Development and evaluation of an ontology for guiding appropriate antibiotic prescribing

Authors:
Tiffani J. Bright;E. Yoko Furuya;Gilad J. Kuperman;James J. Cimino;Suzanne Bakken
Affiliations:
Duke University Medical Center, Division of Clinical Informatics, 2200 West Main St., Suite 600, Durham, NC 27710, United States;Columbia University, Department of Medicine, Division of Infectious Diseases, 630 West 168th St., Box 82, New York, NY 10032, United States;NewYork-Presbyterian Hospital, 525 East 68th St., Box 151, New York, NY 10065, United States and Columbia University, Department of Biomedical Informatics, 622 West 168th St., VC5, New York, NY 10 ...;NIH Clinical Center, Room 6-2551, 10 Center Drive, Bethesda, MD 20892, United States;Columbia University, Department of Biomedical Informatics, 622 West 168th St., VC5, New York, NY 10032, United States and Columbia University, School of Nursing, 617 West 168th St., Box 6, New Yor ...
Venue:
Journal of Biomedical Informatics
Year:
2012

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Abstract

Objectives: To develop and apply formal ontology creation methods to the domain of antimicrobial prescribing and to formally evaluate the resulting ontology through intrinsic and extrinsic evaluation studies. Methods: We extended existing ontology development methods to create the ontology and implemented the ontology using Protege-OWL. Correctness of the ontology was assessed using a set of ontology design principles and domain expert review via the laddering technique. We created three artifacts to support the extrinsic evaluation (set of prescribing rules, alerts and an ontology-driven alert module, and a patient database) and evaluated the usefulness of the ontology for performing knowledge management tasks to maintain the ontology and for generating alerts to guide antibiotic prescribing. Results: The ontology includes 199 classes, 10 properties, and 1636 description logic restrictions. Twenty-three Semantic Web Rule Language rules were written to generate three prescribing alerts: (1) antibiotic-microorganism mismatch alert; (2) medication-allergy alert; and (3) non-recommended empiric antibiotic therapy alert. The evaluation studies confirmed the correctness of the ontology, usefulness of the ontology for representing and maintaining antimicrobial treatment knowledge rules, and usefulness of the ontology for generating alerts to provide feedback to clinicians during antibiotic prescribing. Conclusions: This study contributes to the understanding of ontology development and evaluation methods and addresses one knowledge gap related to using ontologies as a clinical decision support system component-a need for formal ontology evaluation methods to measure their quality from the perspective of their intrinsic characteristics and their usefulness for specific tasks.