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Related Experiment Video

Updated: Jun 25, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Forecasting emergency department crowding: a prospective, real-time evaluation.

Nathan R Hoot1, Larry J Leblanc, Ian Jones

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

Journal of the American Medical Informatics Association : JAMIA
|March 6, 2009
PubMed
Summary

Emergency department crowding can be accurately forecasted up to 8 hours in advance using the ForecastED simulation tool. This real-time forecasting system can help improve emergency department (ED) operations and patient care.

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Area of Science:

  • Healthcare Operations Research
  • Emergency Medicine
  • Health Informatics

Background:

  • Emergency department (ED) crowding negatively impacts healthcare quality and access.
  • Accurate forecasting of ED crowding is needed to develop strategies for mitigation.
  • The ForecastED discrete event simulation model was previously developed for ED crowding prediction.

Purpose of the Study:

  • To implement and validate the ForecastED simulation tool for real-time ED crowding forecasting.
  • To assess the accuracy of forecasts for various operational metrics up to 8 hours ahead.

Main Methods:

  • A prospective observational study was conducted over three months in an adult ED.
  • The ForecastED tool was integrated with existing information systems for real-time data updates every 10 minutes.
  • Forecasts were generated for waiting count, waiting time, occupancy, length of stay, boarding, and ambulance diversion.

Main Results:

  • The system achieved 99.9% forecast availability.
  • High R-squared values were observed for occupancy (0.57), length of stay (0.69), boarding count (0.61), and boarding time (0.53) at 8 hours.
  • The area under the ROC curve for predicting ambulance diversion at 8 hours was 0.85.

Conclusions:

  • The ForecastED tool accurately forecasts key ED crowding indicators up to 8 hours in the future.
  • Real-time deployment is feasible in EDs with access to six specific patient-level data variables.
  • This tool can support proactive management of ED crowding and improve patient flow.