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Eric Mays

Publications and source records attributed to Eric Mays.

2 recordsLinked to original sources

Modeling guidelines for integration into clinical workflow.

The success of clinical decision-support systems requires that they are seamlessly integrated into clinical workflow. In the SAGE project, which aims to create the technological infra-structure for implementing computable clinical practice guide-lines in enterprise settings, we created a deployment-driven methodology for developing guideline knowledge bases. It involves (1) identification of usage scenarios of guideline-based care in clinical workflow, (2) distillation and disambiguation of guideline knowledge relevant to these usage scenarios, (3) formalization of data elements and vocabulary used in the guideline, and (4) encoding of usage scenarios and guideline knowledge using an executable guideline model. This methodology makes explicit the points in the care process where guideline-based decision aids are appropriate and the roles of clinicians for whom the guideline-based assistance is intended. We have evaluated the methodology by simulating the deployment of an immunization guideline in a real clinical information system and by reconstructing the workflow context of a deployed decision-support system for guideline-based care. We discuss the implication of deployment-driven guideline encoding for sharability of executable guidelines.

Decision Making, Computer-Assisted↗

Role grouping as an extension to the description logic of Ontylog, motivated by concept modeling in SNOMED.

Several clinical terminologies now utilize description logic to model the logical definitions of concepts. Recent editions of the Systematized Nomenclature of Medicine (SNOMED) have been developed using the description logic Ontylog. A significant design criterion for SNOMED is to keep concept expressions simple enough to be broadly usable by clinicians, while maintaining faithful representation of concept meaning. Motivated by this criterion, "role grouping" has been developed as an extension to the description logic Ontylog. This paper describes the problems that motivated the creation of role grouping, outlines the semantics of role grouping, illustrates the benefits of this construct with examples from SNOMED Clinical Terms, and provides an algorithm for determining normal forms for expressions involving role groups.

Algorithms↗