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...

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Veröffentlicht in:Proceedings. AMIA Symposium. - 1998. - (2001) vom: 11., Seite 7-11
1. Verfasser: Agrawal, A (VerfasserIn)
Weitere Verfasser: Shiffman, R N
Format: Aufsatz
Sprache:English
Veröffentlicht: 2001
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.
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245 1 0 |a Using GEM-encoded guidelines to generate medical logic modules 
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520 |a 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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650 4 |a Research Support, U.S. Gov't, P.H.S. 
700 1 |a Shiffman, R N  |e verfasserin  |4 aut 
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