Using GEM-encoded guidelines to generate medical logic modules
Among the most effective strategies for changing the process and outcomes of clinical care are those that make use of computer-mediated decision support. A variety of representation models that facilitate computer-based implementation of medical knowledge have been published, including the Guideline...
| Veröffentlicht in: | Proceedings. AMIA Symposium. - 1998. - (2001) vom: 11., Seite 7-11 |
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| Format: | Aufsatz |
| Sprache: | English |
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2001
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| Zugriff auf das übergeordnete Werk: | Proceedings. AMIA Symposium |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. |
| Zusammenfassung: | Among the most effective strategies for changing the process and outcomes of clinical care are those that make use of computer-mediated decision support. A variety of representation models that facilitate computer-based implementation of medical knowledge have been published, including the Guideline Elements Model (GEM) and the Arden Syntax for Medical Logic Modules (MLMs). We describe an XML-based application that facilitates automated generation of partially populated MLMs from GEM-encoded guidelines. These MLMs can be further edited and shared among Arden-compliant information systems to provide decision support. Our work required three steps: (a) Knowledge extraction from published guideline documents using GEM, (b) Mapping GEM elements to the MLM slots, and (c) XSL transformation of the GEM-encoded guideline. Processing of a sample guideline generated 15 MLMs, each corresponding to a conditional or imperative element in the GEM structure. Mechanisms for linking various MLMs are necessary to represent the complexity of logic typical of a guideline |
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| Beschreibung: | Date Completed 24.05.2002 Date Revised 13.11.2018 published: Print Citation Status MEDLINE |
| ISSN: | 1531-605X |