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Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis.
Healthc Inform Res. 2010 Sep; 16(3):158-65.HI

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.

Authors+Show Affiliations

Department of Biomedical Informatics, School of Medicine, Ajou University, Suwon, Korea.No affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

21818435

Citation

Kam, Hye Jin, et al. "Prediction of Daily Patient Numbers for a Regional Emergency Medical Center Using Time Series Analysis." Healthcare Informatics Research, vol. 16, no. 3, 2010, pp. 158-65.
Kam HJ, Sung JO, Park RW. Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis. Healthc Inform Res. 2010;16(3):158-65.
Kam, H. J., Sung, J. O., & Park, R. W. (2010). Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis. Healthcare Informatics Research, 16(3), 158-65. https://doi.org/10.4258/hir.2010.16.3.158
Kam HJ, Sung JO, Park RW. Prediction of Daily Patient Numbers for a Regional Emergency Medical Center Using Time Series Analysis. Healthc Inform Res. 2010;16(3):158-65. PubMed PMID: 21818435.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis. AU - Kam,Hye Jin, AU - Sung,Jin Ok, AU - Park,Rae Woong, Y1 - 2010/09/30/ PY - 2010/05/07/received PY - 2010/09/09/accepted PY - 2011/8/6/entrez PY - 2011/8/6/pubmed PY - 2011/8/6/medline KW - Crowding KW - Emergency Medical Service KW - Seasonal Variation KW - Statistical Models KW - Trends SP - 158 EP - 65 JF - Healthcare informatics research JO - Healthc Inform Res VL - 16 IS - 3 N2 - 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. SN - 2093-369X UR - https://www.unboundmedicine.com/medline/citation/21818435/Prediction_of_Daily_Patient_Numbers_for_a_Regional_Emergency_Medical_Center_using_Time_Series_Analysis_ L2 - https://www.e-hir.org/DOIx.php?id=10.4258/hir.2010.16.3.158 DB - PRIME DP - Unbound Medicine ER -
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