Autoregressive integrated moving average model for long-term prediction of emergency department revenue and visitor volume

Abstract

No studies have simultaneously evaluated the possible associations of meteorological, organizational, and socioeconomic factors with emergency department (ED) revenue and visitor volume. This study analyzed meteorological, organizational and socioeconomic effects on monthly ED revenue and visitor volume. Monthly data for January 1, 2005, to September 31, 2009, were analyzed. Spearman correlation and cross-correlation analyses were performed to identify time lag values between each independent variable, ED revenue, and visitor volume, and autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. The model also performed well in forecasting revenue and visitor volume. Meteorological, organizational and socioeconomic aspects are associated with ED revenue and visitor volume. The proposed model is effective for long term forecasting capability.