Objective Emergency department (ED) triage determines patient prioritization, early risk recognition, and allocation of limited resources. Artificial intelligence (AI) has been explored to support triagerelated decision-making, but the evidence remains heterogeneous. This scoping review aimed to map applications of AI-assisted triage in EDs and summarize reported outcomes, safety, equity, and implementation challenges.
Methods This scoping review followed Arksey and O’Malley’s framework. Scopus, PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and manual searching were used. Original empirical studies published in English between 2015 and 2026 were included if they evaluated AI tools supporting ED triage, risk stratification, resource prediction, or patient-flow decision-making.
Results Of 1,865 records identified, 27 studies met the inclusion criteria. Included studies used machine learning, deep learning, natural language processing (NLP), artificial neural networks, interpretable machine learning, AI-informed decision-support systems, and large language models (LLMs). AI was applied to acuity classification, admission prediction, intensive care unit (ICU) admission prediction, mortality prediction, sepsis detection, waiting-time estimation, and patient-flow optimization. Conventional machine learning and NLP models generally reported promising predictive performance, particularly when structured triage variables were combined with unstructured clinical text. However, fewer studies evaluated prospective validation, workflow integration, measurable clinical and operational impact, equity, or post-deployment monitoring. Evidence on LLMs remains preliminary, with concerns about undertriage, inconsistency, hallucination, local adaptability, and the need for supervised use.
Conclusion AI-assisted triage may support ED decision-making and patient-flow management. Future implementation should prioritize supervised human–AI collaboration, prospective validation, explainability, fairness assessment, clinician training, workflow integration, and continuous monitoring.
Objective Many studies have examined the July effect. However, little is known about the July effect in sepsis. We hypothesized that the July effect would result in worse outcomes for patients with sepsis. Methods Data from patients with sepsis, collected prospectively between January 2018 and December 2021, were analyzed. In Korea, the new academic year starts on March 1, so the “July effect” appears in March. The primary outcome was 30-day mortality. Secondary outcomes included adherence to the Surviving Sepsis Campaign bundle. Outcomes in March were compared to other months. A multivariate Cox proportional hazard regression was performed to adjust for confounders. Results We included 843 patients. There were no significant differences in sepsis severity. The 30-day mortality in March was higher (49.0% vs. 28.5%, P<0.001). However, there was no difference in bundle adherence in March (42.2% vs. 48.0%, P=0.264). The multivariate Cox proportional hazard regression showed that the July effect was associated with 30-day mortality in patients with sepsis (adjusted hazard ratio, 1.925; 95% confidence interval, 1.405–2.638; P<0.001). Conclusion The July effect was associated with 30-day mortality in patients with sepsis. However, bundle adherence did not differ. These results suggest that the increase in mortality during the turnover period might be related to unmeasured in-hospital management. Intensive supervision and education of residents caring for patients with sepsis is needed in the beginning of training.
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Illness severity modifies the association between sepsis and survival in critically ill patients with acute kidney injury Mahnaz Derakhshan, Mory Ghomshei, Hamid Reza Ansarian Clinical and Experimental Nephrology.2026; 30(4): 581. CrossRef
Objective The use of pediatric length-based weight estimation tapes with precalculated drug doses is advocated by major Advanced Life Support organizations, but concerns have been raised on the accuracy of these systems. The objective of this systematic review was to collect, review, evaluate, and create a synthesis of the current literature to establish whether there is high-quality evidence for use of length-based tapes in accurate drug dose administration. A further objective was to compare these tapes with other dosing aids. Methods Eligible studies were identified and analyzed if they were peer reviewed, full text articles containing original data. Studies including any form of length-based precalculated drug dosing methodology in children aged 0 to 18 years were included. Results Eighteen studies met the inclusion criteria. The most studied of the tapes was the Broselow tape in 16 studies (88.9%). When these tapes were used on their own without additional reference material, they produced a substantial number of potentially harmful dosing errors (>20% error). No tape was superior to another. Using the tapes was better than using no dosing aid but was inferior to using both comprehensive drug dosing guides and novel color-coded medication administration systems. Conclusion There was no high-quality evidence that the use of length-based tapes with precalculated drug doses leads to accurate drug dosing. However, comprehensive drug dosing systems were more effective at reducing dosing errors than were length-based tapes on their own. The confounding effect of weight estimation accuracy on drug dosing accuracy has not been sufficiently studied.
