Forecasting Daily Emergency Department Visits Using Calendar Variables and Ambient Temperature Readings

Abstract

Objectives: This study aimed to develop different models to forecast the daily number of patients seeking emergency department (ED) care in a general hospital according to calendar variables and ambient temperature readings and to compare the models in terms of forecasting accuracy. Methods:The authors developed and tested six different models of ED patient visits using total daily counts of patient visits to an ED in Sao Paulo, Brazil, from January 1, 2008, to December 31, 2010. The first 33 months of the data set were used to develop the ED patient visits forecasting models (the training set), leaving the last 3 months to measure each model's forecasting accuracy by the mean absolute percentage error (MAPE). Forecasting models were developed using three different time-series analysis methods: generalized linear models (GLM), generalized estimating equations (GEE), and seasonal autoregressive integrated moving average (SARIMA). For each method, models were explored with and without the effect of mean daily temperature as a predictive variable. Results:The daily mean number of ED visits was 389, ranging from 166 to 613. Data showed a weekly seasonal distribution, with highest patient volumes on Mondays and lowest patient volumes on weekends. There was little variation in daily visits by month. GLM and GEE models showed better forecasting accuracy than SARIMA models. For instance, the MAPEs from GLM models and GEE models at the first month of forecasting (October 2012) were 11.5 and 10.8% (models with and without control for the temperature effect, respectively), while the MAPEs from SARIMA models were 12.8 and 11.7%. For all models, controlling for the effect of temperature resulted in worse or similar forecasting ability than models with calendar variables alone, and forecasting accuracy was better for the short-term horizon (7 days in advance) than for the longer term (30 days in advance).Conclusions: This study indicates that time-series models can be developed to provide forecasts of daily ED patient visits, and forecasting ability was dependent on the type of model employed and the length of the time horizon being predicted. In this setting, GLM and GEE models showed better accuracy than SARIMA models. Including information about ambient temperature in the models did not improve forecasting accuracy. Forecasting models based on calendar variables alone did in general detect patterns of daily variability in ED volume and thus could be used for developing an automated system for better planning of personnel resources.ACADEMIC EMERGENCY MEDICINE 2013; 20:769-777

Assessment of forecasting models for patients arrival at Emergency Department

“…[18][19][20] refer that using precipitation this gives some uncertainty to the model giving also almost no improvements to the precision on the forecast. As stated in [18] different geographical areas with the ED showing different characteristics as well. In this work several environmental variables were studied in order to identify their relation to the ED arrivals.…”

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

“…Three forecasting models including hourly historical average, seasonal ARIMA and sinusoidal with an autoregression-structured error term were used in ED bed occupancy by Schweigler et al (2009). Marcilio et al (2013) used time-series methods including generalized linear models, generalized estimating equations and seasonal ARIMA to forecast daily ED visits. Kim et al (2014) applied exponential smoothing, ARIMA, seasonal ARIMA, and generalized autoregressive conditional heteroscedasticity (GARCH) methods in patient volume forecasting.…”

Using time series analysis to forecast emergency patient arrivals in CT department

“…We advocate the use of time series analysis to forecast EP arrivals [2] [3]. Series analysis is a versatile statistical procedure capable of predicting the future development according to the change trend of the past.…”