title: Surgical Duration Estimation via Data Mining and Predictive Modeling: A Case Study.
abstract: Operating rooms (ORs) are one of the most expensive and profitable resources within a hospital system. OR managers strive to utilize these resources in the best possible manner. Traditionally, surgery durations are estimated using a moving average adjusted by the scheduler (adjusted system prediction or ASP). Other methods based on distributions, regression and data mining have also been proposed. To overcome difficulties with numerous procedure types and lack of sufficient sample size, and avoid distributional assumptions, the main objective is to develop a hybrid method of duration prediction and demonstrate using a case study.
authors.affiliation: Health Care Policy & Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic.
authors.forename: N
authors.initials: N
authors.lastname: Hosseini
authors.affiliation: Health Care Policy & Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic.
authors.forename: M Y
authors.initials: MY
authors.lastname: Sir
authors.affiliation: Department of Anesthesiology, Mayo Clinic.
authors.forename: C J
authors.initials: CJ
authors.lastname: Jankowski
authors.affiliation: Health Care Policy & Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic.
authors.forename: K S
authors.initials: KS
authors.lastname: Pasupathy
mesh_terms: D057225:Data Mining; D006801:Humans; D015233:Models, Statistical; D009873:Operating Rooms / Q000458:organization & administration*; D061646:Operative Time; D019982:Organizational Case Studies; D012044:Regression Analysis; D013514:Surgical Procedures, Operative / Q000145:classification / Q000706:statistics & numerical data*
keywords: Classification; hybrid method; prediction; regression; surgery times