For Mahidol University Faculty of ICT's 2014 Health IT Course (ITCS404)

1. Clinical Decision
Support Systems
ITCS 404 IT for Healthcare Services
Nawanan Theera-Ampornpunt, M.D., Ph.D.
September 27, 2014
http://www.SlideShare.net/Nawanan

2. 2
Outline
• What is a Decision?
• Clinical Decision Making
• Roles of IT in Decision Making
• Clinical Decision Support Systems
– Definitions
– Types & examples
– Architecture
• Issues Related to CDS Implementation
• Summary

3. 3
WHAT IS A DECISION?

4. 4
Data-Information-Knowledge-
Wisdom (DIKW) Pyramid
Wisdom
Knowledge
Information
Data

5. 5
Data-Information-Knowledge-
Wisdom (DIKW) Pyramid
Wisdom
Knowledge
Information
Data
Judgment
Processing/
Synthesis/
Organization
Contextualization/
Interpretation

6. 6
Wisdom
Knowledge
Information
Data
I should buy a luxury car
(and a BIG house)!
Judgment
I am rich!!!!!
Processing/
Synthesis/
Organization
I have 100,000,000
baht in my bank
account
Contextualization/
Interpretation
100,000,000
Example

7. 7
Example: Problem A
• Patient A has a blood pressure reading of
170/100 mmHg
• Data: 170/100
• Information: BP of Patient A = 170/100 mmHg
• Knowledge: Patient A has high blood pressure
• Wisdom (or Decision):
– Patient A needs to be investigated for cause of HT
– Patient A needs to be treated with anti-hypertensives
– Patient A needs to be referred to a cardiologist

8. 8
Example: Problem B
• Patient B is allergic to penicillin. He was recently
prescribed amoxicillin for his sore throat.
• Data: Penicillin, amoxicillin, sore throat
• Information:
– Patient B has penicillin allergy
– Patient B was prescribed amoxicillin for his sore throat
• Knowledge:
– Patient B may have allergic reaction to his prescription
• Wisdom (or Decision):
– Patient B should not take amoxicillin!!!

9. 9
Decision & Decision Making
• Decision
– “A choice that you make about something
after thinking about it : the result of deciding”
(Merriam-Webster Dictionary)
• Decision making
– “The cognitive process resulting in the
selection of a course of action among several
alternative scenarios.” (Wikipedia)

10. 10
LET’S TAKE A LOOK AT
PATIENT CARE PROCESS

11. 11
Patient Care
Image Sources: (Left) Faculty of Medicine Ramathibodi Hospital (Right) /en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)

12. 12
EXERCISE 1
Provide some examples of
“decisions” health care
providers make

13. 13
Clinical Decisions
• Patient Care
– What patient history to ask?
– What physical examinations to do?
– What investigations to order?
• Lab tests
• Radiologic studies (X-rays, CTs, MRIs, etc.)
• Other special investigations (EKG, etc.)
– What diagnosis (or possible diagnosis) to
make?

14. 14
Clinical Decisions
• Patient Care
– What treatment to order/perform?
• Medications
• Surgery/Procedures/Nursing Interventions
• Patient Education/Advice for Self-Care
• Admission
– How should patient be followed-up?
– With good or poor response to treatment, what
to do next?
– With new information, what to do next?

15. 15
Clinical Decisions
• Management
– How to improve quality of care and clinical
operations?
– How to allocate limited budget & resources?
– What strategies should the hospital pursue &
what actions/projects should be done?

16. 16
Clinical Decisions
• Public Health
– How to improve health of population?
– How to investigate/control/prevent disease
outbreak?
– How to allocate limited budget & resources?
– What areas of the country’s public health need
attention & what to do with it?

