Health IT & Smart Hospitals (October 19, 2016)

Health IT
& Smart Hospitals
Nawanan Theera-Ampornpunt
October 19, 2016

2
2003 M.D. (First-Class Honors)
2011 Ph.D. (Health Informatics), Univ. of Minnesota
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

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
Assembling
Manufacturing
Raw Materials Finished
Goods
Value-Add
- Innovation
- Design
- QC

13
Patient Care
Health care
Sick Patient Well Patient
Value-Add
- Technology & medications
- Clinical knowledge & skilled providers
- Quality of care; process improvement
- Customer service
- Information

14
• Large variations & contextual dependence
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?

23
So How is
a “Smart Hospital”
Different from a Digital or
Paperless Hospital?

24
Thailand 4.0
Image Source: คณะทำงำนเพื่อสร้ำงควำมตระหนักและกำรรับรู้เพื่อส่งเสริมรัฐวิสำหกิจเริ่มต้น กระทรวงพำณิชย์
https://www.youtube.com/watch?v=OEfY3rQZpNo

25
Thailand 1.0

26
Thailand 2.0

27
Thailand 3.0

28
Thailand 3.0

29
Thailand’s Middle Income Trap

30
Thailand 4.0

31
Thailand 4.0

32
Thailand 4.0

33
Thailand 4.0

34
Thailand 4.0

36
Healthcare 1.0
• Thai Traditional Medicine in the Ancient Times
Healthcare 1.0 - 4.0 is the speaker’s personal opinion and may not represent official views of the Ministry of Public Health
Image & Caption Source: http://kanchanapisek.or.th/kp6/sub/book/book.php?book=33&chap=8&page=t33-8-detail.html
http://kanchanapisek.or.th/kp6/sub/book/book.php?book=33&chap=8&page=t33-8-infodetail02.html
กำรย่อยขนำดตัวยำสมุนไพร โดยใช้ครกตำและหินบดยำ
ของโรงศิริรำชพยำบำล (ปัจจุบันคือ โรงพยำบำลศิริรำช)
คนไทยสมัยก่อนมักให้บุคคลในครอบครัวบีบนวดได้

37
Healthcare 2.0
• Modern (Western) Medicine

38
Healthcare 3.0
• Quality-Driven Healthcare

39
Healthcare 4.0
• Smart Healthcare & Smart Hospital

40
So What Is
Smart Healthcare?

41
Smart Healthcare For Policymakers?
Image Source: http://healthdata.moph.go.th/kpi/2557/ProvinceKpiTopicListAll.php?provincecode=99

42
Smart Healthcare For Health Promoters?
Image Source: http://www.hiso.or.th/hiso/picture/reportHealth/ThaiHealth2014/thai2014_3.pdf

43
Smart Healthcare For Clinicians?
Image Source: http://www.medscape.com/viewarticle/780298

44
Smart Healthcare For Patients & Consumers?
Image Source: Agence France-Presse/Getty Images

45
So What Exactly Is Smart Healthcare?
Image Source: http://cdn2.hubspot.net/hub/134568/file-1208368053-jpg/6-blind-men-hans.jpg

46
The Road to Digitizing Healthcare
 What is a “Smart Hospital”?
Outline

47
Microsoft Health Future Vision

48
Connecting People to a Healthy Future With
Personalized Care – Kaiser Permanente
https://www.youtube.com/watch?v=gxz9ZVvduGc

49
Back to
something simple...

50
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?

51
• 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

52
Information is Everywhere in Healthcare

53
“Information” in Medicine
Shortliffe EH. Biomedical informatics in the education of physicians. JAMA.
2010 Sep 15;304(11):1227-8.

54
54
WHO (2009)
Components of Health Systems

55
55
WHO (2009)
WHO Health System Framework

56
• 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

57
• Efficient
–Faster care
–Time & cost savings
–Reducing unnecessary tests
• Equitable
–Access to providers & knowledge
• Patient-Centered
–Empowerment & better self-care
Being “Smart” in Healthcare

58
(IOM, 2001)(IOM, 2000) (IOM, 2011)
Landmark Institute of Medicine Reports

59
• 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

60
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

61
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

62Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital
To Err is Human 2: Memory

63
• 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

64
• It already happens....
(Mamede et al., 2010; Croskerry, 2003; Klein,
2005; Croskerry, 2013)
What If This Happens in Healthcare?

65
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

66
• Medication Errors
–Drug Allergies
–Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• Failure to follow clinical practice guidelines
Common Errors

67
Being Smart #3:
“To Err is Human”

68
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making

69
Example of “Alerts & Reminders”
Reducing Errors through “Alerts & Reminders”

70
Why We Need ICT
in Healthcare?
#1: Because information is
everywhere in healthcare

71
Why We Need ICT
in Healthcare?
#2: Because healthcare is
error-prone and technology
can help

72http://www.dplindbenchmark.com/wp-content/uploads/2013/02/HHRI-Our-Health-Care-River.pdf
Fragmented Healthcare

73
Why We Need ICT
in Healthcare?
#3: Because access to
high-quality patient information
should improve care

74
Why We Need ICT
in Healthcare?
#4: Because healthcare at
all levels is fragmented &
in need of process
improvement

75
Documented Values of Health IT
• Guideline adherence
• Better documentation
• Practitioner decision making or
process of care
• Medication safety
• Patient surveillance &
monitoring
• Patient education/reminder

76
Being Smart #4:
Link IT Values to
Quality (Including Safety)

77
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

78
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

79
eHealth  Health IT
Slide adapted from: Dr. Boonchai Kijsanayotin
eHealth & Health IT

80
Health
Information
Technology
Goal
Value-Add
Tools
Health IT: What’s in a Word?

81
Hospital Information System (HIS) Computerized Physician Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
Picture Archiving and
Communication System
(PACS)
Various Forms of Health IT

82
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

83
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

84
Hospital A Hospital B
Clinic C
Government
Lab Patient at Home
Health Information Exchange

85
ควำมฝันอันสูงสุด...
My Life-Long Dream...

