ICT Applications for Healthcare

ICT Applications for Healthcare
MUICT Seminar
Nawanan Theera-Ampornpunt, M.D., Ph.D.
Faculty of Medicine Ramathibodi Hospital
May 28, 2014
SlideShare.net/Nawanan

2
A Bit About Myself...
2003 M.D. (First-Class Honors) (Ramathibodi)
2009 M.S. in Health Informatics (U of MN)
2011 Ph.D. in Health Informatics (U of MN)
2012 Certified HL7 CDA Specialist
• Deputy Executive Director for Informatics (CIO/CMIO)
Chakri Naruebodindra Medical Institute
• Lecturer, Department of Community Medicine
Faculty of Medicine Ramathibodi Hospital
Mahidol University
nawanan.the@mahidol.ac.th
http://groups.google.com/group/ThaiHealthIT

3
Outline
• Healthcare & Information
• Why We Need ICT in Healthcare
• Health IT & eHealth
• Some ICT Applications
• A Dream for Healthcare
• Food for Thought for ICT Folks
• Q&A

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

5Image Source: Guardian.co.uk
Manufacturing

6Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3
Banking

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

8
Healthcare
(At an undisclosed nearby hospital)

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 Like Any Others

10
Back to
something simple...

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What Clinicians Want?
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)

12
High Quality Care
• 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.

13
Information is Everywhere in Healthcare
Shortliffe EH. Biomedical informatics in the education of
physicians. JAMA. 2010 Sep 15;304(11):1227-8.

14
“Information” in Medicine
Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.

15
Outline
“Information” in Healthcare
• Why We Need ICT in Healthcare
• Q&A

16
Why We Need ICT
in Healthcare?
#1: Because information is
everywhere in healthcare

17
(IOM, 2001)(IOM, 2000) (IOM, 2011)
Landmark IOM Reports

18
Patient Safety
• 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
Health IT Workforce Curriculum Version
3.0/Spring 2012 Introduction to Healthcare and Public Health in the US: Regulating Healthcare - Lecture d

19
IOM Reports Summary
• 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

20
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

21Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University
To Err is Human 2: Memory

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To Err is Human 3: Cognition
• 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

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

24
Cognitive Biases in Healthcare
Mamede S, van Gog T, van den Berge K, Rikers RM, van Saase JL, van Guldener C, Schmidt HG. Effect of
availability bias and reflective reasoning on diagnostic accuracy among internal medicine residents. JAMA.
2010 Sep 15;304(11):1198-203.

25
Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them.
Acad Med. 2003 Aug;78(8):775-80.

26
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”

27
• Medication Errors
– Drug Allergies
– Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• Failure to follow clinical practice guidelines
Common Errors

28
Why We Need ICT
in Healthcare?
#2: Because healthcare is
error-prone and technology
can help

29
Fragmented Healthcare
http://www.dplindbenchmark.com/wp-content/uploads/2013/02/HHRI-Our-Health-Care-River.pdf

30
Why We Need ICT
in Healthcare?
#3: Because access to
high-quality patient
information improves care

31
Why We Need ICT
in Healthcare?
#4: Because healthcare at
all levels is fragmented &
in need of process
improvement

32
Outline
Why We Need ICT in Healthcare
• Q&A

33
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: Boonchai Kijsanayotin
Health IT

34
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 & Boonchai Kijsanayotin
eHealth

35
eHealth  Health IT
eHealth & Health IT

36
HIS
All information about health
eHealth
HMIS
mHealth
Tele-
medicine
Slide adapted from: Karl Brown (Rockefeller Foundation), via Boonchai Kijsanayotin
More Terms

37
Health
Information
Technology
Goal
Value-Add
Tools
Health IT: What’s in a Word?

38
 All components are essential
 All components should be balanced
eHealth Components (WHO-ITU Model)

39
eHealth in Thailand: The current status. Stud Health Technol Inform
2010;160:376–80, Presented at MedInfo2010 South Africa
39
Thailand’s eHealth: 2010

40Slide adapted from: Boonchai Kijsanayotin
Thailand: Unbalanced Development

41
eHealth Applications
Enabling Policies &
Strategies
Foundation Policies
& Strategies
• Services
• Applications
• Software
• Standards &
Interoperability
• Capability Building
• Leadership &
Governance
• Legislation & Policy
• Strategy & Investment
• Infrastructure
eHealth Development Model

42Slide adapted from: Boonchai Kijsanayotin
Thailand’s eHealth Development

43
 Silo-type systems
 Little integration and interoperability
 Mostly aim for administration and management
 40% of work-hours spent on managing reports and
documents
 Lack of national leadership and governance body
 Inadequate HIS foundations development
Thailand’s eHealth Situation

44
Outline
Health IT & eHealth
• Q&A

45
Hospital Information System (HIS) Computerized Provider Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
Picture Archiving and
Communication System
(PACS)
Various Forms of Health IT
Screenshot Images from Faculty of Medicine Ramathibodi Hospital, Mahidol University

