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GEM & HL7

GEM & HL7. Richard N. Shiffman, MD, MCIS, Abha Agrawal, MD, Roland Chen, MD, Bryant Karras, MD, Luis Marenco, MD, Kristi Polvani, BS, Sujai Nath, MD Peter Gershkovich, MD, Aniruddha Deshpande, MD Yale Center for Medical Informatics. NOT!!. http://ycmi.med.yale.edu/GEM

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GEM & HL7

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  1. GEM & HL7 Richard N. Shiffman, MD, MCIS, Abha Agrawal, MD, Roland Chen, MD, Bryant Karras, MD, Luis Marenco, MD, Kristi Polvani, BS, Sujai Nath, MD Peter Gershkovich, MD, Aniruddha Deshpande, MD Yale Center for Medical Informatics

  2. NOT!!

  3. http://ycmi.med.yale.edu/GEM richard.shiffman@yale.edu

  4. Use And Satisfaction • 8 physicians—not members of the GEM development team (UNC, UAB, Hopkins, Yale) marked up a guideline • CONCLUSIONS: Subjects were able to model the content of the guideline using GEM elements. • “satisfactory” • Improved editing tools would facilitate translation • Result: GEM Cutter Karras, Proc AMIA 2001

  5. GEM-Q / GEM-Q OnLine • XSL stylesheet extracts info relevant to quality appraisal from GEMified gl • Pass to Shaneyfelt and Cluzeau instruments • Output is a quality report card • Valued by AAP in gl devel process • Available for ad hoc reports on WWW Agrawal, Medinfo 2002

  6. GEM to Arden • Relevant components for Arden extracted and used to pre-populate MLMs Agrawal, Proc AMIA 2002

  7. Implementation • GEMified document can dynamically generate data collection screens and trigger appropriate recommendations based on guideline logic • Proof of concept • Applied to NHLBI asthma guideline and CDC TB screening guideline Gershkovich, Proc AMIA 2002

  8. Knowledge extractor • XSL extracts and formats guideline info relevant to implementation

  9. In use • ~100 guidelines have been GEMified • Groups in US, UK, Germany, Italy, and NZ are using GEM

  10. Funded by NLM: • To improve the quality and implementability of an AAP guideline w/ feedback during development. (Using GEM-Q) • To create tools that transform GEM-encoded guidelines into CDSS. A generic process and software tools will be developed to translate GEM-encoded guidelines into systems that can improve the process of care. • To extend and refine the GEM model to serve as a precise, comprehensive, and consistently applied ontology of guideline-related concepts. (Logic, link, algorithm elements; application of advanced X-technologies)

  11. Logical Analysis with Highlighters • Recommendation 3 If an infant or young child 2 months to 2 years of age with unexplained fever is assessed as being sufficiently ill to warrant immediate antimicrobial therapy, a urine specimen should be obtained by SPA or bladder catheterization; the diagnosis of UTI cannot be established by a culture of urine collected in a bag. (Strength of evidence: good) Urine obtained by SPA or urethral catheterization is unlikely to be contaminated...

  12. UTI Recommendation in XML <decision.variable id=dv1>age</decision.variable> <value>2 months to 2 years</value> <decision.variable id= dv2>unexplained fever</decision.variable> <decision.variable id=dv3>sufficiently ill to warrant immediate antimicrobial therapy</decision.variable> <action id=a1>obtain urine specimen by SPA</action> <action id=a2>obtain urine specimen by catheterization</action> <reason>the diagnosis of UTI cannot be established by a culture of urine collected in a bag</reason> <evidence.quality>Good</evidence.quality> <logic>IF (dv1=2m-2y) AND dv2 AND dv3 THEN a1 OR a2</logic> <link>Diagnosis section</link> <link>after:Recommendation 2</link>

  13. Adding guideline meta-information • Operationalizing abstract constructs • Determining when to collect data, when to deliver advice (site-specific) Sufficiently ill to warrant immediate antimicrobial therapy or Febrile Interactive Tolerating oral fluids

  14. Whither GEM in HL7 • GEM users asking why HL7 is creating a new architecture

  15. HS INF Title Citation Identity Release Date Availability Contact Status Companion Document Adaptation Developer Name Developer Committee Name Funding Endorser Comparable Guideline Health Practices Category Purpose Target Population Rationale Objective Available Options Implementation Strategy Health Outcomes Audience Exceptions Care Setting Clinician Users Evidence Collection Evidence Time Period Method Evidence Grading Combining Evidence Specification of Harm/Benefit Quantification of Harm/Benefit Value Judgment Patient Preference Qualifying Statement Cost Analysis Recommendation Conditional (decision variable) . Action . Logic . Knowledge Reason . Strength of Recommendation . Evidence Quality . . . Cost . Certainty . Testing Algorithm Eligibility Definition External Review Revision Pilot Testing Expiration Date Scheduled Review

