When given a sample of 100 emergency department discharge instructions, Claude Sonnet, a large language model, produced accurate Spanish translations as evaluated by Spanish-speaking physicians and medical interpreters.
OBJECTIVES: Emergency department crowding refers to a complex state of congestion associated with a set of performance indicators such as occupation levels, waiting times and specific scores. Among current methods to model it, an objective gap exists...
BACKGROUND: In an ophthalmology emergency department, determining treatment urgency is crucial for patient safety and the efficient use of resources. The aim of this study was to use artificial intelligence to develop a neural network and evaluate it...
BACKGROUND: Sepsis poses a significant threat in emergency settings, necessitating tools for early and interpretable risk assessment. This study aimed to develop a robust explainable boosting machine (EBM) model, one of the explainable artificial int...
BACKGROUND: Artificial intelligence (AI) tools could assist emergency doctors interpreting chest X-rays to inform urgent care. However, the impact of AI assistance on clinical decision-making, a precursor to enhanced care and patient outcomes, remain...
BACKGROUND: Emergency departments (EDs) routinely screen for fall risk, but patients are rarely notified of their results or referred to preventive resources. There is a critical need for an intervention that notifies patients when they are at risk f...
UNLABELLED: Large language models (LLMs) like ChatGPT are being explored as clinical decision support tools, but their reliability in pediatric acute care remains uncertain. This pilot study assessed ChatGPT-4.0's performance in the early management ...
Physicians face a significant documentation burden, spending twice as much time on electronic health records (EHRs) as on direct patient care. Consultation summary reports from the emergency department (ED) are critical for continuity of care and cli...
BACKGROUND: Emergency department (ED) crowding is often attributed to a slow hospitalization process, leading to reduced quality of care. Predicting early disposition in patients presenting with cardiac issues is challenging: most are ultimately disc...
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