ArticleAnnals of emergency medicine2008
Forecasting emergency department crowding: a discrete event simulation.
Article in Annals of emergency medicine, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 3 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Effectiveness of Mathematical and Simulation Models for Improving Quality of Care in Emergency Departments: A Systematic Literature Review.Applied clinical informatics · 2025Pooled it
- Challenges, consequences, and lessons for way-outs to emergencies at hospitals: a systematic review study.BMC emergency medicine · 2019Pooled it
- Pooled it
- Dynamics and Forecasting of Emergency Department Presentations: An Integrative Approach Based on Statistical Analysis and Time-Series Models.Healthcare (Basel, Switzerland) · 2026Article
- Development and validation of a multivariable predictive model for Emergency Department Overcrowding based on the National Emergency Department Overcrowding Study (NEDOCS) score.Internal and emergency medicine · 2025Article
- Key performance indicators in emergency department simulation: a scoping review.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Article
- Forecasting Mortality Associated Emergency Department Crowding with LightGBM and Time Series Data.Journal of medical systems · 2025Article
- Five Level Triage vs. Four Level Triage in a Quaternary Emergency Department: National Analysis on Waiting Time, Validity, and Crowding-The CREONTE (Crowding and RE-Organization National TriagE) Study Group.Medicina (Kaunas, Lithuania) · 2023Article
- Analyzing the queuing theory at the emergency department at King Hussein cancer center.BMC emergency medicine · 2023Article
- Multisite evaluation of prediction models for emergency department crowding before and during the COVID-19 pandemic.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Article
- Using discrete event simulation to optimize nucleic acid testing process for coronavirus disease 2019 (COVID-19).Journal of thoracic disease · 2022Article
- Modelling Granular Process Flow Information to Reduce Bottlenecks in the Emergency Department.Healthcare (Basel, Switzerland) · 2022Article
- Machine learning based forecast for the prediction of inpatient bed demand.BMC medical informatics and decision making · 2022Article
- Does crowding influence emergency department treatment time and disposition?Journal of the American College of Emergency Physicians open · 2021Article
- A unified machine learning approach to time series forecasting applied to demand at emergency departments.BMC emergency medicine · 2021Article
- Adjusting Daily Inpatient Bed Allocation to Smooth Emergency Department Occupancy Variation.Healthcare (Basel, Switzerland) · 2020Article
- Theoretical bounds and approximation of the probability mass function of future hospital bed demand.Health care management science · 2020Article
- Symbiotic simulation for the operational management of inpatient beds: model development and validation using Δ-method.Health care management science · 2020Article
- Decision Support for the Optimization of Provider Staffing for Hospital Emergency Departments with a Queue-Based Approach.Journal of clinical medicine · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
STUDY
objectiveTo develop a discrete event simulation of emergency department (ED) patient flow for the purpose of forecasting near-future operating conditions and to validate the forecasts with several measures of ED crowding.
methodsWe developed a discrete event simulation of patient flow with evidence from the literature. Development was purely theoretical, whereas validation involved patient data from an academic ED. The model inputs and outputs, respectively, are 6-variable descriptions of every present and future patient in the ED. We validated the model by 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 with Pearson's correlation, residual summary statistics, and area under the receiver operating characteristic curve.
resultsThe 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 area under the receiver operating characteristic curve=0.86).
conclusionBy modeling patient flow, rather than operational summary variables, our simulation forecasts several measures of near-future ED crowding, with various degrees of good performance.
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