Modeling data and knowledge in the EON guideline architecture.
Compared to guideline representation formalisms, data and knowledge modeling for clinical guidelines is a relatively neglected area. Yet it has enormous impact on the format and expressiveness of decision criteria that can be written, on the inferences that can be made from patient data, on the ease with which guidelines can be formalized, and on the method of integrating guideline-based decision-support services into implementation sites' information systems. We clarify the respective roles that data and knowledge modeling play in providing patient-specific decision support based on clinical guidelines. We show, in the context of the EON guideline architecture, how we use the Protégé-2000 knowledge-engineering environment to build (1) a patient-data information model, (2) a medical-specialty model, and (3) a guideline model that formalizes the knowledge needed to generate recommendations regarding clinical decisions and actions. We show how the use of such models allows development of alternative decision-criteria languages and allows systematic mapping of the data required for guideline execution from patient data contained in electronic medical record systems.
Tu
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SW
Stanford Medical Informatics, Stanford University School of Medicine, Stanford, California 94305-5479, USA. tu@smi.stanford.edu
Musen
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MA
D001185:Artificial Intelligence; D003658:Decision Making, Computer-Assisted; D005103:Expert Systems; D006801:Humans; D008498:Medical Record Linkage / Q000379:methods; D016347:Medical Records Systems, Computerized; D008962:Models, Theoretical; D017410:Practice Guidelines as Topic; D012984:Software