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- W44437510 abstract "Hospital-acquired infections (HAIs) and the selection of empiric antibiotics continue to create problems for physicians. We developed computerized methods to identify patients at high risk of acquiring HAIs. Data from 3,151 patients with HAIs were compared with 3,152 control patients. Logistic regression was used to create a model to predict HAIs. Computer programs now monitor hospitalized patients every day and infection control personnel are notified of high risk patients before the onset of infections. During a six month study, between 60 and 70 percent of patients with HAIs were identified before infection onset. We used similar methods to identify patient factors that can be used to predict the pathogens and to help select empiric antibiotics for individual patients. A computerized antibiotic assistant program was developed to be used by physicians both inside and outside the hospital. The computer program utilizes five years of microbiology data (over 13,500 cultures) and a knowledge base containing empiric logic from infectious disease experts to help physicians select antibiotics. The computer predicted a susceptible antibiotic in 238 of 250 (95%) patients with infections." @default.
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- W44437510 date "1990-11-07" @default.
- W44437510 modified "2023-09-26" @default.
- W44437510 title "Large-Scale Clinical Systems. Impact of Large-Scale Systems on Clinical Practice: Prediction of Hospital Infections and Selection of Antibiotics Using an Automated Hospital Database" @default.
- W44437510 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2245512" @default.
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