biostudies-literatureUnknown11(12)Trotzky D<h4>Aim</h4>The emergency department (ED) is the first port-of-call for most patients receiving hospital care and as such acts as a gatekeeper to the wards, directing patient flow through the hospital. ED overcrowding is a well-researched field and negatively affects patient outcome, staff well-being and hospital reputation. An accurate, real-time model capable of predicting ED overcrowding has obvious merit in a world becoming increasingly computational, although the complicated dynamics of the department have hindered international efforts to design such a model. Triage nurses' assessments have been shown to be accurate predictors of patient disposition and could, therefore, be useful input for overcrowding and patient flow models.<h4>Methods</h4>In this study, we assess the prediction cBMJ opene050026https://www.ebi.ac.uk/biostudies/studies/S-EPMC8663100biostudies-literatureReal-time prediction of patient disposition and the impact of reporter confidence on mid-level triage accuracies: an observational study in Israel.PMC8663100Trotzky DShopen NNegri Galam NCohen AMosery JKatz Shalhav MAvisar SMimran YPachys GFordham DEfalseReal-time prediction of patient disposition and the impact of reporter confidence on mid-level triage accuracies: an observational study in Israel.<h4>Aim</h4>The emergency department (ED) is the first port-of-call for most patients receiving hospital care and as such acts as a gatekeeper to the wards, directing patient flow through the hospital. ED overcrowding is a well-researched field and negatively affects patient outcome, staff well-being and hospital reputation. An accurate, real-time model capable of predicting ED overcrowding has obvious merit in a world becoming increasingly computational, although the complicated dynamics of the department have hindered international efforts to design such a model. Triage nurses' assessments have been shown to be accurate predictors of patient disposition and could, therefore, be useful input for overcrowding and patient flow models.<h4>Methods</h4>In this study, we assess the prediction c2021-01-01T00:00:00Z2021 Dec2025-04-19T16:19:54.177Z2024-12-03T16:41:25.768ZS-EPMC86631003488727210.1136/bmjopen-2021-050026