Adaptation of Practice Guidelines for Clinical Decision Support

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; 2 Department of Biomedical Informatics, Columbia University, NY 3 Inst. of Endocrinology, Diabetes & Metabolism, Rambam Medical Center, and RB. Faculty of Medicine, Technion 4 Department of Computer Science, University of Haifa, Israel

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 • GLIF 3 is a CIG formalism dev. by Inter. Med

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

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

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

Methods • Guideline: Diabetes foot care – By the American College of Foot and Ankle Surgeons • • • Guideline encoding language: GLIF 3 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

Guideline encoding and adaptation Narrative Guideline encoding Abstract flowchart in GLIF 3 informaticians

GLIF 3’ guideline process model (Diabetes) Created using Protégé-2000

Hierarchical model

Guideline encoding and adaptation Narrative Guideline encoding Abstract flowchart in GLIF 3 informaticians Analysis of Local Practice Needed changes+ Concept defs Local CIG Mapped to EMR Informatician+ Experts Encoding Revision & Formalization

Hierarchical model

Computable specification Note the different naming conventions

Guideline encoding and adaptation Narrative Guideline encoding Abstract flowchart in GLIF 3 informaticians Analysis of Local Practice Needed changes+ Concept defs Local CIG Mapped to EMR Informatician+ Experts Encoding Revision & Formalization changes Iterative Manual Validation chan ges Validation by Execution of test-cases

GLIF Execution Engine

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

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)

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”)

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

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

Thanks! Peleg. mor@gmail. com

Changes made during encoding Knowledge Item Decision steps Action steps Decision criteria Data items Original 23 84 9 15 Versions V 1 13 60 52 73 V 2 13 60 35 66 V 3 21 60 50 150