Do Machine Learning-Guided Proactive Full Capacity Protocols Mitigate Emergency Department Overcrowding? A Simulation-Based Evaluation
Abstract
Objective: Emergency department (ED) overcrowding threatens care quality, yet Full Capacity Protocols (FCPs) are typically activated only after crowding thresholds have been exceeded. This study evaluated whether machine learning-guided proactive FCP activation improves ED crowding control compared with conventional reactive activation. Materials and Methods: We developed and validated an integrated machine learning and discrete-event simulation framework representing ED patient flow and capacity management. An embedded machine learning model predicted hourly waiting-room crowding six hours ahead and triggered proactive FCP activation. Using empirically calibrated operational parameters from a large US hospital, we compared proactive and reactive deployment strategies over a 365-day simulation horizon with 10 independent replications. Outcomes included ED length of stay, wait time, percentage of time above the crowding threshold, number of FCP activations, and protocol duration. Results: Proactive deployment produced statistically significant reductions in the percentage of time above the crowding threshold and average protocol duration across all threshold settings. It also reduced crowding variability and extreme congestion events, resulting in smoother ED operations. Although proactive deployment generally improved ED length of stay and wait time, these differences were not statistically significant. Conclusion: Embedding machine learning predictions within an operational simulation enables proactive, prediction-guided capacity management before severe congestion develops. These findings support the feasibility and potential value of proactive FCP deployment as a decision-support strategy for improving ED crowding control and provide evidence to support prospective implementation in clinical practice.
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BibTeX
@misc{aram2026machine,
title = {{Do Machine Learning-Guided Proactive Full Capacity Protocols Mitigate Emergency Department Overcrowding? A Simulation-Based Evaluation}},
author = {Khalid Aram and Orhun Vural and Bunyamin Ozaydin and James Booth and Abdulaziz Ahmed},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30148},
eprint = {2610.30148},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30148},
}
RIS
TY - UNPB
TI - Do Machine Learning-Guided Proactive Full Capacity Protocols Mitigate Emergency Department Overcrowding? A Simulation-Based Evaluation
AU - Khalid Aram
AU - Orhun Vural
AU - Bunyamin Ozaydin
AU - James Booth
AU - Abdulaziz Ahmed
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.30148
UR - https://arxiv.org/abs/2610.30148
AB - Objective: Emergency department (ED) overcrowding threatens care quality, yet Full Capacity Protocols (FCPs) are typically activated only after crowding thresholds have been exceeded. This study evaluated whether machine learning-guided proactive FCP activation improves ED crowding control compared with conventional reactive activation. Materials and Methods: We developed and validated an integrated machine learning and discrete-event simulation framework representing ED patient flow and capacity management. An embedded machine learning model predicted hourly waiting-room crowding six hours ahead and triggered proactive FCP activation. Using empirically calibrated operational parameters from a large US hospital, we compared proactive and reactive deployment strategies over a 365-day simulation horizon with 10 independent replications. Outcomes included ED length of stay, wait time, percentage of time above the crowding threshold, number of FCP activations, and protocol duration. Results: Proactive deployment produced statistically significant reductions in the percentage of time above the crowding threshold and average protocol duration across all threshold settings. It also reduced crowding variability and extreme congestion events, resulting in smoother ED operations. Although proactive deployment generally improved ED length of stay and wait time, these differences were not statistically significant. Conclusion: Embedding machine learning predictions within an operational simulation enables proactive, prediction-guided capacity management before severe congestion develops. These findings support the feasibility and potential value of proactive FCP deployment as a decision-support strategy for improving ED crowding control and provide evidence to support prospective implementation in clinical practice.
ER -