yes
Yes
Non-Substitutable?
Valuable ?
No
Resources/
capabilities
89
Rare ?
No
Inimitable ?
No
Preemptive
advantage
No
Competitive
necessity
Competitive
parity
Competitive
Disadvantage
From a teaching slide by Nelson F. Granados, 2006 at University of Minnesota Carlson School of Management

90. “The Sail”
Vision เป็นโรงพยาบาลชั้นนําของ
ภูมิภาคเอเชียทีมยาลัย 900 ศใน ง
รพ.มหาวิท่ ีความเป็นเลิ เตีย
ด้านบริการ การศึกษา และวิจัย
Current IT Environment
 เป็น รพ.แรกๆ ที่มี HIS ซึ่งพัฒนาเอง และ
ต่อยอดจาก MPI, ADT ไปสู่ CPOE (แต่
ยังขาด advanced CDSS) ระบบ HIS
เข้ากับ workflow ของ รพ. เป็นอย่างดี
 ปัจจุบัน ระบบ HIS ยังใช้เทคโนโลยี
เดียวกับช่วงที่พัฒนาใหม่ๆ (20 ปีก่อน)
เป็นหลัก มีการนําเทคโนโลยีใหม่ๆ มาใช้
อย่างช้าๆ
90
Vision เป็นโรงพยาบาล High Tech
Highรพ.เอกชน นําของประเทศ
Touch ชั้น 200 เตียง
Current IT Environment
 มี MPI, ADT, EHRs, CPOE แต่ยังมี
CDSS จํากัด
 ยังไม่มี Customer Relationship
Management (CRM)
 ยังไม่มี Personal Health Records
(PHRs)

91. IT As A Strategic Advantage
Sustainable
competitive
Yes
advantage
Yes
Yes
Yes
Non-Substitutable?
Valuable ?
No
Resources/
capabilities
91
Rare ?
No
Inimitable ?
No
Preemptive
advantage
No
Competitive
necessity
Competitive
parity
Competitive
Disadvantage
From a teaching slide by Nelson F. Granados, 2006 at University of Minnesota Carlson School of Management

92. “The Sailors”
People
Process
92
Techno‐
logy

93. “The Sailors”
 บุคลากรมีอายุเฉลีย 42 ปี
่
(range 20-65) ยาลัย 900 เตียง
รพ.มหาวิท
 แผนก IT มีทั้งบุคลากรใหม่และทีเคย
่
พัฒนาระบบ HIS ตั้งแต่แรกเริ่ม
 แพทย์มีความเป็นตัวของตัวเองสูง,
มักทํางานเอกชนด้วย, มี turn-over
rate สูง
 พยาบาลและวิชาชีพอื่นมักมองว่า
แพทย์คืออภิสทธิ์ชน และมีเรื่อง
ิ
ถกเถียงกันบ่อยๆ
93
 บุคลากรมีอายุเฉลีย 32 ปี
่
(range 20-57) 200 เตียง
รพ.เอกชน
 แผนก IT เข้มแข็ง
 แพทย์ไม่ค่อยมี interaction กับ
บุคลากรอื่น, รายได้เป็นแรงดึงดูดหลัก
 ผู้บริหารได้รับการยอมรับจากบุคลากร
ทุกวิชาชีพว่ามีวิสัยทัศน์และบริหารงาน
ได้ดี

94. IT Outsourcing Decision Tree
No
No
Is external delivery
reliable and lower cost?
Yes
Does service offer
competitive advantage?
Yes
94
Keep Internal
Keep Internal
OUTSOURCE!

95. IT Outsourcing Decision Tree: Ramathibodi’s Case
External delivery unreliable
• Non‐Core HIS
External delivery higher cost
• ERP maintenance/ongoing
customization
No
No
Is external delivery
reliable and lower cost?
Yes
Does service offer
competitive advantage?
Yes
95
Keep Internal
Keep Internal
Core HIS, CPOE
Strategic advantages
• Agility due to local workflow accommodations
• Secondary data utilization (research, QI)
• Roadmap to national leader in informatics
OUTSOURCE!
ERP initial
implementation,
PACS, RIS,
Departmental
systems,
IT Training

96. Gartner Hype Cycle
96
Image source: Jeremy Kemp via http://en.wikipedia.org/wiki/Hype_cycle
http://www.gartner.com/technology/research/methodologies/hype‐cycle.jsp

97. Rogers’ Diffusion of Innovations: Adoption Curve
Rogers (2003)
97

98. Hospital IT Adoption Success Factors
 Communications of project plans & progresses
 Workflow considerations
 Management support of IT projects
 Common visions
 Shared commitment
 Multidisciplinary user involvement
 Project management
 Training
 Innovativeness
 Organizational learning
98
Theera‐Ampornpunt (2009, 2011) [Unpublished]

99. Resources on Change Management
Lorenzi & Riley (2004)
99
Leviss (Editor) (2010)

100. Summary
 Healthcare is complex
 Health IT can benefit healthcare through
 Information delivery
 Process improvement
 Empowering providers & patients
 The world is moving toward health IT
 Management of hospital IT is crucial to success
 Balance of “People, Process & Technology”
 Know your organization (“context”)
 Strategic mindset
 Project & change management
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101. Patients Are Counting on Us...
101 Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/

102. Q & A...
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102

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