ArticleHealth systems (Basingstoke, England)2024
Probabilistic forecasting of hourly emergency department arrivals.
Article in Health systems (Basingstoke, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Forecasting hospital bed occupancy: a time series approach with prophet.BMC medical informatics and decision making · 2026Article
- Predicting Emergency Department Patient Arrivals at Hospitals Using Machine Learning Techniques.Healthcare (Basel, Switzerland) · 2026Article
- Ascertaining cause-specific emergency department demand using forecast combinations.BMC emergency medicine · 2026Article
- Enhancing patient admission efficiency through a hybrid cloud framework for medical record sharing.Scientific reports · 2026Article
- Discrete Event Simulation-Based Analysis and Optimization of Emergency Patient Scheduling Strategies.Healthcare (Basel, Switzerland) · 2025Article
- AI-powered models for overcrowding prediction at TUMS hospitals.Scientific reports · 2025Article
- An Artificial Intelligence-Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study.JMIR medical informatics · 2025Article
- Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.BMC medical informatics and decision making · 2024Article
- On the assessment of the ability of measurements, nowcasts, and forecasts to track changes.BMC medical research methodology · 2024Article
- Two-stage multi-objective optimization for ICU bed allocation under multiple sources of uncertainty.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An accurate forecast of Emergency Department (ED) arrivals by an hour of the day is critical to meet patients' demand. It enables planners to match ED staff to the number of arrivals, redeploy staff, and reconfigure units. In this study, we develop a model based on Generalised Additive Models and an advanced dynamic model based on exponential smoothing to generate an hourly probabilistic forecast of ED arrivals for a prediction window of 48 hours. We compare the forecast accuracy of these models against appropriate benchmarks, including TBATS, Poisson Regression, Prophet, and simple empirical distribution. We use Root Mean Squared Error to examine the point forecast accuracy and assess the forecast distribution accuracy using Quantile Bias, PinBall Score and Pinball Skill Score. Our results indicate that the proposed models outperform their benchmarks. Our developed models can also be generalised to other services, such as hospitals, ambulances or clinical desk services.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.