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Clinical Decision Support Systems (CDSS)
Sunil NairDalhousie Health InformaticsNovember 1, 2007
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Artificial Intelligence in Medicine Quest for “electronic brain” Early research – “”doctors in a box” More emphasis on Diagnosis “Medical Artificial Intelligence is primarily
concerned with the construction of AI programs that perform diagnosis and make therapy recommendations”
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What are CDSS’s
“active knowledge systems which use two or more items of patient data to generate case-specific advise”
[Wyatt J, Spiegelhalter D, 1991].
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Clinical Decision
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CDSS - Definition
“the provision of Clinical Knowledge and Patient related Information, intelligently filtered or presented at appropriate times, to enhance patient care”
Osheroff JA, Pifer EA, Sittig DF, Jenders RA, Teich JM. Clinical decision support implementers' workbook. Chicago: HIMSS, 2004. www.himss.org/cdsworkbook.
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Evolution: Early Systems.1972 AAPHELP –Leeds Abdominal Pain System
1974 INTERNIST 1 – diagnosis
1976 MYCIN – Infection management -Rule based, IF and THEN rules.PUFF – LIS for interpretation of Pulmonary function test.
1980 HELP – HIS integrated with CDSS
1989 DXplain – CDSS – Diagnosis aid
Current Systems
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Why CDSS’s?
Knowledge at the point of care Apply the best evidence Serve as a peripheral brain – assist Decision making – enhance communication. Improve Healthcare processes and
Outcomes.
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The Information Overload
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Clinical decision makingPaul Gorman, Medical Decision Making 1995
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Clinical Workflow: How does CDSS fit?
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CDSS application areas
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CDSS - Applications Alerts and reminders Diagnostics Assistance Therapy critiquing and planning Prescribing decision support systems Information Retrieval Image recognition and interpretation Diagnostic and educational systems
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CDSS Functions - trend
Administration Managing clinical complexity details Cost control Decision support
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An Effective CDSS should: Speed
Anticipate/Suggest
Fit in to user’s Workflow
User friendly-interface-rules
Alerts should be descriptive
Changing direction
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Effective CDSS features contd. Simple guidelines
Prompt Additional Information only when required
Monitor impact, feedback and respond
Manage and maintain Knowledge Based system
Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality. David W. Bates, MD, MSc, Division of General Medicine and Primary Care, Brigham and Women's Hospital, May 27, 2003
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CDSS Goals
Patient Safety – Error Reduction/prevention Cost Reduction – without compromising care Promoting best practice – Enforcing
compliance (Practice Guidelines)
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What makes CDSS possible? Machine Learning Systems/Expert Systems
Create new knowledge Expressed as RULES or decision aids. KARDIO – for interpreting ECGA Study in Deep and Qualitative Knowledge for Expert Systems
Ivan Bratko, et al., Nov 1989http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=5059 Data Mining and Knowledge Management
systems KNOWLEDGE MANAGEMENT, DATA MINING, AND TEXT MINING IN
MEDICAL INFORMATICS., Hsinchun chen et al., http://ai.arizona.edu/hchen/chencourse/MedBook/Chapter_01.pdf
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CDSS - Benefits Improve patient safety
Reduce medical errors Improved medication and test ordering
Improve quality of care Application of Clinical Pathways and Guidelines Evidence based Medicine Improved Clinical documentation Increase quality time for direct patient care
Improve efficiency in Healthcare delivery Reduce costs, reduce test duplication, decrease
adverse events
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CDSS: Computerized Physician Order Entry Growing evidence that CPOE reduce medical
errors and adverse drug events.Effects of Computerized Physician Order Entry and Clinical Decision
Support Systems on Medication Safety Rainu Kaushal,MD,MPH et al Arch Intern Med. 2003 http://archinte.ama-assn.org/cgi/content/full/163/12/1409
Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes
Garg et al JAMA. 2005http://jama.ama-assn.org/cgi/content/full/293/10/1223
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Opposing views…
CPOE facilitate medication error ‘risks’, create new errors.
Role of Computerized Physician Order Entry Systems in Facilitating
Medication Errors. Ross Koppel,PhD et al. JAMA. 2005 http://jama.ama-assn.org/cgi/content/full/293/10/1197
Computer Technology and Clinical Work Robert L. Wears et. al. JAMA. 2005;293:1261-1263. http://jama.ama-assn.org/cgi/content/full/293/10/1261
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CDSS: Drawbacks
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CDSS Issues: Success or Failure
Careful evaluation of user needs Leadership Integration Issues Human-Computer interface
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Why does CDSS fail?
Belief that Diagnosis is the dominant decision making issue “what does this patient have?” vs. “how can I help
this patient” Cognitive factors – different people
understand differently. Human-Computer interaction.
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=130077
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CDSS reasons for failure.
Dependence on electronic patient record. Challenging task of interaction between
technologies and organizations. Only as effective as the underlying
Knowledge base, needs constant updating. Additional effort (already busy, overworked) Resist to Change Computer Literacy
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CDSS Integration Objectives:
Critical Evaluation Identifying main factors involved Understand that Healthcare is Complex and
Patient focused Efficient Data Management Tracking the “Alerts” Standardization/Interoperability
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Evaluation
Problem Definition
Potential for Errors
Change Issues
Measure Outcomes
Implementation
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CDSS: Summary
Possible in a complex healthcare environment
Has to fit in to the workflow Enhance patient safety features
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Conclusion
The future of CDSS depends on removal of barriers to implementation.
Continue to have profound effect on medical education
Trained clinicians will always be required, the key is cooperative relationship between physician and computer based decision making tool
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Discussions CDSS recommendation
to clinician results in patient harm, who is responsible?
Questions
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Thank you!