17. 17
CLINICAL
DECISION MAKING

18. 18
Clinical Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
PATIENT
Perception
Attention
Working
Memory
Inference
DECISION
CLINICIAN
Elson, Faughnan & Connelly (1997)

19. 19
PROBLEMS WITH
HUMAN’S
DECISION MAKING

20. 20
Pitfalls of Human Decision Making
• Perception errors
Image Source: interaction-dynamics.com

21. 21
Pitfalls of Human Decision Making
• Lack of Attention
Image Source: aafp.org

22. 22
Pitfalls of Human Decision Making
• Cognitive Errors - Example: Decoy Pricing
The Economist Purchase Options
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Ariely (2008)
# of
People
16
0
84
The Economist Purchase Options
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# of
People
68
32

23. 23
“To Err Is Human”
IOM (2000)

24. • Medical Errors
–Drug allergies
–Drug interactions
• Abnormal Lab Findings
• Clinical Practice Guidelines
• Bias in Judgment & Decision-Making
24
What About Health Care?

25. 25
ROLES OF
INFORMATION TECHNOLOGY
IN DECISION MAKING

26. 26
EXERCISE 2
Provide some examples on
how IT can help reduce errors
in clinical decision making

27. 27
Clinical Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
PATIENT
Perception
Attention
Working
Memory
Inference
DECISION
CLINICIAN
Elson, Faughnan & Connelly (1997)

28. 28
Possible Human Errors
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
PATIENT
Perception
Attention
Working
Memory
Inference
DECISION
CLINICIAN
Elson, Faughnan & Connelly (1997)
Possibility of
Human Errors

29. 29
CLINICAL DECISION
SUPPORT SYSTEMS
(CDS)

30. 30
• Clinical Decision Support (CDS) “is a
process for enhancing health-related
decisions and actions with pertinent,
organized clinical knowledge and patient
information to improve health and healthcare
delivery” (Including both computer-based &
non-computer-based CDS)
(Osheroff et al., 2012)
What Is A CDS?

31. 31
• Computer-based clinical decision support
(CDS): “Use of the computer [ICT] to bring
relevant knowledge to bear on the health
care and well being of a patient.”
(Greenes, 2007)
What Is A CDS?

32. 32
Clinical Decision Support
Systems (CDS)
• The real place where most of the values
of health IT can be achieved
• There are a variety of forms and nature
of CDS

33. 33
CDS Examples
• Expert systems
–Based on artificial
intelligence, machine
learning, rules, or
statistics
– Examples: differential
diagnoses, treatment
options
Shortliffe (1976)

34. 34
CDS Examples
• Alerts & reminders
–Based on specified logical conditions
• Drug-allergy checks
• Drug-drug interaction checks
• Drug-lab interaction checks
• Drug-formulary checks
• Reminders for preventive services or certain actions
(e.g. smoking cessation)
• Clinical practice guideline integration (e.g. best
practices for chronic disease patients)

35. 35
Example of “Reminders”

36. 36
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)

37. 37
Infobuttons
Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html

38. 38
CDS Examples
• Pre-defined documents
–Order sets, personalized “favorites”
–Templates for clinical notes
–Checklists
–Forms
• Can be either computer-based or
paper-based

39. 39
Order Sets
Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm

40. 40
CDS Examples
• Simple UI designed to help clinical
decision making
–Abnormal lab highlights
–Graphs/visualizations for lab results
–Filters & sorting functions

41. 41
Abnormal Lab Highlights
Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html

42. 42
How CDS Supports
Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
PATIENT
Perception
Attention
Working
Memory
Inference
DECISION
CLINICIAN
Elson, Faughnan & Connelly (1997)
Abnormal lab
highlights

43. 43
How CDS Supports
Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
PATIENT
Perception
Attention
Working
Memory
Inference
DECISION
CLINICIAN
Elson, Faughnan & Connelly (1997)
Order Sets

44. 44
How CDS Supports
Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
PATIENT
Perception
Attention
Working
Memory
Inference
DECISION
CLINICIAN
Elson, Faughnan & Connelly (1997)
Drug-Allergy
Checks

45. 45
How CDS Supports
Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
PATIENT
Perception
Attention
Working
Memory
Inference
DECISION
CLINICIAN
Elson, Faughnan & Connelly (1997)
Drug-Drug
Interaction
Checks

46. 46
How CDS Supports
Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
PATIENT
Perception
Attention
Working
Memory
Inference
DECISION
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Practice
Guideline
Alerts/Reminders

