The American journal of emergency medicine
-
Observational Study
Exploring ChatGPT's potential in ECG interpretation and outcome prediction in emergency department.
Approximately 20 % of emergency department (ED) visits involve cardiovascular symptoms. While ECGs are crucial for diagnosing serious conditions, interpretation accuracy varies among emergency physicians. Artificial intelligence (AI), such as ChatGPT, could assist in ECG interpretation by enhancing diagnostic precision. ⋯ ChatGPT demonstrates moderate accuracy in ECG interpretation, yet its current limitations, especially in assessing critical cases, restrict its clinical utility in ED settings. Future research and technological advancements could enhance AI's reliability, potentially positioning it as a valuable support tool for emergency physicians.
-
Early recognition of sepsis is essential for timely initiation of adequate care. However, this is challenging as signs and symptoms may be absent or nonspecific. The cascade of events leading to organ failure in sepsis is characterized by immune-metabolic alterations. Volatile organic compounds (VOCs) are metabolic byproducts released in expired air. We hypothesize that measuring the VOC profile using electronic nose technology (eNose) could improve early recognition of sepsis. ⋯ The study is embedded in the Acutelines data-biobank (www.acutelines.nl), registered in Clinicaltrials.gov (NCT04615065).
-
Observational Study
Pragmatic evaluation of point of care lung ultrasound for the triage of COVID-19 patients using a simple scoring matrix: Intraclass-classification and predictive value.
The value of routine bedside lung ultrasound (LUS) for predicting patient disposition during visits to the Emergency Department (ED) is difficult to quantify. We hypothesized that a simplified scoring of bedside-acquired LUS images for the triage of acute respiratory symptoms in the ED would be associated with patient disposition. ⋯ A simplified scoring of bedside-acquired LUS images from patients with acute respiratory symptoms at the emergency department reliably predicts patient disposition.
-
Observational Study
Effect of a best practice advisory activated "kit in hand" naloxone distribution program in the emergency department.
We implemented a "kit in hand" naloxone distribution program at emergency department (ED) discharge activated by electronic health record Best Practice Advisory (BPA). The purpose of this study was to evaluate naloxone kit distribution before and after implementation. ⋯ Implementation of a BPA-activated kit in hand naloxone distribution program increases the rate of successful naloxone distribution to patients presenting to the ED following unintentional opioid overdose, a subpopulation at very high risk for recurrence of overdose. Opportunities for program improvement were identified as there were instances where kits were intended to be distributed but barriers in the process existed.
-
Transfer of patients between hospitals is common, costly, and over 20 % are estimated to be avoidable, meaning patients do not receive specialized interventions once transferred. Older adults are more likely to be transferred and may be at increased risk for developing delirium or other complications due to transfer. We aimed to determine the frequency of potentially avoidable transfer (PAT) among older adults; identify conditions most likely to involve a PAT; and describe factors associated with PAT. ⋯ PATs were common in transfers of older adults, particularly among a subset of neurologic, cardiovascular, and injury-related conditions. These conditions may represent ideal targets for intervention to decrease rates of avoidable transfer. Research exploring hospital variation in transfer practices and the impact of PAT on older adults' health outcomes are also needed.