title: Outlier detection for patient monitoring and alerting.
abstract: We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25% to 66% for a variety of patient-management actions, with 66% corresponding to the strongest outliers.
authors.affiliation: Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA. milos@pitt.edu
authors.forename: Milos
authors.initials: M
authors.lastname: Hauskrecht
authors.forename: Iyad
authors.initials: I
authors.lastname: Batal
authors.forename: Michal
authors.initials: M
authors.lastname: Valko
authors.forename: Shyam
authors.initials: S
authors.lastname: Visweswaran
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
authors.forename: Gilles
authors.initials: G
authors.lastname: Clermont
mesh_terms: D006801:Humans; D016347:Medical Records Systems, Computerized; D008991:Monitoring, Physiologic / Q000379:methods*; D011184:Postoperative Period