47. 47
How CDS Supports
Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
PATIENT
Perception
Attention
Working
Memory
Inference
DECISION
CLINICIAN
Elson, Faughnan & Connelly (1997)
Integration of
Evidence-Based
Resources (e.g.
drug databases,
literature)

48. 48
How CDS Supports
Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
PATIENT
Perception
Attention
Working
Memory
Inference
DECISION
CLINICIAN
Elson, Faughnan & Connelly (1997)
Diagnostic/Treatment
Expert Systems

49. 49
Example of CDS
Architecture
User User Interface
Patient
Data
Inference Engine
Knowledge
Other Data Base
• Rules & Parameters
• Statistical data
• Literature
• Etc.
• System states
• Epidemiological/
surveillance data
• Etc.
Other
Systems

50. 50
ISSUES RELATED TO
CDS IMPLEMENTATION

51. 51
Human Factor Issues of CDS
• How will CDS be implemented in real life?
• Will it interfere with user workflow?
• Will it be used by users? If not, why?
• What user interface design is best?
• What are most common user complaints?
• Who is responsible if something bad
happens?
• How to balance reliance on machines &
humans

52. 52
IBM’s Watson
Image Source: socialmediab2b.com

53. 53
Image Source: englishmoviez.com
Rise of the Machines?

54. 54
Human Factor Issues of CDS
Issues
• CDSS as a supplement or replacement of clinicians?
– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
The “Greek Oracle” Model
Wrong Assumption
The “Fundamental Theorem”
Correct Assumption
Friedman (2009)

55. 55
Human Factor Issues of CDS
• Features with improved clinical practice
(Kawamoto et al., 2005)
– Automatic provision of decision support as part of
clinician workflow
– Provision of recommendations rather than just
assessments
– Provision of decision support at the time and location of
decision making
– Computer based decision support
• Usability & impact on productivity

56. 56
Alert Fatigue
Issues
• Alert sensitivity & alert fatigue

57. 57
Ethical-Legal Issues of CDS
• Liabilities
– Clinicians as “learned intermediaries”
• Prohibition of certain transactions vs.
Professional autonomy
(see Strom et al., 2010)

58. 58
Workarounds

59. 59
Unintended Consequences of
CDS & Health IT
• “Unanticipated and unwanted effect of
health IT implementation”
(www.ucguide.org)
• Resources
– www.ucguide.org
– Ash et al. (2004)
– Campbell et al. (2006)
– Koppel et al. (2005)

60. 60
Ash et al. (2004)
Unintended Consequences of
CDS & Health IT

61. 61
• Errors in the process of entering and
retrieving information
– A human-computer interface that is not
suitable for a highly interruptive use context
– Causing cognitive overload by
overemphasizing structured and “complete”
information entry or retrieval
• Structure
• Fragmentation
• Overcompleteness
Ash et al. (2004)
Unintended Consequences of
CDS & Health IT

62. 62
• Errors in communication & coordination
– Misrepresenting collective, interactive work as
a linear, clearcut, and predictable workflow
• Inflexibility
• Urgency
• Workarounds
• Transfers of patients
– Misrepresenting communication as information
transfer
• Loss of communication
• Loss of feedback
• Decision support overload
• Catching errors
Ash et al. (2004)
Unintended Consequences of
CDS & Health IT

63. 63
Technical Issues of CDS
• Which type of CDS should be chosen?
• What algorithms should be used?
• How to “represent” knowledge in the system?
• How to update/maintain knowledge base in
the system?
• How to standardize data/knowledge?
• How to implement CDS with good system
performance?