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Clin Exp Emerg Med 2022;9(4):345-353. Published online September 21, 2022
Objective Falls are one of the most frequently occurring adverse events among hospitalized patients. The Morse Fall Scale, which has been widely used for fall risk assessment, has the two limitations of low specificity and difficulty in practical implementation. The aim of this study was to develop and validate an interpretable machine learning model for prediction of falls to be integrated in an electronic medical record (EMR) system.
Methods This was a retrospective study involving a tertiary teaching hospital in Seoul, Korea. Based on the literature, 83 known predictors were grouped into seven categories. Interpretable fall event prediction models were developed using multiple machine learning models including gradient boosting and Shapley values.
Results Overall, 191,778 cases with 272 fall events (0.1%) were included in the analysis. With the validation cohort of 2020, the area under the receiver operating curve (AUROC) of the gradient boosting model was 0.817 (95% confidence interval [CI], 0.720–0.904), better performance than random forest (AUROC, 0.801; 95% CI, 0.708–0.890), logistic regression (AUROC, 0.802; 95% CI, 0.721–0.878), artificial neural net (AUROC, 0.736; 95% CI, 0.650–0.821), and conventional Morse fall score (AUROC, 0.652; 95% CI, 0.570–0.715). The model’s interpretability was enhanced at both the population and patient levels. The algorithm was later integrated into the current EMR system.
Conclusion We developed an interpretable machine learning prediction model for inpatient fall events using EMR integration formats.
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Overcrowding with associated delays in patient care is a problem faced by emergency departments (EDs) worldwide. ED overcrowding can be the result of poor ED department design and prolonged throughput due to staffing, ancillary service performance, and flow processes. As such, the problem may be addressed by process improvements within the ED. A broad body of literature demonstrates that ED overcrowding can be a function of hospital capacity rather than an ED specific issue. Lack of institutional capacity leads to boarding in the ED with resultant ED crowding. This is a problem not solvable by the ED and must be addressed as an institution-wide problem. This paper discusses the causes of ED overcrowding, provides a brief overview of the drastic consequences, and discusses possible cures that have been successfully implemented.
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Man vs. machine: Predicting hospital bed demand from an emergency department Filipe Rissieri Lucini, Mateus Augusto dos Reis, Giovani José Caetano da Silveira, Flavio Sanson Fogliatto, Michel José Anzanello, Giordanna Guerra Andrioli, Rafael Nicolaidis, Rafael Coimbra Ferreira Beltrame, Jeruza Lavanholi Neyeloff, Beatriz D'Agord S PLOS ONE.2020; 15(8): e0237937. CrossRef
Emergency department crowding and hospital transformation during COVID-19, a retrospective, descriptive study of a university hospital in Stockholm, Sweden Björn af Ugglas, Niclas Skyttberg, Andreas Wladis, Therese Djärv, Martin J. Holzmann Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine.2020;[Epub] CrossRef
Emergency Medicine Challenges in Ecuador Andrés Patiño, Santiago Cantillo-Campos, Alexis Kearney, Sean Kivlehan, Augusto Maldonado Western Journal of Emergency Medicine.2020;[Epub] CrossRef
Association between Emergency Department Overcrowding and Mortality at a Teaching Hospital in Saudi Arabia Fatimah Yahyia Khubrani, Mona Faisal Al-Qahtani The Open Public Health Journal.2020; 13(1): 756. CrossRef
Forced Centralized Allocation of Patients to Temporarily ‘Closed’ Emergency Departments Harald Dormann Deutsches Ärzteblatt international.2020;[Epub] CrossRef
Hourly Forecasting of Emergency Department Arrivals – Time Series Analysis Avishek Choudhury SSRN Electronic Journal .2019;[Epub] CrossRef
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