  16. GEM: Distinguishing Characteristics • Conceived and built in XML • Multi-platform • Open standard • Human-readable yet can be processed by machine • DTD/schema allows file validation • Markup can be performed by non-programmers • … • GEM passed balloting as a standard (ASTM E2210-02)

  17. Goals • Comprehensive – capable of expressing all the knowledge contained in guidelines. Health service models cannot express recommendations in sufficient detail; informatics models inadequate to model constructs that express and support guideline validity

  18. Goal 2 • Expressively adequate to convey the complexities and nuances of clinical medicine while remaining informationally equivalent to the original guideline; tagged elements store actual language

  19. Goal 3 • Flexible – must be able to deal with variety and complexity of guidelines; permit modeling at high and low levels of granularity

  20. Goal 4 • Comprehensible – the model should match the stakeholder’s normal problem-solving language and allow domain experts to describe their knowledge with little effort; markup should not require a background as a programmer

  21. Goal 5 • Shareable across institutions

  22. Goal 6 • Reusable - across all phases of the guideline lifecycle

  23. GEM: Major Components Guideline Document Header Document Body Identity Developer Purpose Method of Development Testing Revision Plan Intended Audience Target Population Knowledge Components

  24. Unit of implementability Revision Plan Identity Developer Purpose Method of Dev Intended User Target Popul’n Pilot Testing Recommendation

  25. Title Citation Release Date Availability Status Companion Document Adaptation Length Electronic Print Contact Patient Resource Identity Identity

  26. Developer Developer Developer Name Committee Name Funding Endorser Comparable Guideline Developer Type Committee Expertise Committee Member Member Expertise NGC Controlled Vocabulary

  27. Purpose Purpose Main Focus Category Rationale Objective Available Option Implem’n Strategy Health Outcome Exception

  28. Intended Audience Intended Audience User Care Setting Clinical Specialty Professional Group

  29. Method of Development Method of Development Descrip’n Evidence Collection Evid Time Period Method Evidence Grading Descrip’n Evidence Combinat’n Cost Anal Spec’n Harm Benefit Quant Harm Benefit Role Value Judgmt Role Pt Pref Qualifying Statement Method Evid Collect Number Source Docs Rating Scheme Method Evidence Combinat’n

  30. Target Population Target Population Eligibility Age Sex Inclusion Criterion Exclusion Criterion

  31. External Review Pilot Testing Review Method Testing Testing

  32. Expiration Scheduled Review Revision Plan Revision Plan

  33. Knowledge Components Knowledge Components Algorithm Recommendation Definition Term Action Step Condit’l Step Branch Step Sync Step Conditional Imperative Term Meaning

  34. Conditional Knowledge Components Recommendation Conditional Dec Var Action Reason Evid Quality Recmdn Strength Flexblty Link Ref Certainty Logic Cost Value Dec Var Cost Action Benefit ActionRisk Harm Action Descripn Action Cost Dec Variable Descripn Test Param Sensitivity Specificity Predictive Value

  35. Conditional Knowledge Components Recommendation Conditional Dec Var Action Reason Evid Quality Recmdn Strength Flexblty Link Ref Certainty Logic Cost Value Dec Var Cost Action Benefit ActionRisk Harm Action Descripn Action Cost Dec Variable Descripn Test Param Sensitivity Specificity Predictive Value What How Much Where When Who

  36. Actions

  37. GEM Cutter

  38. Knowledge customization • Add meta-information necessary for implementation, e.g. • Identifier, clinical source, interface, prompt, mechanism of actions • Local adaptation • Translation of national recommendations into systems that operate at a local level • Must account for legitimate variations in clinical settings, populations served, and resources available • Danger: protection of professional habit or economic self-interest

  39. Strengths of GEM • Hierarchy is relatively intuitive • Elements are derived from published models • Value-added applications have been developed • Stable >1 year • Designation as a standard

  40. GEM Limitations • Has been “frozen” > 1 year • Not comprehensive (as demonstrated by CPGA) • Need guidelines for extension • GEM file only as good as guideline document • Requires training to use correctly • Need to develop <link>, <logic>, and <algorithm> elements

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