Emergency Department (ED) overcrowding is a major issue for the efficient management of patients. To this end, triage algorithms have been developed to support the task of patient prioritization. In this paper an ontology was designed to represent the knowledge about patient triage procedure in EDs.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.3233/SHTI210362 | DOI Listing |
Am J Emerg Med
December 2024
Department of Emergency Medicine, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Turkey.
Background: The number of emergency department (ED) visits has been on steady increase globally. Artificial Intelligence (AI) technologies, including Large Language Model (LLMs)-based generative AI models, have shown promise in improving triage accuracy. This study evaluates the performance of ChatGPT and Copilot in triage at a high-volume urban hospital, hypothesizing that these tools can match trained physicians' accuracy and reduce human bias amidst ED crowding challenges.
View Article and Find Full Text PDFSleep Med
December 2024
Department of Psychiatry & Division of Sleep Medicine, AIIMS Rishikesh, India.
Among the mental health outcomes and disaster types (determined by damage to life, property, long-term consequences, displacement, and unpredictability), floods are associated with anxiety and sleep problems, mudslides with anxiety and mood disturbance, volcanic eruptions with acute stress reactions, and earthquakes with anxiety, depression, and physical complaints. Disasters such as tunnel collapse are unique as it involves the healthy, without loss of personal property or displacement; hence, they can have very different health-related outcomes. In this study, we explore mental health and sleep-related issues in workers rescued from an under-construction collapsed tunnel trapped for 17 days.
View Article and Find Full Text PDFJ Pers Med
December 2024
Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark.
Artificial intelligence (AI) is becoming increasingly influential in ophthalmology, particularly through advancements in machine learning, deep learning, robotics, neural networks, and natural language processing (NLP). Among these, NLP-based chatbots are the most readily accessible and are driven by AI-based large language models (LLMs). These chatbots have facilitated new research avenues and have gained traction in both clinical and surgical applications in ophthalmology.
View Article and Find Full Text PDFJ Pers Med
December 2024
UWA Dental School, The University of Western Australia, Nedlands, WA 6009, Australia.
This study evaluated the accuracy of diagnosing oral and maxillofacial diseases using telehealth. We recruited 100 patients from the Oral Health Centre of Western Australia. They were either new patients or existing patients with a condition not previously diagnosed.
View Article and Find Full Text PDFFront Pediatr
December 2024
Faculty of Health, Universidad del Valle, Cali, Colombia.
Background: Pediatric trauma is a major global health concern, accounting for a substantial proportion of deaths and disease burden from age 5 onwards. Effective triage and management are essential in pediatric trauma care, and prediction models such as the Trauma Injury Severity Score (TRISS) play a crucial role in estimating survival probability and guiding quality improvement. However, TRISS does not account for age-specific factors in pediatric populations, limiting its applicability to younger patients.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!