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Healthc Inform Res. 2010 Sep;16(3):158-65. doi: 10.4258/hir.2010.16.3.158. Epub 2010 Sep 30.

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

Author information

1
Department of Biomedical Informatics, School of Medicine, Ajou University, Suwon, Korea.

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: 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).

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.

KEYWORDS:

Crowding; Emergency Medical Service; Seasonal Variation; Statistical Models; Trends

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