The American journal of emergency medicine
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The use of acute hospital-level care at home (hospital-at-home) for patients who are chronically ill has led to decreased medical costs, amount of sedentary time, and hospital admissions. Our large integrated healthcare system identified the need to develop a mechanism through which to decrease emergency department (ED) visits in this patient population by creating a home acute care program called Urgent Dispatch. The primary objective of this study was to determine the medical condition for referral and seven and 30-day ED visit rates. ⋯ A home-based care model of healthcare delivery for patients with chronic medical conditions can provide effective care, with 80.2 % of patients avoiding an ED visit within seven days and 68.2 % avoiding an ED visit within 30 days.
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Given the increasing proportion of patients and caregivers who use languages other than English (LOE) at our institution and across the U.S, we evaluated key workflow and outcome measures in our emergency department (ED) for patients and caregivers who use LOE. ⋯ These results highlight the need for better language documentation and understanding of factors contributing to extended stays and increased revisits for pediatric patients and caregivers who use LOE.
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Despite advancements in antimicrobial therapies, bacteremia remains a life-threatening condition. Appropriate antimicrobials must be promptly administered to ensure patient survival. However, diagnosing bacteremia based on blood cultures is time-consuming and not something emergency department (ED) personnel are routinely trained to do. ⋯ The ML models developed effectively predicted bacteremia among febrile or hypothermic patients in the ED, with all models demonstrating high AUROC values and rapid processing times. The findings suggest that ED clinicians can effectively utilize ML techniques to develop predictive models for addressing clinical challenges.
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Limited data are available on locations of public access defibrillation programs across communities in the United States, despite their widespread presence. Our goal was to determine publicly available AED locations of large businesses in a mixed urban-rural county. We then compared our survey results to a NC state-mandated AED registry and the county's emergency medical dispatch center AED registry. ⋯ Our survey yielded a response rate of 79.1 % and identified 411 businesses with ≥ 1 AEDs. An additional 162 AED locations were contained in AED lists from multi-building organizations and registries. In total, our canvas identified 963 AEDs at 573 unique locations. The majority of AEDs (65.1 % [627/963]) were not previously registered in the NC OEMS AED registry. Few identified AEDs (11.8 % [114/963]) were listed in the county emergency medical dispatch center registry.
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Artificial intelligence (AI) is becoming increasingly integral in clinical practice, such as during imaging tasks associated with the diagnosis and evaluation of blunt chest trauma (BCT). Due to significant advances in imaging-based deep learning, recent studies have demonstrated the efficacy of AI in the diagnosis of BCT, with a focus on rib fractures, pulmonary contusion, hemopneumothorax and others, demonstrating significant clinical progress. ⋯ Here, we provide a review of the available evidence surrounding the potential utility of AI in BCT, and additionally identify the challenges impeding its development. This review offers insights on how to optimize the role of AI in the diagnostic evaluation of BCT, which can ultimately enhance patient care and outcomes in this critical clinical domain.