title: Short term hospital occupancy prediction.
abstract: Inpatient census, or occupancy, is a primary driver of resource use in hospitals. Fluctuations in occupancy complicate decisions related to staffing, bed management, ambulance diversions, and may ultimately impact both quality of patient care and nursing job satisfaction. We describe our approach in building a computerized model to provide short-term occupancy predictions for an entire hospital by nursing unit and shift. Our model is a comprehensive system built using real hospital data and utilizes statistical predictions at the individual patient level. We discuss the results of piloting an early version of the model at a mid-size community hospital. The primary focus of the paper is on the development and methodology of a second generation of the predictive occupancy model. The results and accuracy of this new model is compared to a variety of other predictive methods based on tests using 2 years of actual hospital data.
authors.affiliation: Improvement Path Systems, Inc., Farmington Hills, MI, USA. littig@improvementpath.com
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Littig
authors.forename: Mark W
authors.initials: MW
authors.lastname: Isken
mesh_terms: D001509:Bed Occupancy / Q000639:trends*; D005544:Forecasting; D006739:Hospital Administration; D006801:Humans; D015233:Models, Statistical; D014481:United States