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AbstractObjectiveAntibiotic computerized decision support systems (CDSSs) were developed to guide antibiotic decisions, yet prescriptions of CDSS-recommended antibiotics have remained low. Our aim was to identify predictors of patients' receipt of empiric antibiotic therapies recommended by a CDSS when the prescribing physician had an initial preference for using broad-spectrum antibiotics.MethodsWe conducted a prospective cohort study in a 1 500-bed tertiary-care hospital in Singapore. We included all patients admitted from October 1, 2011 through September 30, 2012, who were prescribed piperacillin-tazobactam or carbapenem for empiric therapy and auto-triggered to receive antibiotic recommendations by the in-house antibiotic CDSS. Relevant data on the patient, prescribing and attending physicians were collected via electronic linkages of medical records and administrative databases. To account for clustering, we used multilevel logistic regression models to explore factors associated with receipt of CDSS-recommended antibiotic therapy.ResultsOne-quarter of the 1 886 patients received CDSS-recommended antibiotics. More patients treated for pneumonia (33.2%) than sepsis (12.1%) and urinary tract infections (7.1%) received CDSS-recommended antibiotic therapies. The prescribing physician – but not the attending physician or clinical specialty – accounted for some (13.3%) of the variation. Prior hospitalization (odds ratio [OR] 1.32, 95% CI, 1.01-1.71), presumed pneumonia (OR 6.77, 95% CI, 3.28-13.99), intensive care unit (ICU) admission (OR 0.38, 95% CI, 0.21-0.66), and renal impairment (OR 0.70, 95% CI, 0.52-0.93) were factors associated with patients’ receipt of CDSS-recommended antibiotic therapies.ConclusionsWe observed that ICU admission and renal impairment were negative predictors of patients’ receipt of CDSS-recommended antibiotic therapies. Patients admitted to ICU and those with renal impairment might have more complex clinical conditions that require a physician's assessment in addition to antibiotic CDSS.
Journal of the American Medical Informatics Association – Oxford University Press
Published: Apr 1, 2016
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