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Mor Peleg 1 , Dongwen Wang 2 , Adriana Fodor 3 , Sagi Keren 4 and Eddy Karnieli 3

Adaptation of Practice Guidelines for Clinical Decision Support: A Case Study of Diabetic Foot Care. Mor Peleg 1 , Dongwen Wang 2 , Adriana Fodor 3 , Sagi Keren 4 and Eddy Karnieli 3 1 Department of Management Information systems, University of Haifa, Israel;

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Mor Peleg 1 , Dongwen Wang 2 , Adriana Fodor 3 , Sagi Keren 4 and Eddy Karnieli 3

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  1. Adaptation of Practice Guidelines for Clinical Decision Support: A Case Study of Diabetic Foot Care Mor Peleg1, Dongwen Wang2, Adriana Fodor3, Sagi Keren4 and Eddy Karnieli3 1Department of Management Information systems, University of Haifa, Israel; 2Department of Biomedical Informatics, Columbia University, NY 3Inst. of Endocrinology, Diabetes & Metabolism, Rambam Medical Center, and RB. Faculty of Medicine, Technion 4Department of Computer Science, University of Haifa, Israel

  2. What are clinical guidelines? • A recommended strategy for management of a medical problem in order to • Improve outcomes • Reduce practice variation • Reduce inappropriate use of resources • Computer-interpretable Guidelines can deliver patient-specific advice during encounters • GLIF3 is a CIG formalism dev. by InterMed

  3. Guideline Sharing: the GLIF approach Internet Central Server to Support Browsing and Downloading of CIGs Database of CIGs Encoded in GLIF Tools for Encoding CIGs, Validating, & Testing them Integration with Local Application (e.g., EPR, order-entry system, Other decision-support system) Local Adaptation of CIG

  4. Reasons for Local Adaptation/changes • Variations among settings due to • Institution type (hospital vs. physician office) • Location (e.g., urban vs. rural) • Availability of resources • Dissimilarity of patient population (prevalence) • Local policies • Practice patterns • Consideration of EMR schema and data availability

  5. Research purpose • Characterize a tool-supported process of encoding guidelines as DSSs that supports local adaptation and EMR integration • Identify and classify the types of changes in guideline encoding during a local adaptation process

  6. Methods • Guideline: Diabetes foot care • By the American College of Foot and Ankle Surgeons • Guideline encoding language: GLIF3 • Authoring tool: Protégé-2000 • Guideline execution/simulation tool: GLEE • EMR: Web-based interface to an Oracle DB • Analysis of changes that have been made during the encoding and adaptation process

  7. informaticians Guideline encoding and adaptation Narrative Guideline encoding Abstract flowchart in GLIF3

  8. GLIF3’ guideline process model (Diabetes) Created using Protégé-2000

  9. Hierarchical model

  10. informaticians Informatician+ Experts Guideline encoding and adaptation Narrative Guideline encoding Analysis of Local Practice Abstract flowchart in GLIF3 Needed changes+ Concept defs Encoding Revision & Formalization Local CIG Mapped to EMR

  11. Hierarchical model

  12. Computable specification Note the different naming conventions

  13. informaticians Informatician+ Experts Iterative Guideline encoding and adaptation Narrative Guideline encoding Analysis of Local Practice Abstract flowchart in GLIF3 Needed changes+ Concept defs Encoding Revision & Formalization Manual Validation changes Local CIG Mapped to EMR Validation by Execution of test-cases changes

  14. GLIF Execution Engine

  15. Validation using GLEE • Executed: • 14 real patient cases from the EMR • 6 simulated cases, which covered all paths through the algorithm • The validation considered 22 branching points and recommendations • At the end of the validation, all 22 criteria matched with the expected results

  16. Types of changes made • Defining concepts • 2 of 10 concepts not defined in original GL • 6 definitions restated according to available data • Adjusting to local setting • GPs don’t give parenteral antibiotics (4 changes) • Defining workflow • Two courses of antibiotics may be given (4) • Matching with local practice • e.g. EMG should be ordered (4)

  17. The EMR schema & data availability affected encoding of decision criteria • Multiple guideline concepts mapped to 1 EMR data item (e.g., abscess & fluctuance) • A single guideline concept mapped to multiple EMR data (e.g., “ulcer present”) • Guideline concepts were not always available in the EMR schema(restate decision criteria) • Unavailable data(e.g., “ulcer present”) • Mismatches in data types and normal ranges (e.g., a>3 vs. “a_gt_3.4”)

  18. Summary • We suggest a tool-supported process for encoding a narrative guideline as a DSS in a local institution • We analyzed changes made throughout this process

  19. Discussion • Encoding by informatician was done before consulting clinicians re: localization • Presenting an abstract flowchart to them eases communication • But involving clinicians early saves time • Ongoing work: • Integration of the decision support functions within the web-based interface to the EMR • a mapping ontology that would allow encoding the guideline in GLIF through clinical abstractions and mapping to the actual EMR tables

  20. Thanks! Peleg.mor@gmail.com

  21. Changes made during encoding Versions Knowledge Item Original V1 V2 V3 Decision steps 23 13 13 21 Action steps 84 60 60 60 Decision criteria 9 52 35 50 Data items 15 73 66 150

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