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The challenge of predicting demand for emergency department services.
Acad Emerg Med. 2008 Apr; 15(4):337-46.AE

Abstract

OBJECTIVES

The objective was to develop methodology for predicting demand for emergency department (ED) services by characterizing ED arrivals.

METHODS

One year of ED arrival data from an academic ED were merged with local climate data. ED arrival patterns were described; Poisson regression was selected to represent the count of hourly ED arrivals as a function of temporal, climatic, and patient factors. The authors evaluated the appropriateness of prediction models by whether the data met key Poisson assumptions, including variance proportional to the mean, positive skewness, and absence of autocorrelation among hours. Model accuracy was assessed by comparing predicted and observed histograms of arrival counts and by how frequently the observed hourly count fell within the 50 and 90% prediction intervals.

RESULTS

Hourly ED arrivals were obtained for 8,760 study hours. Separate models were fit for high- versus low-acuity patients because of significant arrival pattern differences. The variance was approximately equal to the mean in the high- and low-acuity models. There was no residual autocorrelation (r = 0) present after controlling for temporal, climatic, and patient factors that influenced the arrival rate. The observed hourly count fell within the 50 and 90% prediction intervals 50 and 90% of the time, respectively. The observed histogram of arrival counts was nearly identical to the histogram predicted by a Poisson process.

CONCLUSIONS

At this facility, demand for ED services was well approximated by a Poisson regression model. The expected arrival rate is characterized by a small number of factors and does not depend on recent numbers of arrivals.

Authors+Show Affiliations

Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. mmccarth@jhmi.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

18370987

Citation

McCarthy, Melissa L., et al. "The Challenge of Predicting Demand for Emergency Department Services." Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine, vol. 15, no. 4, 2008, pp. 337-46.
McCarthy ML, Zeger SL, Ding R, et al. The challenge of predicting demand for emergency department services. Acad Emerg Med. 2008;15(4):337-46.
McCarthy, M. L., Zeger, S. L., Ding, R., Aronsky, D., Hoot, N. R., & Kelen, G. D. (2008). The challenge of predicting demand for emergency department services. Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine, 15(4), 337-46. https://doi.org/10.1111/j.1553-2712.2008.00083.x
McCarthy ML, et al. The Challenge of Predicting Demand for Emergency Department Services. Acad Emerg Med. 2008;15(4):337-46. PubMed PMID: 18370987.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - The challenge of predicting demand for emergency department services. AU - McCarthy,Melissa L, AU - Zeger,Scott L, AU - Ding,Ru, AU - Aronsky,Dominik, AU - Hoot,Nathan R, AU - Kelen,Gabor D, PY - 2008/3/29/pubmed PY - 2008/5/7/medline PY - 2008/3/29/entrez SP - 337 EP - 46 JF - Academic emergency medicine : official journal of the Society for Academic Emergency Medicine JO - Acad Emerg Med VL - 15 IS - 4 N2 - OBJECTIVES: The objective was to develop methodology for predicting demand for emergency department (ED) services by characterizing ED arrivals. METHODS: One year of ED arrival data from an academic ED were merged with local climate data. ED arrival patterns were described; Poisson regression was selected to represent the count of hourly ED arrivals as a function of temporal, climatic, and patient factors. The authors evaluated the appropriateness of prediction models by whether the data met key Poisson assumptions, including variance proportional to the mean, positive skewness, and absence of autocorrelation among hours. Model accuracy was assessed by comparing predicted and observed histograms of arrival counts and by how frequently the observed hourly count fell within the 50 and 90% prediction intervals. RESULTS: Hourly ED arrivals were obtained for 8,760 study hours. Separate models were fit for high- versus low-acuity patients because of significant arrival pattern differences. The variance was approximately equal to the mean in the high- and low-acuity models. There was no residual autocorrelation (r = 0) present after controlling for temporal, climatic, and patient factors that influenced the arrival rate. The observed hourly count fell within the 50 and 90% prediction intervals 50 and 90% of the time, respectively. The observed histogram of arrival counts was nearly identical to the histogram predicted by a Poisson process. CONCLUSIONS: At this facility, demand for ED services was well approximated by a Poisson regression model. The expected arrival rate is characterized by a small number of factors and does not depend on recent numbers of arrivals. SN - 1553-2712 UR - https://www.unboundmedicine.com/medline/citation/18370987/The_challenge_of_predicting_demand_for_emergency_department_services_ L2 - https://doi.org/10.1111/j.1553-2712.2008.00083.x DB - PRIME DP - Unbound Medicine ER -