Hospitals and outpatient surgery centres are often plagued by a recurring staff management question: "How can we plan our nursing schedule weeks in advance, not knowing how many and when patients will require surgery?" Demand for surgery is driven by patient needs, physician constraints, and weekly or seasonal fluctuations. With all of these factors embedded into historical surgical volume, we use time series analysis methods to forecast daily surgical case volumes, which can be extremely valuable for estimating workload and labour expenses. Seasonal Autoregressive Integrated Moving Average (SARIMA) modelling is used to develop a statistical prediction model that provides short-term forecasts of daily surgical demand. We used data from a Level 1 Trauma Centre to build and evaluate the model. Our results suggest that the proposed SARIMA model can be useful for estimating surgical case volumes 2-4 weeks prior to the day of surgery, which can support robust and reliable staff schedules.
Keywords: ARIMA modelling; Time series analysis; forecasting; perioperative Systems; seasonality; surgical case volume.