Articles: emergency-medical-services.
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Observational Study
Enhancing Bystander Intervention: Insights from the Utstein Analysis of Out-of-Hospital Cardiac Arrests in Slovenia.
Background and Objectives: Out-of-hospital cardiac arrest (OHCA) and survival is a pressing matter all around the world. Despite years of research and great strides and advancements, survival remains alarmingly low. The aim of this study was to measure the survival and characteristics of patients having an OHCA in Slovenia, with an in-depth look at how the bystanders affect the return of spontaneous circulation (ROSC) and survival of OHCA. ⋯ Our data show that bystanders do not significantly improve survival. This represents an untapped potential of general public education in cardiopulmonary resuscitation and automatic external defibrillator use. Following good practices from abroad and improving layperson CPR knowledge could further improve OHCA survival.
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Psychiatric conditions are one of the leading non-battle injury diseases resulting in medical evacuation (MEDEVAC) from combat environments. The challenge of limited MEDEVAC capability necessitating prolonged field care in future large-scale combat operations must be addressed. Therefore, a robust program is needed to address frontline care of behavioral health (BH), maximizing service members returning to duty and minimizing MEDEVAC. This review summarizes the literature on the impacts of the Emergency Psychiatric Assessment, Treatment, and Healing (EmPATH) Unit program as a solution to the challenges of treating behavioral health in future wars. ⋯ This is the first literature review to consider EmPATH units for psychiatric prolonged field care based on its advantages demonstrated in the civilian sector. Studies have yet to be done on EmPATH units' usefulness in the military, showing a knowledge gap in current evidence supporting its suitability. Thus, this review recommends further studies of EmPATH units in military settings, especially prolonged field care environments.
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This study assesses the feasibility, inter-rater reliability, and accuracy of using OpenAI's ChatGPT-4 and Google's Gemini Ultra large language models (LLMs), for Emergency Medical Services (EMS) quality assurance. The implementation of these LLMs for EMS quality assurance has the potential to significantly reduce the workload on medical directors and quality assurance staff by automating aspects of the processing and review of patient care reports. This offers the potential for more efficient and accurate identification of areas requiring improvement, thereby potentially enhancing patient care outcomes. ⋯ Large language models demonstrate potential in supporting quality assurance by effectively and objectively extracting data elements. However, their accuracy in interpreting non-standardized and time-sensitive details remains inferior to human evaluators. Our findings suggest that current LLMs may best offer supplemental support to the human review processes, but their current value remains limited. Enhancements in LLM training and integration are recommended for improved and more reliable performance in the quality assurance processes.