Adaptation of Practice Guidelines for Clinical Decision Support: A Case Study of Diabetic Foot Care

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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

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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

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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
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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
  

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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

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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

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Guideline encoding and adaptation
Narrative Guideline
encoding
Abstract flowchart in GLIF3
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GLIF3 guideline process model (Diabetes)

• Created using Protégé-2000

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Hierarchical model
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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
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Hierarchical model
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Computable specification
Note the different naming conventions
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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
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GLIF Execution Engine
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(No Transcript)
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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

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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 dont 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)

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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.,
  agt3 vs. a_gt_3.4)

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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

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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

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• Thanks!
• Peleg.mor_at_gmail.com

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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