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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; - PowerPoint PPT Presentation
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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
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
Guideline Sharing: the GLIF approach
Database of CIGs Encoded in GLIF
Central Serverto Support
Browsing andDownloading
of CIGsTools for Encoding CIGs, Validating, &
Testing them
Internet
Local Adaptation of CIG
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: 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
Guideline encoding and adaptation
NarrativeGuideline encoding
Abstract flowchart in GLIF3
informaticians
GLIF3’ guideline process model (Diabetes)
Created using Protégé-2000
Hierarchical model
Guideline encoding and adaptation
NarrativeGuideline encoding
Abstract flowchart in GLIF3
Analysis of Local Practice
informaticians
Informatician+Experts
Needed changes+Concept defs
Encoding Revision& Formalization
Local CIGMapped to EMR
Hierarchical model
Computable specification
Note the different naming
conventions
Guideline encoding and adaptation
NarrativeGuideline encoding
Abstract flowchart in GLIF3
Analysis of Local Practice
informaticians
Informatician+Experts
Needed changes+Concept defs
Encoding Revision& Formalization
Local CIGMapped to EMR
ManualValidation
Validation by
Execution of test-cases
Iterative
changes
changes
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!
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