Using Statistical Forecasting to Optimize Staff Scheduling in Healthcare Organizations

Abstract

Modern-day business environment of healthcare organizations demands the maximization of operational effectiveness and quality with optimal cost. Therefore, healthcare executives are often required to make difficult decisions based on subjective experience and judgement. An example of such a decision is scheduling of resources to fulfil demand for service. The effective use of statistical forecasting can lead to better personnel scheduling decisions based on estimates of patient arrival rates, resulting in improvement in quality of service as well as reduction of cost. The purpose of this article is to demonstrate the typical steps involved in applying forecasting techniques in patient care: This demonstration involves use of statistical techniques like Analysis of Variance (ANOVA) to identify factors driving demand, and Auto Regressive Integrated Moving Average (ARIMA) to develop a forecasting model for optimal staff scheduling in healthcare organizations based on patient arrival rates. The models are developed and subsequently tested on a set of real data gathered from a regional hospital located in the US. Statistically significant difference in average patient count was found among different days of the week. The findings of the research suggests that resources like cleaning personnel can be better utilized by allocating different proportions of resources to different parts of the week, based on the understanding of different patient load over these time periods.