46
mHealth
Biosurveillance
Telemedicine &
Telehealth
Images from Apple Inc., Geekzone.co.nz, Google, HealthVault.com and American Telecare, Inc.
Personal Health Records
(PHRs) and Patient Portals
Still Many Other Forms of Health IT

47
• Guideline adherence
• Better documentation
• Practitioner decision making or
process of care
• Medication safety
• Patient surveillance & monitoring
• Patient education/reminder
Values of Health IT

48
• Master Patient Index (MPI)
• Admit-Discharge-Transfer (ADT)
• Electronic Health Records (EHRs)
• Computerized Physician Order Entry (CPOE)
• Clinical Decision Support Systems (CDS)
• Picture Archiving and Communication System
(PACS)
• Nursing applications
• Enterprise Resource Planning (ERP)
Enterprise-wide Hospital IT

49
• 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
Departmental IT in Hospitals

50
The Challenge - Knowing What It Means
Electronic Medical
Records (EMRs)
Computer-Based
Patient Records
(CPRs)
Electronic Patient
Records (EPRs)
Electronic Health
Records (EHRs)
Personal Health
Records (PHRs)
Hospital
Information System
(HIS)
Clinical Information
System (CIS)
EHRs & HIS

51
Computerized Provider Order Entry (CPOE)

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

53
• The real place where most of the
values of health IT can be achieved
– Expert systems
• Based on artificial intelligence,
machine learning, rules, or
statistics
• Examples: differential
diagnoses, treatment options(Shortliffe, 1976)
Clinical Decision Support Systems (CDS)

54
– Alerts & reminders
• Based on specified logical conditions
• Examples:
– Drug-allergy checks
– Drug-drug interaction checks
– Reminders for preventive services
– Clinical practice guideline integration
Clinical Decision Support Systems (CDS)

56
• 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)
More CDS Examples

57Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html
Infobuttons

58
• Pre-defined documents
– Order sets, personalized “favorites”
– Templates for clinical notes
– Checklists
– Forms
• Can be either computer-based or
paper-based
Other CDS Examples

59Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
Order Sets

60
• Simple UI designed to help clinical
decision making
– Abnormal lab highlights
– Graphs/visualizations for lab results
– Filters & sorting functions
Other CDS Examples

61Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
Abnormal Lab Highlights

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

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Abnormal lab
highlights
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN

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External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Drug-Allergy
Checks

65
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Drug-Drug
Interaction
Checks

66
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Clinical Practice
Guideline
Reminders

67
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Diagnostic/Treatment
Expert Systems

68Image Source: socialmediab2b.com
IBM’s Watson

69Image Source: englishmoviez.com
Rise of the Machines?

70
• 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
Proper Roles of CDS

71
Some risks
• Alert fatigue
Unintended Consequences of Health IT

73
Outline
Some ICT Applications
• Q&A

74
Hospital A Hospital B
Clinic C
Government
Lab Patient at Home
Health Information Exchange (HIE)

75
Standards & Interoperability in HIE
Technical Standards
(TCP/IP, encryption,
security)
Exchange Standards (HL7 v.2,
HL7 v.3 Messaging, HL7 CDA,
DICOM)
Vocabularies, Terminologies,
Coding Systems (ICD-10, ICD-9,
CPT, SNOMED CT, LOINC)
Information Models (HL7 v.3 RIM,
ASTM CCR, HL7 CCD)
Standard Data Sets
Functional Standards (HL7 EHR
Functional Specifications)
Some may be hybrid: e.g. HL7 v.3, HL7 CCD
Unique ID

76
Hospital A Hospital B
Clinic C
Government
Lab Patient at Home
Message
Message
Message
Message
Message
Message Exchange

77
• As the second formally-trained M.D., Ph.D.
in Health Informatics in Thailand, I am
driven and socially obligated...
• To promote personal & population health
through establishment of sustainable
foundations for eHealth and strengthening
of the field of Biomedical and Health
Informatics in Thailand before my end of
life.
• HIE is at the heart of my life-long dream
My “Mission in Life”

78http://www.ega.or.th/Content.aspx?m_id=94
Cloud: To Go or Not To Go?

79WHO mHealth Report: http://www.who.int/goe/publications/goe_mhealth_web.pdf
Roles of mHealth in Future Healthcare

80
Outline
A Dream for Healthcare
• Q&A

81
• What will the future be for healthcare?
• Where’s the roles of ICT professionals in
future healthcare?
• How to leverage different perspectives &
strengths to achieve common goals?
• How will we shape future healthcare
together?
Some Food for Thought

82
Patients Are Counting on Us...
Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/

83
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
What ICT Will It Be?

84
More Resources
• American Medical Informatics Association (AMIA)
www.amia.org
• International Medical Informatics Association (IMIA)
www.imia.org
• Thai Medical Informatics Association (TMI)
www.tmi.or.th
• Asia eHealth Information Network (AeHIN)
www.aehin.org
• ThaiHealthIT Google Groups Mailing List
http://groups.google.com/group/ThaiHealthIT
• Thai Health Informatics Academy

85
Outline
A Dream for Healthcare
Food for Thought for ICT Folks
• Q&A

ICT Applications for Healthcare

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