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Clinical Decision
Support Systems
ITCS 404 IT for Healthcare Services
Nawanan Theera-Ampornpunt, M.D., Ph.D.
October 19, 2013
http://www.SlideShare.net/Nawanan
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
WHAT IS A DECISION?
4
Wisdom
Knowledge
Information
Data
Data-Information-Knowledge-
Wisdom (DIKW) Pyramid
5
Wisdom
Knowledge
Information
Data
Contextualization/
Interpretation
Processing/
Synthesis/
Organization
Judgment
Data-Information-Knowledge-
Wisdom (DIKW) Pyramid
6
Wisdom
Knowledge
Information
Data
Contextualization/
Interpretation
Processing/
Synthesis/
Organization
Judgment
100,000,000
I have 100,000,000
baht in my bank
account
I am rich!!!!!
I should buy a luxury car
(and a BIG house)!
Example
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
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
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
LET’S TAKE A LOOK AT
PATIENT CARE PROCESS
11
Patient Care
Image Sources: (Left) Faculty of Medicine Ramathibodi Hospital (Right) /en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)
12
EXERCISE 1
Provide some examples of
“decisions” health care
providers make
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
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
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
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
CLINICAL
DECISION MAKING
18
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
19
PROBLEMS WITH
HUMAN’S
DECISION MAKING
20
• Perception errors
Pitfalls of Human Decision Making
Image Source: interaction-dynamics.com
21
• Lack of Attention
Pitfalls of Human Decision Making
Image Source: aafp.org
22
• 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
Pitfalls of Human Decision Making
23
IOM (2000)
“To Err Is Human”
24
• Medical Errors
–Drug allergies
–Drug interactions
• Abnormal Lab Findings
• Clinical Practice Guidelines
• Bias in Judgment & Decision-Making
What About Health Care?
25
ROLES OF
INFORMATION TECHNOLOGY
IN DECISION MAKING
26
EXERCISE 2
Provide some examples on
how IT can help reduce errors
in clinical decision making
27
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
28
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Possible Human Errors
Possibility of
Human Errors
29
CLINICAL DECISION
SUPPORT SYSTEMS
(CDS)
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
• 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
• The real place where most of the values
of health IT can be achieved
• There are a variety of forms and nature
of CDS
Clinical Decision Support
Systems (CDS)
33
• Expert systems
–Based on artificial
intelligence, machine
learning, rules, or
statistics
–Examples: differential
diagnoses, treatment
options
CDS Examples
Shortliffe (1976)
34
• 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)
CDS Examples
35
Example of “Reminders”
36
• 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)
CDS Examples
37
Infobuttons
Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html
38
• Pre-defined documents
–Order sets, personalized “favorites”
–Templates for clinical notes
–Checklists
–Forms
• Can be either computer-based or
paper-based
CDS Examples
39
Order Sets
Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
40
• Simple UI designed to help clinical
decision making
–Abnormal lab highlights
–Graphs/visualizations for lab results
–Filters & sorting functions
CDS Examples
41
Abnormal Lab Highlights
Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
42
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Abnormal lab
highlights
43
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Order Sets
44
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Drug-Allergy
Checks
45
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Drug-Drug
Interaction
Checks
46
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Drug-Drug
Interaction
Checks
47
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Clinical Practice
Guideline
Alerts/Reminders
48
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Integration of
Evidence-Based
Resources (e.g.
drug databases,
literature)
49
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports
Decision Making
Diagnostic/Treatment
Expert Systems
50
User User Interface
Patient
Data
Inference Engine
Knowledge
BaseOther Data
• Rules & Parameters
• Statistical data
• Literature
• Etc.
• System states
• Epidemiological/
surveillance data
• Etc.
Example of CDS
Architecture
Other
Systems
51
ISSUES RELATED TO
CDS IMPLEMENTATION
52
• 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
Human Factor Issues of CDS
53
IBM’s Watson
Image Source: socialmediab2b.com
54
Image Source: englishmoviez.com
Rise of the Machines?
55
Issues
• CDSS as a supplement or replacement of clinicians?
– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
The “Greek Oracle” Model
The “Fundamental Theorem”
Friedman (2009)
Human Factor Issues of CDS
Wrong Assumption
Correct Assumption
56
• 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
Human Factor Issues of CDS
57
Issues
• Alert sensitivity & alert fatigue
Alert Fatigue
58
• Liabilities
– Clinicians as “learned intermediaries”
• Prohibition of certain transactions vs.
Professional autonomy
(see Strom et al., 2010)
Ethical-Legal Issues of CDS
59
Workarounds
60
• “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)
Unintended Consequences of
CDS & Health IT
61
Ash et al. (2004)
Unintended Consequences of
CDS & Health IT
62
• 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
63
• 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
64
• 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?
