ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2006
An early warning system for overcrowding in the emergency department.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- What proportion of healthcare worker masks carry virus? A systematic review.Emergency medicine Australasia : EMA · 2020Pooled it
- Crowding in real-time: an artificial intelligence-based model to manage crowding in the emergency department (CRAMMED Study).BMC emergency medicine · 2026Article
- National Emergency Department Overcrowding Scale as a Predictor for Running an In-Situ Simulation.Cureus · 2026Article
- Emergency department overcrowding: first Swiss application of the Emergency Department Work Index and risk factors for overcrowding.Frontiers in public health · 2025Article
- Emergency department overcrowding and its associated factors at HARME medical emergency center in Eastern Ethiopia.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2024Article
- The Association between Emergency Department Overcrowding and Delay in Treatment: A Systematic Review.Healthcare (Basel, Switzerland) · 2023Review
- Ochsner Emergency Department Overcrowding Scale (OEDOCS): predicting Emergency Department crowding among diverse academic hospital settings.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Factors Relating to Decision Delay in the Emergency Department: Effects of Diagnostic Tests and Consultations.Open access emergency medicine : OAEM · 2023Article
- Measures of Emergency Department Crowding, a Systematic Review. How to Make Sense of a Long List.Open access emergency medicine : OAEM · 2022Review
- Ambulance diversions following public hospital emergency department closures.Health services research · 2019Article
- Article
- Overcrowding and Its Association With Patient Outcomes in a Median-Low Volume Emergency Department.Journal of clinical medicine research · 2017Article
- Forecasting emergency department crowding: a discrete event simulation.Annals of emergency medicine · 2008Article
- Measuring and forecasting emergency department crowding in real time.Annals of emergency medicine · 2007Article
- Supporting patient care in the emergency department with a computerized whiteboard system.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Overcrowding of emergency departments impedes health care access and quality nationwide. A real-time early warning system for overcrowding may allow administrators to alleviate the problem before reaching a crisis state. Two original probabilistic models - a logistic regression and a recurrent neural network - were created to predict overcrowding crises one hour in the future. The two original and two pre-existing models were validated at 8,496 observation points from January 1, 2006 to February 28, 2006. All models showed high discriminatory ability in terms of area under the receiver operating characteristic curve (logistic regression = .954; recurrent neural network = .957; EDWIN = .879; NEDOCS = .924). At comparable rates of false alarms, the logistic regression gave more advance notice of crises than other models (logistic regression = 62 min; recurrent neural network = 13 min; EDWIN = 0 min; NEDOCS = 0 min). These results demonstrate the feasibility of using models based on key operational variables to anticipate overcrowding crises in real time.
Indexed as
Identifiers
17238359PMC1839284What OpenQuestion holds
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.