This document discusses a presentation about ICT applications for healthcare given by Dr. Nawanan Theera-Ampornpunt. It provides background on her education and experience in health informatics. The presentation covers why healthcare needs ICT due to issues like errors, fragmentation, and large amounts of information. It defines key terms like health IT, eHealth, and examples of ICT applications like EHRs, telemedicine, and clinical decision support systems. It discusses the need for standards, interoperability, and a vision for connected healthcare information exchange.
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ICT Applications for Healthcare
1. 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. 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. 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
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 Like Any Others
11. 11
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. 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. 13
Information is Everywhere in Healthcare
Shortliffe EH. Biomedical informatics in the education of
physicians. JAMA. 2010 Sep 15;304(11):1227-8.
15. 15
Outline
“Information” in Healthcare
• Why We Need ICT in Healthcare
• Health IT & eHealth
• Some ICT Applications
• A Dream for Healthcare
• Food for Thought for ICT Folks
• Q&A
16. 16
Why We Need ICT
in Healthcare?
#1: Because information is
everywhere in healthcare
18. 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. 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. 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
21. 21Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University
To Err is Human 2: Memory
22. 22
To Err is Human 3: Cognition
• Cognitive Errors - Example: Decoy Pricing
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Ariely (2008)
16
0
84
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68
32
# of
People
# of
People
23. 23
• It already happens....
(Mamede et al., 2010; Croskerry, 2003;
Klein, 2005; Croskerry, 2013)
What If This Happens in Healthcare?
24. 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. 25
Cognitive Biases in Healthcare
Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them.
Acad Med. 2003 Aug;78(8):775-80.
26. 26
Cognitive Biases in Healthcare
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. 27
• Medication Errors
– Drug Allergies
– Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• Failure to follow clinical practice guidelines
Common Errors
28. 28
Why We Need ICT
in Healthcare?
#2: Because healthcare is
error-prone and technology
can help
30. 30
Why We Need ICT
in Healthcare?
#3: Because access to
high-quality patient
information improves care
31. 31
Why We Need ICT
in Healthcare?
#4: Because healthcare at
all levels is fragmented &
in need of process
improvement
32. 32
Outline
“Information” in Healthcare
Why We Need ICT in Healthcare
• Health IT & eHealth
• Some ICT Applications
• A Dream for Healthcare
• Food for Thought for ICT Folks
• Q&A
33. 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. 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. 35
eHealth Health IT
Slide adapted from: Boonchai Kijsanayotin
eHealth & Health IT
36. 36
HIS
All information about health
eHealth
HMIS
mHealth
Tele-
medicine
Slide adapted from: Karl Brown (Rockefeller Foundation), via Boonchai Kijsanayotin
More Terms
38. 38
All components are essential
All components should be balanced
Slide adapted from: Boonchai Kijsanayotin
eHealth Components (WHO-ITU Model)
39. 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
43. 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
Slide adapted from: Boonchai Kijsanayotin
Thailand’s eHealth Situation
44. 44
Outline
“Information” in Healthcare
Why We Need ICT in Healthcare
Health IT & eHealth
• Some ICT Applications
• A Dream for Healthcare
• Food for Thought for ICT Folks
• Q&A
45. 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
47. 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. 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. 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. 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
52. 52
Values
• No handwriting!!!
• Structured data entry: Completeness, clarity,
fewer mistakes (?)
• No transcription errors!
• Streamlines workflow, increases efficiency
Computerized Provider Order Entry (CPOE)
53. 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. 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. 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
58. 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
60. 60
• Simple UI designed to help clinical
decision making
– Abnormal lab highlights
– Graphs/visualizations for lab results
– Filters & sorting functions
Other CDS Examples
62. 62
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
63. 63
Abnormal lab
highlights
Clinical Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
64. 64
Clinical Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Drug-Allergy
Checks
65. 65
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
Drug-Drug
Interaction
Checks
66. 66
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
Clinical Practice
Guideline
Reminders
67. 67
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
Diagnostic/Treatment
Expert Systems
70. 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
73. 73
Outline
“Information” in Healthcare
Why We Need ICT in Healthcare
Health IT & eHealth
Some ICT Applications
• A Dream for Healthcare
• Food for Thought for ICT Folks
• Q&A
74. 74
Hospital A Hospital B
Clinic C
Government
Lab Patient at Home
Health Information Exchange (HIE)
75. 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. 76
Hospital A Hospital B
Clinic C
Government
Lab Patient at Home
Message
Message
Message
Message
Message
Message Exchange
77. 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”
79. 79WHO mHealth Report: http://www.who.int/goe/publications/goe_mhealth_web.pdf
Roles of mHealth in Future Healthcare
80. 80
Outline
“Information” in Healthcare
Why We Need ICT in Healthcare
Health IT & eHealth
Some ICT Applications
A Dream for Healthcare
• Food for Thought for ICT Folks
• Q&A
81. 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. 82
Patients Are Counting on Us...
Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/
84. 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. 85
Outline
“Information” in Healthcare
Why We Need ICT in Healthcare
Health IT & eHealth
Some ICT Applications
A Dream for Healthcare
Food for Thought for ICT Folks
• Q&A