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Comparing Computer-interpretable Guideline Models: A Case-study Approach

Affiliations of the authors: Stanford Medical Informatics, Stanford University School of Medicine, Stanford, California (MP, ST); Advanced Computation Laboratory Cancer Research UK, London (JB, JF); Medical Informatics Laboratory, Department of Computers and Systems Science, University of Pavia, Pavia, Italy (PC, AK, SQ, MS); Decision Systems Group, Harvard Medical School, Brigham and Women’s Hospital, Boston, Massachusetts (RAG); Sowerby Centre for Health Informatics at Newcastle, University of Newcastle, Newcastle upon Tyne, United Kingdom (RH, PDJ, NJ); Institute of Software Technology and Interactive Systems, Vienna University of Technology, Vienna, Austria (SM, AS); Department of Medical Informatics, Columbia University, New York (EHS).

Correspondence and reprints: Mor Peleg, PhD, Stanford Medical Informatics, Medical School Office Building X-208, 251 Campus Drive, Stanford, CA 94305-5479; e-mail: <peleg{at}smi.stanford.edu>.

Abstract Objectives: Many groups are developing computer-interpretable clinical guidelines (CIGs) for use during clinical encounters. CIGs use "Task-Network Models" for representation but differ in their approaches to addressing particular modeling challenges. We have studied similarities and differences between CIGs in order to identify issues that must be resolved before a consensus on a set of common components can be developed.

Design: We compared six models: Asbru, EON, GLIF, GUIDE, PRODIGY, and PROforma. Collaborators from groups that created these models represented, in their own formalisms, portions of two guidelines: the American College of Physicians–American Society of Internal Medicine’s guideline for managing chronic cough and the Sixth Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure.

Measurements: We compared the models according to eight components that capture the structure of CIGs. The components enable modelers to encode guidelines as plans that organize decision and action tasks in networks. They also enable the encoded guidelines to be linked with patient data—a key requirement for enabling patient-specific decision support.

Results: We found consensus on many components, including plan organization, expression language, conceptual medical record model, medical concept model, and data abstractions. Differences were most apparent in underlying decision models, goal representation, use of scenarios, and structured medical actions.

Conclusion: We identified guideline components that the CIG community could adopt as standards. Some of the participants are pursuing standardization of these components under the auspices of HL7.

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