Can artificial intelligence and machine learning help reduce the harms of emergency department crowding?

Hudson, George R.; Howley, Natasha; Boyle, Adrian

doi:10.1097/mej.0000000000000781

Cited by 8 publications

(9 citation statements)

References 14 publications

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“…Artificial intelligence (AI), with an explainable machine learning model, is a viable option [6]. Using historic data from past mass gatherings, models could play a crucial role in optimizing medical deployment in various ways [7].…”

Section: Technological Advancements: the Role Of Aimentioning

confidence: 99%

Redefining the role of emergency medicine in mass gatherings

Stiers,

Van Asbroeck,

Hoogmartens

et al. 2024

European Journal of Emergency Medicine

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Section: Technological Advancements: the Role Of Aimentioning

confidence: 99%

Redefining the role of emergency medicine in mass gatherings

Stiers,

Van Asbroeck,

Hoogmartens

et al. 2024

European Journal of Emergency Medicine

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“…A mandatory rule for patient admission in less than 4 h after ED arrival for at least 95% of patients has been implemented in Great Britain as well as Australia and had a favourable impact on in-hospital mortality [12,13]. New tools are investigated in order to improve patient flow through the ED and reduce the harms of crowding [14,15].…”

Section: Introductionmentioning

confidence: 99%

Impact of emergency department length of stay on in-hospital mortality: a retrospective cohort study

Balen,

Routoulp,

Charpentier

et al. 2023

European Journal of Emergency Medicine

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Background and importance Emergency Department (ED) workload may lead to ED crowding and increased ED length of stay (LOS). ED crowding has been shown to be associated with adverse events and increasing mortality. We hypothesised that ED-LOS is associated with mortality. Objective To study the relationship between ED-LOS and in-hospital mortality. Design Observational retrospective cohort study. Settings and participants From 1 January 2015 to 30 September 2018, all visits by patients aged 15 or older to one of the two ED at Toulouse University Hospital were screened. Patients admitted to the hospital after ED visits were included. Visits followed by ED discharge, in-ED death or transfer to ICU or another hospital were not included. Outcome measure and analysis The primary outcome was 30-day in-hospital mortality. ED-LOS was defined as time from ED registration to inpatient admission. ED-LOS was categorised according to quartiles [<303 min (Q1), between 303 and 433 minutes (Q2), between 434 and 612 minutes (Q3) and >612 min (Q4)]. A multivariable logistic regression tested the association between ED-LOS and in-hospital mortality. Main results A total of 49 913 patients were admitted to our hospital after ED visits and included in the study. ED-LOS was not independently associated with in-hospital mortality. Compared to ED-LOS < 303 min (Q1, reference), odd-ratios (OR) [95% CI] of in-hospital mortality for Q2, Q3, and Q4 were respectively 0.872 [0.747–1.017], 0.906 [0.777–1.056], and 1.137 [0.985–1.312]. Factors associated to in-hospital mortality were: aged over 75 years (OR [95% CI] = 4.3 [3.8–4.9]), Charlson Comorbidity Index score > 1 (OR [95% CI] = 1.3 [1.1–1.5], and 2.2 [1.9–2.5] for scores 2 and ≥ 3 respectively), high acuity at triage (OR [95% CI] = 3.9 [3.5–4.4]), ED visit at Hospital 1 (OR [95% CI] = 1.6 [1.4–1.7]), and illness diagnosis compared to trauma (OR [95% CI] = 2.1 [1.7–2.6]). Night-time arrival was associated with decreased in-hospital mortality (OR [95% CI] = 0.852 [0.767–0.947]). Conclusion In this retrospective cohort study, there was no independent association between ED-LOS before admission to general non-ICU wards and in-patient mortality.

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“…As stated by Hudson et al [5], it is of more value to focus on predicting the outflow of admitted patients rather than predicting the influx, as the outflow is a more critical influencer of ED crowding. However, choices have to be made as executing reverse triage protocols, in case of MCIs or ED crowding, requires medical personnel who are simultaneously needed to provide care.…”

mentioning

confidence: 99%

“…This major influx due to MCIs or ED crowding can easily overwhelm an ED and subsequently the entire hospital capacity and its resources. In the search for coping strategies, triage systems and new technologies have been studied [3][4][5]. A healthcare system's ability to rapidly expand its normal capacity to meet the increase in demand is called surge capacity.…”

mentioning

confidence: 99%