Healthc Inform Res.  2010 Sep;16(3):158-165. 10.4258/hir.2010.16.3.158.

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

• Kam HJ
• Sung JO
• Park RW

• KMID: 2284528
• DOI: http://doi.org/10.4258/hir.2010.16.3.158

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.

Keyword

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

MeSH Terms

Crowding
Emergencies
Emergency Medical Services
Forecasting
Hospital Information Systems
Humans
Models, Statistical
Seasons
Weather

Figure

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  Figure 1 Time plots of daily emergency department (ED) patients (2007. 01-2009. 03). During the period from January 2007 to March 2009, a total of 189,511 ED patients visited and average number of daily patients was 231. The sequencing graph showed a 7-day periodicity and seasonal trend. In particular, there was a sharp increase in the number of patients in Chuseok.

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  Figure 2 The time series after transforms using seasonal difference [1].

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  Figure 3 Observed and predicted daily emergency department patients.

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