Forecasting Emergency Department Crowding by Discrete Event Simulation

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