Design and implementation of the GLIF3 guideline execution engine

  • Authors:

  • Dongwen Wang;Mor Peleg;Samson W. Tu;Aziz A. Boxwala;Omolola Ogunyemi;Qing Zeng;Robert A. Greenes;Vimla L. Patel;Edward H. Shortliffe

  • Affiliations:

  • Department of Biomedical Informatics, Columbia University, New York, NY;Department of Management Information Systems, University of Haifa, Haifa 31905, Israel and Stanford Medical Informatics, Stanford University, Stanford, CA;Stanford Medical Informatics, Stanford University, Stanford, CA;Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA and Eclipsys Corporation, Boston, MA;Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA;Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA;Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA;Department of Biomedical Informatics, Columbia University, New York, NY;Department of Biomedical Informatics, Columbia University, New York, NY

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2004

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Abstract

We have developed the GLIF3 Guideline Execution Engine (GLEE) as a tool for executing guidelines encoded in the GLIF3 format. In addition to serving as an interface to the GLIF3 guideline representation model to support the specified functions, GLEE provides defined interfaces to electronic medical records (EMRs) and other clinical applications to facilitate its integration with the clinical information system at a local institution. The execution model of GLEE takes the "system suggests, user controls" approach. A tracing system is used to record an individual patient's state when a guideline is applied to that patient. GLEE can also support an event-driven execution model once it is linked to the clinical event monitor in a local environment. Evaluation has shown that GLEE can be used effectively for proper execution of guidelines encoded in the GLIF3 format. When using it to execute each guideline in the evaluation, GLEE's performance duplicated that of the reference systems implementing the same guideline but taking different approaches. The execution flexibility and generality provided by GLEE, and its integration with a local environment, need to be further evaluated in clinical settings. Integration of GLEE with a specific event-monitoring and order-entry environment is the next step of our work to demonstrate its use for clinical decision support. Potential uses of GLEE also include quality assurance, guideline development, and medical education.