title: Identifying Deviations from Usual Medical Care using a Statistical Approach.
abstract: Developing methods to detect deviations from usual medical care may be useful in the development of automated clinical alerting systems to alert clinicians to treatment choices that warrant additional consideration. We developed a method for identifying deviations in medication administration in the intensive care unit that is based on learning logistic regression models from past patient data that when applied to current patient data identifies statistically unusual treatment decisions. The models predicted a total of 53 deviations for 6 medications on a set of 3000 patient cases. A set of 12 predicted deviations and 12 non-deviations was evaluated by a group of intensive care physicians. Overall, the predicted deviations were assessed to often warrant an alert and to be clinically useful, and furthermore, the frequency with which such alerts would be raised is not likely to be disruptive in a clinical setting.
authors.affiliation: Department of Biomedical Informatics.
authors.forename: Shyam
authors.initials: S
authors.lastname: Visweswaran
authors.forename: James
authors.initials: J
authors.lastname: Mezger
authors.forename: Gilles
authors.initials: G
authors.lastname: Clermont
authors.forename: Milos
authors.initials: M
authors.lastname: Hauskrecht
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
mesh_terms: D003422:Critical Care; D006801:Humans; D007362:Intensive Care Units; D016015:Logistic Models; D050316:Medical Order Entry Systems; D008508:Medication Errors; D010820:Physicians