8. 8
• 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. 10
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)
11. 11
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.
12. 12
Information is Everywhere in Healthcare
Shortliffe EH. Biomedical informatics in the education of
physicians. JAMA. 2010 Sep 15;304(11):1227-8.
16. 16
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
17. 17
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
18. 18Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University
To Err is Human 2: Memory
19. 19
To Err is Human 3: Cognition
• Cognitive Errors - Example: Decoy Pricing
The Economist Purchase Options
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Ariely (2008)
16
0
84
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68
32
# of
People
# of
People
20. 20
• It already happens....
(Mamede et al., 2010; Croskerry, 2003;
Klein, 2005; Croskerry, 2013)
What If This Happens in Healthcare?
21. 21
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.
22. 22
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.
23. 23
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”
24. 24
• Medication Errors
– Drug Allergies
– Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• Failure to follow clinical practice guidelines
Common Errors
25. 25
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
27. 27
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
29. 29
• Guideline adherence
• Better documentation
• Practitioner decision making or
process of care
• Medication safety
• Patient surveillance & monitoring
• Patient education/reminder
Values of Health IT
30. 30
• 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
31. 31
• 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
33. 33
Values
• No handwriting!!!
• Structured data entry: Completeness, clarity,
fewer mistakes (?)
• No transcription errors!
• Streamlines workflow, increases efficiency
Computerized Provider Order Entry (CPOE)
34. 34
• 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)
35. 35
– 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)
37. 37
• 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
39. 39
• Pre-defined documents
– Order sets, personalized “favorites”
– Templates for clinical notes
– Checklists
– Forms
• Can be either computer-based or
paper-based
Other CDS Examples
41. 41
• Simple UI designed to help clinical
decision making
– Abnormal lab highlights
– Graphs/visualizations for lab results
– Filters & sorting functions
Other CDS Examples
43. 43
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
44. 44
Abnormal lab
highlights
Clinical Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
45. 45
Clinical Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Drug-Allergy
Checks
46. 46
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
47. 47
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
48. 48
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
49. 49
• 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
53. 53
Hospital A Hospital B
Clinic C
Government
Lab Patient at Home
Health Information Exchange (HIE)
54. 54
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
