Abstract

http://ssrn.com/abstract=1088143
 
 

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Forecasting Emergency Department Crowding by Discrete Event Simulation


Nathan R. Hoot


Vanderbilt University

Larry J. LeBlanc


Vanderbilt University - Operations Management

Ian Jones


Vanderbilt University

Scott R. Levin


Vanderbilt University

Chuan Zhou


Vanderbilt University

Cindy Gadd


Vanderbilt University

Brent Lemonds


Vanderbilt University

Dominik Aronsky


Vanderbilt University


Annals of Emergency Medicine, April 2008

Abstract:     
Objective: To develop a discrete event simulation of ED patient flow for the purpose of forecasting near-future operating conditions, and to validate the forecasts using several measures of ED crowding.

Methods: We developed a discrete event simulation of patient flow using evidence from the literature. Development was purely theoretical, while validation involved patient data from an academic ED. The model inputs and outputs, respectively, are six-variable descriptions of every present and future patient in the ED. We validated the model using a sliding-window design, ensuring separation of fitting and validation data in time series. We sampled consecutive 10-minute observations during 2006 (n= 52,560). The outcome measures all forecast 2, 4, 6, and 8 hours into the future from each observation were the waiting count, waiting time, occupancy level, length of stay, boarding count, boarding time, and ambulance diversion. Forecasting performance was assessed using Pearson's correlation, residual summary statistics, and area under the receiver operating characteristic curve (AUC).

Results: The correlations between crowding forecasts and actual outcomes started high and decreased gradually up to 8 hours into the future (lowest Pearson's r for waiting count=0.56; waiting time=0.49; occupancy level=0.78; length of stay=0.86; boarding count=0.79; boarding time=0.80). The residual means were unbiased for all outcomes except the boarding time. The discriminatory power for ambulance diversion remained consistently high up to 8 hours into the future (lowest AUC=0.86).

Conclusion: By modeling patient flow, rather than operational summary variables, our simulation forecasts several different measures of near-future ED crowding with varying degrees of good performance.

Number of Pages in PDF File: 32

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Date posted: February 1, 2008  

Suggested Citation

Hoot, Nathan R. and LeBlanc, Larry J. and Jones, Ian and Levin, Scott R. and Zhou, Chuan and Gadd, Cindy and Lemonds, Brent and Aronsky, Dominik, Forecasting Emergency Department Crowding by Discrete Event Simulation. Annals of Emergency Medicine, April 2008. Available at SSRN: http://ssrn.com/abstract=1088143

Contact Information

Nathan R. Hoot
Vanderbilt University ( email )
Nashville, TN 37240
United States
Larry LeBlanc (Contact Author)
Vanderbilt University - Operations Management ( email )
Nashville, TN 37203
United States

Ian Jones
Vanderbilt University ( email )
Nashville, TN 37240
United States
Scott R. Levin
Vanderbilt University ( email )
Nashville, TN 37240
United States
Chuan Zhou
Vanderbilt University ( email )
Nashville, TN 37240
United States
Cindy Gadd
Vanderbilt University ( email )
Nashville, TN 37240
United States
Brent Lemonds
Vanderbilt University ( email )
Nashville, TN 37240
United States
Dominik Aronsky
Vanderbilt University ( email )
Nashville, TN 37240
United States
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