Forecasting emergency department hourly occupancy using time series analysis.

The American Journal of Emergency Medicine
Qian ChengSerhan Ziya

Abstract

To develop a novel predictive model for emergency department (ED) hourly occupancy using readily available data at time of prediction with a time series analysis methodology. We performed a retrospective analysis of all ED visits from a large academic center during calendar year 2012 to predict ED hourly occupancy. Due to the time-of-day and day-of-week effects, a seasonal autoregressive integrated moving average with external regressor (SARIMAX) model was selected. For each hour of a day, a SARIMAX model was built to predict ED occupancy up to 4-h ahead. We compared the resulting model forecast accuracy and prediction intervals with previously studied time series forecasting methods. The study population included 65,132 ED visits at a large academic medical center during the year 2012. All adult ED visits during the first 265 days were used as a training dataset, while the remaining ED visits comprised the testing dataset. A SARIMAX model performed best with external regressors of current ED occupancy, average department-wide ESI, and ED boarding total at predicting up to 4-h-ahead ED occupancy (Mean Square Error (MSE) of 16.20, and 64.47 for 1-hr- and 4-h- ahead occupancy, respectively). Our 24-SARIMAX model outperformed othe...Continue Reading

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