Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis / 대한의료정보학회지
Healthcare Informatics Research
;
: 158-165, 2010.
Article
in English
| WPRIM
| ID: wpr-191454
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Seasons
/
Weather
/
Crowding
/
Models, Statistical
/
Hospital Information Systems
/
Emergencies
/
Emergency Medical Services
/
Forecasting
Type of study:
Prognostic study
/
Risk factors
Limits:
Humans
Language:
English
Journal:
Healthcare Informatics Research
Year:
2010
Type:
Article
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