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Forecasting daily patient volumes in the emergency department.
Acad Emerg Med. 2008 Feb; 15(2):159-70.AE

Abstract

BACKGROUND

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED.

OBJECTIVES

The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method.

METHODS

Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature.

RESULTS

All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes.

CONCLUSIONS

This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

Authors+Show Affiliations

Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA. spencer.jones@hsc.utah.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article
Research Support, N.I.H., Extramural

Language

eng

PubMed ID

18275446

Citation

Jones, Spencer S., et al. "Forecasting Daily Patient Volumes in the Emergency Department." Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine, vol. 15, no. 2, 2008, pp. 159-70.
Jones SS, Thomas A, Evans RS, et al. Forecasting daily patient volumes in the emergency department. Acad Emerg Med. 2008;15(2):159-70.
Jones, S. S., Thomas, A., Evans, R. S., Welch, S. J., Haug, P. J., & Snow, G. L. (2008). Forecasting daily patient volumes in the emergency department. Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine, 15(2), 159-70. https://doi.org/10.1111/j.1553-2712.2007.00032.x
Jones SS, et al. Forecasting Daily Patient Volumes in the Emergency Department. Acad Emerg Med. 2008;15(2):159-70. PubMed PMID: 18275446.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Forecasting daily patient volumes in the emergency department. AU - Jones,Spencer S, AU - Thomas,Alun, AU - Evans,R Scott, AU - Welch,Shari J, AU - Haug,Peter J, AU - Snow,Gregory L, PY - 2008/2/16/pubmed PY - 2008/2/29/medline PY - 2008/2/16/entrez SP - 159 EP - 70 JF - Academic emergency medicine : official journal of the Society for Academic Emergency Medicine JO - Acad Emerg Med VL - 15 IS - 2 N2 - BACKGROUND: Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. OBJECTIVES: The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. METHODS: Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. RESULTS: All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. CONCLUSIONS: This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes. SN - 1553-2712 UR - https://www.unboundmedicine.com/medline/citation/18275446/Forecasting_daily_patient_volumes_in_the_emergency_department_ L2 - https://doi.org/10.1111/j.1553-2712.2007.00032.x DB - PRIME DP - Unbound Medicine ER -