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Translating Clinical Guidelines into Knowledge-guided Decision Support


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Translating Clinical Guidelines into Knowledge-guided Decision Support. Middleton B. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)

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Translating Clinical Guidelines into Knowledge-guided Decision Support

  1. 1. Translating Clinical Guidelines into Knowledge-Guided Decision Support Blackford Middleton, MD, MPH, MSc, FACP, FACMI, FHIMSS Corporate Director, Clinical Informatics Research & Development Chairman, Center for Information Technology Leadership Harvard Medical School
  2. 2. Overview • The Evidence for CDS • The Value Potential of CDS • Current examples and R&D Projects • The Clinical Decision Support Consortium
  3. 3. Flexner Report "...The curse of medical education is the excessive number of schools. The situation can improve only as weaker and superfluous schools are extinguished." “Society reaps at this moment but a small fraction of the advantage which current knowledge has the power to confer.” Abraham Flexner, Medical Education in the United States and Canada. Boston: Merrymount Press, 1910
  4. 4. The Evidence for CDS • CDS yields increased adherence to guideline-based care, enhanced surveillance and monitoring, and decreased medication errors • (Chaudhry et al., 2006) • CDS, at the time of order entry in a computerized provider order entry system can help eliminate overuse, underuse, and misuse. • (Bates et al., 2003; Austin et al., 1994; Linder, Bates and Lee, 2005; Tierney et al., 2003) • For expensive radiologic tests and procedures this guidance at the point of ordering can guide physicians toward ordering the most appropriate and cost effective, radiologic tests. • (Bates et al., 2003; Khorasani et al., 2003) • Showing the cumulative charge display for all tests ordered, reminding about redundant tests ordered, providing counter-detailing during order entry, and reminding about consequent or corollary orders may also impact resource utilization • (Bates and Gawande, 2003; Bates, 2004; McDonald et al., 2004).
  5. 5. The Value of Ambulatory CDS • Savings potential: $44 billion • reduced medication, radiology, laboratory, and ADE- related expenses • Advanced CDS systems • Savings potential only with advanced CDS • cost five times as much as basic CDS • generate 12 times greater financial return • A potential reduction of more than 2 million adverse drug events (ADEs) annually Johnston et al., 2003
  6. 6. Serious Medication Error Rates Before and After CPOE Bates et. al. JAMA 1998.
  7. 7. CAD/DM Smart Form Smart View: Smart Smart Data Display Documentation Assessment, Orders, and Plan Assessment and recommendations generated from rules engine • Lipids • Anti-platelet therapy • Blood pressure • Glucose control • Microalbuminuria • Immunizations • Smoking • Weight • Eye and foot examinations
  8. 8. CAD/DM Smart Form Medication Orders Lab Orders Referrals Handouts/Education
  9. 9. Preliminary Results: Smart Form On Treatment Analysis Smart Form Used Control 0% 10% 20% 30% 40% 50% 60% 70% 80% Up-to-date BP result <0.001 Change in BP therapy if above goal 0.05 Up-to-date height and weight 0.004 Change in therapy if A1C above goal 0.006 Up-to-date foot exam documented <0.001 Up-to-date eye exam documented <0.001 # of deficiencies addressed <0.001
  10. 10. CAD Quality Dashboard Targets are 90th percentile for Red, yellow, and green indicators show HEDIS or for Partners providers adherence with targets Zero defect care: • Aspirin • Beta-blockers • Blood pressure • Lipids
  11. 11. Discrepancy Details
  12. 12. Patient Journal Causes Provider Activation More medication changes in visits after diabetes journal submission: Grant RW et al. Practice-linked Online Personal Health Records for Type 2 Diabetes: A Randomized Controlled Trial. Arch Intern Med. 2008 Sep 8;168(16):1776-82. .
  13. 13. A Roadmap for National Action on Clinical Decision Support “to ensure that optimal, usable and effective clinical decision support is widely available to providers, patients, and individuals where and when they need it to make health care decisions.” Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. J. Am. Med. Inform. Assoc. 2007;14(2):141-145.
  14. 14. CDS Consortium Goal To assess, define, demonstrate, and evaluate best practices for knowledge management and clinical decision support in healthcare information technology at scale – across multiple ambulatory care settings and EHR technology platforms.
  15. 15. Guideline Model Chronology Decision Tables GEM Arden GEODE-CM ONCOCIN EON(T-Helper) GLIF2 GLIF3 MBTA EON2 Asbru PRODIGY PRODIGY3 Oxford System DILEMMA PROforma of Medicine PRESTIGE 1980 1990 2000 P. L. Elkin, M. Peleg, R. Lacson, E. Bernstam, S. Tu, A. Boxwala, R. Greenes, & E. H. Shortliffe. Toward Standardization of Electronic Guidelines. MD Computing 17(6):39-44, 2000
  16. 16. CDSC Multilayered Knowledge Representation Machine Execution Abstract Representation Semistructured Recommendation Narrative Guideline Narrative Recommendation layer Semi-Structured Recommendation layer Narrative text of the recommendation from the published guideline. Abstract Representation layer Breaks down the text into various slots such as those for applicable Machine Executable layer Structures the recommendation for use inintervention, andCDS tools clinical scenario, the recommended particular kinds of evidence • basis for theencoded in a format that can be rapidly integrated into a Knowledge recommendation Reminder and alert rules Standard vocabularyspecific HIT platform • Order sets on a codes for data and more precise criteria CDS tool A(pseudocode) be encoded in Arden Syntax E.g., rule could could have several different artifacts created in this layer, recommendation Aone for each kind of CDS tool recommendation could have several different artifacts created in this layer, one for each of the different HIT platforms
  17. 17. Enterprise CDS Framework CDS Consumers ECRS O External to R Vendor Products PHS Metadata C H Query Rule Rule Execution Authoring E Action Server Internal, S Cache T Run Rules R A Controller T Rule DB O Vendor, nonCache R Patient Factory Supporting Services CCD CCD Pt Data Classification Translation Factory Access Services Services Patient Data Reference Data
  18. 18. An external repository of clinical content with web-based viewer Search Criteria Content Type… Specialty
  19. 19. Where are we? “I conclude that though the individual physician is not perfectible, the system of care is, and that the computer will play a major part in the perfection of future care systems.” Clem McDonald, MD NEJM 1976 Thank you! Blackford Middleton, MD