An Integrated Forecasting Prototype for Emergency Department Boarding Time to Support Proactive Operational Decision Making
A new forecasting prototype has been developed to predict emergency department boarding times, which is crucial for managing overcrowding. This model utilizes real-world data and external factors to enhance operational decision-making. The research aims to improve patient care by proactively addressing potential congestion in emergency departments.
- ▪The prototype predicts boarding times at multiple horizons, including 6, 8, 10, 12, and 24 hours.
- ▪It integrates data from a university-affiliated urban hospital along with external contextual data such as weather and local events.
- ▪The study employs advanced deep learning models that demonstrated superior performance in forecasting.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18839 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Machine Learning arXiv:2605.18839 (cs) [Submitted on 13 May 2026] Title:An Integrated Forecasting Prototype for Emergency Department Boarding Time to Support Proactive Operational Decision Making Authors:Orhun Vural, Abdulaziz Ahmed, Ferhat Zengul, James Booth, Bunyamin Ozaydin View a PDF of the paper titled An Integrated Forecasting Prototype for Emergency Department Boarding Time to Support Proactive Operational Decision Making, by Orhun Vural and 4 other authors View PDF Abstract:Overcrowding in emergency departments (ED) remains a persistent operational challenge worldwide, causing delays in care delivery and downstream congestion.
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