86WHO & ITU
Achieving Health Information Exchange (HIE)

87
• The Large N Interfaces Problem
N = 2, Interface = 1
# Interfaces = N(N-1)/2
N = 100, Interface = 4,950
Standards: Why?

88
นวนรรน ธีระอัมพรพันธุ์. ตำนำนควำมเชื่อและข้อเท็จจริงเกี่ยวกับมำตรฐำนสำรสนเทศทำงสุขภำพ. 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
Myths & Truths on Standards

89
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

90
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

91
The Road to Digitizing Healthcare
What is a “Smart Hospital”?
Outline

92
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/

93Image Source: socialmediab2b.com
Another Smart Machine: IBM’s Watson

94Image Source: englishmoviez.com
Rise of the Machines?

95Image Source: amazon.com
Smart Phones, Dumb People?

97
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

98
Being Smart #6:
Don’t Replace
Human Users.
Use ICT to Help Them
Perform Smarter & Better.

99
Some Risks of Clinical Decision Support Systems
• Alert Fatigue
Unintended Consequences of Health IT

100
Workarounds
Unintended Consequences of Health IT

101
Being Smart #7:
Health IT Also Have
Risks &
Unintended Consequences

102
Balanced Focus of Informatics
Technology
ProcessPeople

103
Being Smart #8:
Balance Your Focus
(People, Process, Technology)

104The sailboat image source: Uwe Kils via http://en.wikipedia.org/wiki/Sailing
The destination
The boat
The sailor(s) &
people on board
The tailwind The headwind
The direction
The speed
The past journey
The sea
The sail
The current location
IT & Organizational Context

105
Being Smart #9:
Know Your Context &
Align IT with that Context

106
A 900-Bed University Hospital
Vision To be a leading hospital in
Asia with excellence in
healthcare services, education,
and research
A 200-Bed Private Hospital
Vision To be a leading High Tech
High Touch hospital in Thailand
Vision, Mission & IT Strategies

107
Carr (2004) Carr (2003)
IT as “The Sail”

108
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

109
Being Smart #10:
Identify Your
Strategic IT Assets

110
People
Techno-
logy
Process
“The Sailors”

111
A 900-Bed University Hospital
• Average age of personnel at 42
years (range 20-65)
• IT department consists of new,
young members and senior
members who developed the first
version of its systems
• Doctors are highly individualistic,
most also practice in private
hospitals, high turn-over rate
• Nurses and other professions often
view doctors as hospital’s “elites”
and often have heated debates
with them
A 200-Bed Private Hospital
• Average age of personnel at 32
years (range 20-57)
• Strong IT department
• Doctors typically don’t interact
with other staff, income is the
main motivator
• Executives are highly respected
by all professions as visionary
and high-performing
“The Sailors”

112Ash et al. (2003)
The “Special People”

113Ash et al. (2003)
• Administrative
Leadership Level
–CEO
•Provides top level
support and vision
•Holds steadfast
•Connects with the
staff
•Listens
•Champions
– CIO
• Selects champions
• Gains support
• Possesses vision
• Maintains a thick skin
– CMIO
• Interprets
• Possesses vision
• Maintains a thick skin
• Influences peers
• Supports the clinical support
staff
• Champions

114Ash et al. (2003)
• Clinical Leadership
Level
– Champions
• Necessary
• Hold steadfast
• Influence peers
• Understand other
physicians
– Opinion leaders
• Provide a balanced
view
• Influence peers
– Curmudgeons
• “Skeptic who is
usually quite vocal in
his or her disdain of
the system”
• Provide feedback
• Furnish leadership
– Clinical advisory
committees
• Solve problems
• Connect units

115Ash et al. (2003)
• Bridger/Support level
–Trainers & support
team
•Necessary
•Provide help at the
elbow
•Make changes
•Provide training
•Test the systems
–Skills
•Possess clinical
backgrounds
•Gain skills on the
job
•Show patience,
tenacity, and
assertiveness

116
Being Smart #11:
Manage Your
“Special People” Well

117
A True Story of Failure to
Involve Users in Hospital IT
Implementation

118
Being Smart #12:
Involve Users Early &
Intensively in Your Process

119
Image source: Jeremy Kemp via http://en.wikipedia.org/wiki/Hype_cycle
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
Gartner Hype Cycle

120
Rogers (2003)
Rogers’ Diffusion of Innovations:
Adoption Curve

121
• 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
Theera-Ampornpunt (2009, 2011)
Success Factors of Hospital IT Adoption

122
Being Smart #13:
Work Smartly with
Smart People

123
To become a smart hospital, you must
• Know what is “smart” all about
• Know how to use smart machines
together with smart people
• Manage both of them smartly
Summary

124
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Contacts
nawanan.the@mahidol.ac.th
www.tc.umn.edu/~theer002
Nawanan Theera-Ampornpunt
Line ID: NawananT
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Health IT & Smart Hospitals (October 19, 2016)

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