title: Using GEM-encoded guidelines to generate medical logic modules.
abstract: 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.
authors.affiliation: Yale Center for Medical Informatics, New Haven, CT, USA.
authors.forename: A
authors.initials: A
authors.lastname: Agrawal
authors.forename: R N
authors.initials: RN
authors.lastname: Shiffman
mesh_terms: D003658:Decision Making, Computer-Assisted; D017410:Practice Guidelines as Topic; D011381:Programming Languages; D012984:Software