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.