Technical Issues of CDS
65
• Choosing the right CDSS strategies
• Expertise required for proper CDSS design &
implementation
• Everybody agreeing on the “rules” to be enforced
• Evaluation of effectiveness
Other Issues
66
• 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
67
• 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
Key Points
68
• 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
Key Points
69
• 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
Key Points
70
Intelligent &
helpful
machines
Machines with a
human touch
Machines that
replace humans
HAL 9000 Data David NS-5
Dangerous
killer machines
What Will The Future Be
for Health Care?
71
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.
72
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.
73
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.

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Clinical Decision Support Systems (MUICT Teaching)

  • 1. Clinical Decision Support Systems ITCS 404 IT for Healthcare Services Nawanan Theera-Ampornpunt, M.D., Ph.D. October 19, 2013 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?
  • 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?
  • 18. 18 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  • 20. 20 • Perception errors Pitfalls of Human Decision Making Image Source: interaction-dynamics.com
  • 21. 21 • Lack of Attention Pitfalls of Human Decision Making Image Source: aafp.org
  • 22. 22 • 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 Pitfalls of Human Decision Making
  • 23. 23 IOM (2000) “To Err Is Human”
  • 24. 24 • Medical Errors –Drug allergies –Drug interactions • Abnormal Lab Findings • Clinical Practice Guidelines • Bias in Judgment & Decision-Making What About Health Care?
  • 26. 26 EXERCISE 2 Provide some examples on how IT can help reduce errors in clinical decision making
  • 27. 27 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  • 28. 28 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Possible Human Errors Possibility of Human Errors
  • 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 • The real place where most of the values of health IT can be achieved • There are a variety of forms and nature of CDS Clinical Decision Support Systems (CDS)
  • 33. 33 • Expert systems –Based on artificial intelligence, machine learning, rules, or statistics –Examples: differential diagnoses, treatment options CDS Examples Shortliffe (1976)
  • 34. 34 • 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) CDS Examples
  • 36. 36 • 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) CDS Examples
  • 38. 38 • Pre-defined documents –Order sets, personalized “favorites” –Templates for clinical notes –Checklists –Forms • Can be either computer-based or paper-based CDS Examples
  • 39. 39 Order Sets Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
  • 40. 40 • Simple UI designed to help clinical decision making –Abnormal lab highlights –Graphs/visualizations for lab results –Filters & sorting functions CDS Examples
  • 41. 41 Abnormal Lab Highlights Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
  • 42. 42 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Abnormal lab highlights
  • 43. 43 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Order Sets
  • 44. 44 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Drug-Allergy Checks
  • 45. 45 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Drug-Drug Interaction Checks
  • 46. 46 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports 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) How CDS Supports Decision Making Clinical Practice Guideline Alerts/Reminders
  • 48. 48 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Integration of Evidence-Based Resources (e.g. drug databases, literature)
  • 49. 49 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Diagnostic/Treatment Expert Systems
  • 50. 50 User User Interface Patient Data Inference Engine Knowledge BaseOther Data • Rules & Parameters • Statistical data • Literature • Etc. • System states • Epidemiological/ surveillance data • Etc. Example of CDS Architecture Other Systems
  • 51. 51 ISSUES RELATED TO CDS IMPLEMENTATION
  • 52. 52 • 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 Human Factor Issues of CDS
  • 53. 53 IBM’s Watson Image Source: socialmediab2b.com
  • 55. 55 Issues • CDSS as a supplement or replacement of clinicians? – The demise of the “Greek Oracle” model (Miller & Masarie, 1990) The “Greek Oracle” Model The “Fundamental Theorem” Friedman (2009) Human Factor Issues of CDS Wrong Assumption Correct Assumption
  • 56. 56 • 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 Human Factor Issues of CDS
  • 57. 57 Issues • Alert sensitivity & alert fatigue Alert Fatigue
  • 58. 58 • Liabilities – Clinicians as “learned intermediaries” • Prohibition of certain transactions vs. Professional autonomy (see Strom et al., 2010) Ethical-Legal Issues of CDS
  • 60. 60 • “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) Unintended Consequences of CDS & Health IT
  • 61. 61 Ash et al. (2004) Unintended Consequences of CDS & Health IT
  • 62. 62 • 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
  • 63. 63 • 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
  • 64. 64 • 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? Technical Issues of CDS
  • 65. 65 • Choosing the right CDSS strategies • Expertise required for proper CDSS design & implementation • Everybody agreeing on the “rules” to be enforced • Evaluation of effectiveness Other Issues
  • 66. 66 • 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
  • 67. 67 • 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 Key Points
  • 68. 68 • 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 Key Points
  • 69. 69 • 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 Key Points
  • 70. 70 Intelligent & helpful machines Machines with a human touch Machines that replace humans HAL 9000 Data David NS-5 Dangerous killer machines What Will The Future Be for Health Care?
  • 71. 71 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.
  • 72. 72 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.
  • 73. 73 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.