title: Using a Bayesian belief network model to categorize length of stay for radical prostatectomy patients.
abstract: A clinical pathway implements best medical practices and represents sequencing and timing of interventions by clinicians for a particular clinical presentation. We used a Bayesian belief network (BBN) to model a clinical pathway for radical prostatectomy and to categorize patient's length of stay (LOS) as being met or delayed given the patient's outcomes and activities. A BBN model constructed from historical data collected as part of a retrospective chart study represents probabilistic dependencies between specific events from the pathway and identifies events directly affecting LOS. Preliminary evaluation of a BBN model on an independent test sample of patients' data shows that model reliably categorizes LOS for the second and third day after the surgery (with overall accuracy of 82 and 84%, respectively).
authors.affiliation: School of Management, University of Ottawa, 136 Jean-Jacques Lussier St., P.O. Box 450, Stn. A, Ottawa K1N 6N5, Canada. wojtek@management.uottawa.ca
authors.forename: Wojtek
authors.initials: W
authors.lastname: Michalowski
authors.forename: Szymon
authors.initials: S
authors.lastname: Wilk
authors.forename: Anthony
authors.initials: A
authors.lastname: Thijssen
authors.forename: Mingmei
authors.initials: M
authors.lastname: Li
mesh_terms: D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D006801:Humans; D007902:Length of Stay; D008297:Male; D008485:Medical Audit; D011468:Prostatectomy; D012189:Retrospective Studies