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.
KeywordsForecast Error Multiple Regression Model Laboratory Procedure Forecast Method Inpatient Admission
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- McGee, Victor E., Jenkins, Elizabeth, and Rawnsley, Howard M., Statistical forecasting in a hospital clinical laboratory.J. Med. Syst. 3:161–174, 1979.Google Scholar
- Pang, Catherine Y., and Swint, J. Michael, Forecasting Staffing Needs for Productivity Management in Hospital Laboratories.J. Med. Syst. 9:365–376, 1985.Google Scholar
- Taylor, H.W., A study of factors affecting laboratory workload.Clin. Biochem. 4:179–182.Google Scholar
- Wilson, J. Holton, and Keating, Barry.Business Forecasting Richard D. Irwin, Inc., Homewood, IL. 1990.Google Scholar
- Wyse, Randy A., Manager of Laboratory, MidMichigan Regional Medical Center, 1991. Interview by author, Midland, MI.Google Scholar