Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis

Abstract

ObjectivesTo develop and evaluate time series models to predict the daily number of patients visiting the Emergency Department (ED) of a Korean hospital.MethodsData 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: 1) average; 2) univariate seasonal auto-regressive integrated moving average (SARIMA); and 3) 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).ResultsThe 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.ConclusionsThis 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.

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“…Sun et al (2009) established ARIMA models to predict daily attendance at an ED. Kam (2010) used seasonal ARIMA to predict daily number of patient visits to ED. ARIMA was constructed to predict ED visitor volume in Chen et al's study (2011).…”

Time Series Models for Short Term Prediction of the Incidence of Japanese Encephalitis in Xianyang City, P R China△

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“…Schweigler et al (Schweigler et al, 2009) showed that the models using time series methods generate more accurate shortterm forecasts of Emergency Department (ED) bed occupancy than using traditional historical averages models. Kam et al (Kam et al, 2010) developed and assessed three time series models to predict the daily number of patients visiting the ED of a Korean hospital. The results show that the multivariate SARIMA model exhibits high reliability and forecasting accuracy compared to other models.…”