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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.

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Presentation on theme: "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."— Presentation transcript:

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!!

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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 age 2 months to 2 years unexplained fever sufficiently ill to warrant immediate antimicrobial therapy obtain urine specimen by SPA obtain urine specimen by catheterization the diagnosis of UTI cannot be established by a culture of urine collected in a bag Good IF (dv1=2m-2y) AND dv2 AND dv3 THEN a1 OR a2 after: Recommendation 2 Diagnosis section

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

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

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16 Title Citation Release Date Availability Contact Status Companion Document Adaptation Developer Name Committee Name Funding Endorser Comparable Guideline Health Practices Category Target Population Rationale Objective Available Options Implementation Strategy Health Outcomes Exceptions Care Setting Clinician Users Evidence Collection Evidence Time Period 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. Reason. Strength of Recommendation. Evidence Quality... Cost. Certainty. Algorithm Eligibility Definition External Review Pilot Testing Expiration Date Scheduled Review Developer Purpose Method Knowledge Audience Identity Testing Revision HS INF

17 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 E )

18 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

19 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

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

21 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

22 Goal 5 Shareable across institutions

23 Goal 6 Reusable - across all phases of the guideline lifecycle

24 GEM: Major Components Guideline Identity Purpose Intended Audience Method of Development Knowledge Components TestingRevision Plan Target Population Document Header Document Body Developer

25 Unit of implementability IdentityDeveloperTarget Popul’n Purpose Method of Dev Revision Plan Pilot TestingIntended User Recommendation

26 Identity TitleCitation Release Date AvailabilityStatus Companion Document Adaptation LengthElectronicPrintContact Patient Resource Identity

27 Developer Name Committee Name FundingEndorser Comparable Guideline Developer Type Committee Member Expertise Committee Expertise Developer NGC Controlled Vocabulary

28 Purpose Main FocusCategoryRationaleObjective Available Option Implem’n Strategy Health Outcome Exception Purpose

29 Intended Audience User Care Setting Clinical Specialty Professional Group Intended Audience

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

31 Target Population EligibilityAge Inclusion Criterion Exclusion Criterion Sex Target Population

32 Testing External Review Pilot Testing Review Method Testing

33 Revision Plan ExpirationScheduled Review Revision Plan

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

35 Conditional Value Dec Var ActionFlexbltyReasonEvid Quality Recmdn Strength LogicCost Link Dec Variable Descripn Test Param Dec Var Cost SensitivitySpecificityPredictive Value Action Benefit Action Risk Harm Action Descripn Action Cost Ref Certainty Recommendation Conditional Knowledge Components

36 Conditional Value Dec Var ActionFlexbltyReasonEvid Quality Recmdn Strength LogicCost Link Dec Variable Descripn Test Param Dec Var Cost SensitivitySpecificityPredictive Value Action Benefit Action Risk Harm Action Descripn Action Cost RefCertainty Recommendation Conditional Knowledge Components What How Much Where When Who

37 Actions WhatHow (much) WhereWhenWho Medication Lab test Procedure Consultation Pt Education Disposition

38 GEM Cutter

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40 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

41 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

42 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,, and elements


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