Forecasting Emergency Department Crowding by Discrete Event Simulation

Annals of Emergency Medicine, April 2008

32 Pages Posted: 1 Feb 2008

See all articles by Nathan R. Hoot

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

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.

Suggested Citation

Hoot, Nathan R. and LeBlanc, Larry 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: https://ssrn.com/abstract=1088143

Nathan R. Hoot

Vanderbilt University ( email )

2301 Vanderbilt Place
Nashville, TN 37240
United States

Larry LeBlanc (Contact Author)

Vanderbilt University - Operations Management ( email )

Nashville, TN 37203
United States

Ian Jones

Vanderbilt University ( email )

2301 Vanderbilt Place
Nashville, TN 37240
United States

Scott R. Levin

Vanderbilt University ( email )

2301 Vanderbilt Place
Nashville, TN 37240
United States

Chuan Zhou

Vanderbilt University ( email )

2301 Vanderbilt Place
Nashville, TN 37240
United States

Cindy Gadd

Vanderbilt University ( email )

2301 Vanderbilt Place
Nashville, TN 37240
United States

Brent Lemonds

Vanderbilt University ( email )

2301 Vanderbilt Place
Nashville, TN 37240
United States

Dominik Aronsky

Vanderbilt University ( email )

2301 Vanderbilt Place
Nashville, TN 37240
United States

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