AIMC Topic: Triage
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Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning.
BMC medical informatics and decision making
Jul 28, 2025
INTRODUCTION: Emergency departments (EDs) are critical for ensuring timely patient care, especially in triage, where accurate prioritisation is essential for patient safety and resource utilisation. Building on previous research, this study leverages...
Predicting patient risk of leaving without being seen using machine learning: a retrospective study in a single overcrowded emergency department.
BMC emergency medicine
Jul 15, 2025
Emergency department (ED) overcrowding has become a critical issue in hospital management, leading to increased patient wait times and higher rates of individuals leaving without being seen (LWBS). This study aims to identify key factors influencing ...
Assessing the Accuracy of ChatGPT in Appropriately Triaging Common Postoperative Concerns Regarding Mohs Micrographic Surgery.
JMIR dermatology
Jul 8, 2025
Artificial intelligence (AI) is increasingly integrated into health care, offering potential benefits in patient education, triage, and administrative efficiency. This study evaluates AI-driven dialogue interfaces within an electronic health record a...
Machine learning to improve predictive performance of prehospital early warning scores.
Scientific reports
Jul 1, 2025
Early warning scores are used to assess acute patients' risk of being in a critical situation, allowing for early appropriate treatment, avoiding critical outcomes. The early warning scores use changes in vital signs to provide an assessment, however...
Urgency vs triage prioritisation: appropriateness of referrer-rated urgency of referrals to a public dermatology service.
The New Zealand medical journal
Jun 27, 2025
AIM: To characterise the appropriateness of community referrer-rated urgency among dermatology referrals.
Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments.
BMJ health & care informatics
Jun 26, 2025
OBJECTIVE: Machine learning (ML) models have emerged as tools to predict length of stay (LOS) and disposition decision (DD) in emergency departments (EDs) to combat overcrowding. However, site-specific ML models are not transferable to different site...
A Machine Learning Trauma Triage Model for Critical Care Transport.
JAMA network open
Jun 2, 2025
IMPORTANCE: Under austere prehospital conditions, rapid classification of injured patients for intervention or transport is essential for providing lifesaving care. Discerning which patients need care most urgently further allows for optimal allocati...
Patient Triage and Guidance in Emergency Departments Using Large Language Models: Multimetric Study.
Journal of medical Internet research
May 15, 2025
BACKGROUND: Emergency departments (EDs) face significant challenges due to overcrowding, prolonged waiting times, and staff shortages, leading to increased strain on health care systems. Efficient triage systems and accurate departmental guidance are...
Validation of an Artificial Intelligence-Powered Virtual Assistant for Emergency Triage in Neurology.
The neurologist
May 1, 2025
OBJECTIVES: Neurological emergencies pose significant challenges in medical care in resource-limited countries. Artificial intelligence (AI), particularly health chatbots, offers a promising solution. Rigorous validation is required to ensure safety ...
The role of AI in emergency department triage: An integrative systematic review.
Intensive & critical care nursing
Apr 29, 2025
BACKGROUND: Overcrowding in emergency departments (EDs) leads to delayed treatments, poor patient outcomes, and increased staff workloads. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to optimize triage.
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