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Forecasting the number of bed days (NBD) needed within a large hospital network is extremely challenging, but it is imperative that management find a predictive model that best estimates the calculation. This estimate is used by operational managers for logistical planning purposes. Furthermore, the finance staff of a hospital would require an expected NBD as input for estimating future expenses. Some hospital reimbursement contracts are on a per diem schedule, and expected NBD is useful in forecasting future revenue.

This chapter examines two ways of estimating the NBD for a large hospital system, and it builds from previous work comparing time regression and an autoregressive integrated moving average (ARIMA). The two approaches discussed in this chapter examine whether using the total or combined NBD for all the data is a better predictor than partitioning the data by different types of services. The four partitions are medical, maternity, surgery, and psychology. The partitioned time series would then be used to forecast future NBD by each type of service, but one could also sum the partitioned predictors for an alternative total forecaster. The question is whether one of these two approaches outperforms the other with a best fit for forecasting the NBD. The approaches presented in this chapter can be applied to a variety of time series data for business forecasting when a large database of information can be partitioned into smaller segments.

Managing a large hospital network can be an extremely challenging task. Management must rely on numerous pieces of information when making business decisions. This chapter focuses on the number of bed days (NBD) which can be extremely valuable for operational managers to forecast for logistical planning purposes. In addition, the finance staff often requires an expected NBD as input for estimating future expenses. Some hospital reimbursement contracts are on a per diem schedule, and expected NBD is useful in forecasting future revenue.Two models, time regression and autoregressive integrated moving average (ARIMA), are applied to nine years of monthly counts of the NBD for the Rhode Island Hospital System. These two models are compared to see which gives the best fit for the forecasted NBD. Also, the question of summarizing the time data from monthly to quarterly time periods is addressed. The approaches presented in this chapter can be applied to a variety of time series data for business forecasting.