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Biomedical subjects

P Gershkovich

Publications and source records attributed to P Gershkovich.

2 recordsLinked to original sources

An approach to guideline implementation with GEM.

Implementation of practice guidelines refers to the creation of strategies and systems to operationalize the knowledge and recommendations set forth by guideline developers. We describe an approach to guideline implementation that makes direct use of the guideline document as a knowledge base. The Guideline Elements Model (GEM) provides an XML-based guideline document model that facilitates implementation of guidelines. Knowledge extraction using GEM requires document markup rather than programming and can promote authenticity and consistent knowledge encoding. Knowledge customization for the local enterprise requires addition of meta-information to pertinent components of the GEM hierarchy in a design database. GEM provides an audit trail to track local adaptation. Knowledge integration with patient data can be promoted using information management services. A design goal is to devise a system that can be applied by local clinical domain experts, quality assurance experts, and information systems programmers without requiring trained informaticians and knowledge engineers to serve as intermediaries

Artificial Intelligence↗

An implementation framework for GEM encoded guidelines.

Access to timely decision support information is critical for delivery of high-quality medical care. Transformation of clinical knowledge that is originally expressed in the form of a guideline to a computable format is one of the main obstacles to the integration of knowledge sharing functionality into computerized clinical systems. The Guideline Element Model (GEM) provides a methodology for such a transformation. Although the model has been used to store heterogeneous guideline knowledge, it is important to demonstrate that GEM markup facilitates guideline implementation. This report demonstrates the feasibility of implementation of GEM-encoded guideline recommendations using Apache Group s Cocoon Web Publishing Framework. We further demonstrate how XML-based programming allows for maintaining the separation of guideline content from processing logic and from presentation format. Finally, we analyze whether the guideline authors original intent has been sufficiently captured and conveyed to the end user.

Artificial Intelligence↗