Prediction of daily patient numbers for a regional emergency medical center using time series analysis

Abstract

Objectives: To develop and evaluate time series models to predict the daily number of patients visiting the Emergency Department (ED) of a Korean hospital. Methods: Data were collected from the hospital information system database. In order to develop a forecasting model, we used, 2 years of data from January 2007 to December 2008 data for the following 3 consecutive months were processed for validation. To establish a forecasting model, calendar and weather variables were utilized. Three forecasting models were established: average; univariate seasonal auto-regressive integrated moving average (SARIMA); and multivariate SARIMA. To evaluate goodness-of-fit, residual analysis, Akaike information criterion and Bayesian information criterion were compared. The forecast accuracy for each model was evaluated via mean absolute percentage error (MAPE). Results: The multivariate SARIMA model was the most appropriate for forecasting the daily number of patients visiting the ED. Because it's MAPE was 7.4%, this was the smallest among the models, and for this reason was selected as the final model. Conclusions: This study applied explanatory variables to a multivariate SARIMA model. The multivariate SARIMA model exhibits relativelyhigh reliability and forecasting accuracy. The weather variables play a part in predicting daily ED patient volume. © 2010 The Korean Society of Medical Informatics.