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!

Peleg.mor@gmail.com

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