title: Forecasting hospital laboratory procedures.
abstract: Improved forecasts of hospital laboratory procedures can provide the basis for better resource planning and enhanced operating efficiency. The research reported here-in describes how multiple regression models can be both a source of insight into causal relationships and a tool for achieving accurate monthly forecasts. Past research in this area may have overstated the statistical significance of findings because of a failure to address the potential effect of serial correlation. The present study uses the Cochrane-Orcutt regression procedure, rather than OLS, to overcome this problem. A model using inpatient admissions, acuity days, length of stay, discharge days and seasonal dummy variables is shown to account for 87% of the variation in the number of billable laboratory procedures. A simpler multiple regression model and a Winters' exponential smoothing model were found to provide excellent forecasts for laboratory procedures. In a one year out of sample evaluation, the annual percent forecast error was 0.7% for the regression model. This compares favorably to a percentage forecast error of 11.6% using subjective forecasting methods.
authors.affiliation: Department of Marketing, Central Michigan University, Mt. Pleasant 48859.
authors.forename: J H
authors.initials: JH
authors.lastname: Wilson
authors.forename: S J
authors.initials: SJ
authors.lastname: Schuiling
mesh_terms: D005544:Forecasting; D006745:Hospital Bed Capacity, 300 to 499; D006784:Hospitals, Teaching / Q000706:statistics & numerical data; D007755:Laboratories, Hospital / Q000458:organization & administration / Q000706:statistics & numerical data*; D016014:Linear Models; D008824:Michigan; D015233:Models, Statistical; D012044:Regression Analysis; D016526:Workload / Q000706:statistics & numerical data*