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.
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: Nathan R Hoot; Stephen K Epstein; Todd L Allen; Spencer S Jones; Kevin M Baumlin; Neal Chawla; Anna T Lee; Jesse M Pines; Amandeep K Klair; Bradley D Gordon; Thomas J Flottemesch; Larry J LeBlanc; Ian Jones; Scott R Levin; Chuan Zhou; Cynthia S Gadd; Dominik Aronsky Journal: Ann Emerg Med Date: 2009-08-29 Impact factor: 5.721