64. 64
Other Issues
• Choosing the right CDSS strategies
• Expertise required for proper CDSS design &
implementation
• Everybody agreeing on the “rules” to be enforced
• Evaluation of effectiveness

65. 65
• Speed is Everything
• Anticipate Needs and Deliver in Real Time
• Fit into the User’s Workflow
• Little Things (like Usability) Can Make a Big Difference
• Recognize that Physicians Will Strongly Resist Stopping
• Changing Direction Is Easier than Stopping
• Simple Interventions Work Best
• Ask for Additional Information Only When You Really Need It
• Monitor Impact, Get Feedback, and Respond
• Manage and Maintain Your Knowledge-based Systems
Bates et al. (2003)
“Ten Commandments” for
Effective CDS

66. 66
Key Points
• There are several decisions made in a clinical
patient care process
• Data leads to information, knowledge, and
ultimately, decision & actions
• Human clinicians are not perfect and can make
mistakes
• A clinical decision support systems (CDS) provides
support for clinical decision making (to prevent
mistakes & provide best patient care)
• A CDS can be computer-based or paper-based

67. 67
Key Points
• CDS comes in various forms, designs, and
architecture
• There are many issues related to design,
implementation and use of CDS
– Technical Issues
– Human Factor Issues
– Ethical-Legal Issues

68. 68
Key Points
• Current mindset: CDS should be used to
help, not replace, human providers
• Be attentive to workarounds, alert fatigues,
and other unintended consequences of CDS
– They can cause more danger to patients!!
– They may lead users to abandon using CDS (a
failure)
• There are recommendations on how to best
design & implement CDS

69. 69
What Will The Future Be
for Health Care?
HAL 9000 Data David NS-5
Dangerous
killer machines
Intelligent &
helpful
machines
Machines with a
human touch
Machines that
replace humans

70. 70
References
• Ash JS, Berg M, Coiera E. Some unintended consequences of information
technology in health care: the nature of patient care information system-related
errors. J Am Med Inform Assoc. 2004 Mar-Apr;11(2):104-12.
• Ariely D. Predictably irrational: the hidden forces that shape our decisions. New
York City (NY): HarperCollins; 2008. 304 p.
• Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, Spurr C, Khorasani R,
Tanasijevic M, Middleton B. Ten commandments for effective clinical decision
support: making the practice of evidence-based medicine a reality. J Am Med
Inform Assoc. 2003 Nov-Dec;10(6):523-30.
• Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended
consequences related to computerized provider order entry. J Am Med Inform
Assoc. 2006 Sep-Oct;13(5):547-56.
• Elson RB, Faughnan JG, Connelly DP. An industrial process view of information
delivery to support clinical decision making: implications for systems design
and process measures. J Am Med Inform Assoc. 1997 Jul-Aug;4(4):266-78.
• Friedman CP. A "fundamental theorem" of biomedical informatics. J Am Med
Inform Assoc. 2009 Apr;16(2):169-170.

71. 71
References
• Greenes RA. Clinical decision support: the road ahead. Oxford (UK): Elsevier;
2007. 581 p.
• Institute of Medicine, Committee on Quality of Health Care in America. To err is
human: building a safer health system. Kohn LT, Corrigan JM, Donaldson MS,
editors. Washington, DC: National Academy Press; 2000. 287 p.
• Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice
using clinical decision support systems: a systematic review of trials to
identify features critical to success. BMJ. 2005 Apr 2;330(7494):765.
• Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, et al. Role of
computerized physician order entry systems in facilitating medication errors.
JAMA. 2005 Mar 9;293(10):1197-1203.
• Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical
diagnostic systems. Methods Inf Med. 1990 Jan;29(1):1-2.
• Osheroff JA, Teich JM, Levick D, Saldana L, Velasco FT, Sittig DF, Rogers KM,
Jenders RA. Improving outcomes with clinical decision support: an
implementer’s guide. 2nd ed. Chicago (IL): Healthcare Information and
Management Systems Society; 2012. 323 p.

72. 72
References
• Shortliffe EH. Computer-based medical consultations: MYCIN. New York (NY):
Elsevier; 1976. 264 p.
• Strom BL, Schinnar R, Aberra F, Bilker W, Hennessy S, Leonard CE, Pifer E.
Unintended effects of a computerized physician order entry nearly hard-stop
alert to prevent a drug interaction: a randomized controlled trial. Arch Intern
Med. 2010 Sep 27;170(17):1578-83.