New computer-based tools for empiric antibiotic decision support.
Since 1995 we have been developing a decision-support model, called Q-ID, which uses a series of infectious disease knowledge bases to make recommendations for empirical treatment or to check the appropriateness of current antibiotic therapy. From disease manifestations and risk factors, a differential diagnosis for the patient is generated by a diagnostic medical expert system. The resulting probability of each: disease is multiplied by the expected benefit in improved mortality and morbidity from optimal antibiotic treatment of each disease. To generate empirical treatment recommendations, site-specific data on sensitivity to antibiotics of each organism is used as an estimate of the likelihood of achieving maximum benefit for each disease on the patient's differential. Combining this data with drug and patient specific factors, the model recommends the antibiotic(s) most likely to produce the optimal benefit in this patient with the least risk and expense. In this paper the model is described, excerpts from each of the knowledge bases are presented, and performance of the model in a real case is shown for illustration.
Warner
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Division of Infectious Disease, University of Utah Medical Center, Sunquest Information Systems, Inc., Salt Lake City, USA.
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Sorenson
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Reimer
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Li
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Nelson
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Barton
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Warner
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D000368:Aged; D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted; D004360:Drug Therapy, Computer-Assisted; D006801:Humans; D008297:Male; D014552:Urinary Tract Infections / Q000188:drug therapy
D000900:Anti-Bacterial Agents