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Updated: Jul 6, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
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Published on: January 15, 2017

Forecasting emergency department crowding: a discrete event simulation.

Nathan R Hoot1, Larry J LeBlanc, Ian Jones

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA. nathan.hoot@vanderbilt.edu

Annals of Emergency Medicine
|April 5, 2008
PubMed
Summary
This summary is machine-generated.

This study developed a simulation model to forecast emergency department (ED) crowding. The model accurately predicts patient flow and crowding measures up to eight hours in advance.

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Area of Science:

  • Healthcare Operations Research
  • Emergency Medicine
  • Predictive Analytics

Background:

  • Emergency departments (EDs) face challenges in managing patient flow and predicting crowding.
  • Accurate forecasting of ED operational conditions is crucial for optimizing resource allocation and patient care.

Purpose of the Study:

  • To develop and validate a discrete event simulation model for forecasting near-future ED patient flow and crowding.
  • To assess the model's predictive performance using various ED crowding measures.

Main Methods:

  • A discrete event simulation model of ED patient flow was developed using literature evidence.
  • Model validation involved using patient data from an academic ED with a sliding-window design.
  • Forecasting performance was evaluated for up to 8 hours using measures like waiting count, waiting time, occupancy, length of stay, boarding, and ambulance diversion.

Main Results:

  • The simulation demonstrated good forecasting performance for most ED crowding measures up to 8 hours.
  • Correlations between forecasts and actual outcomes were generally high, with Pearson's r values ranging from 0.49 to 0.86.
  • The model showed consistently high discriminatory power for predicting ambulance diversion.

Conclusions:

  • Discrete event simulation of patient flow effectively forecasts near-future ED crowding.
  • The model provides valuable insights into operational conditions, outperforming models based on summary variables.
  • This approach aids in proactive management of ED resources and patient flow.