The American journal of emergency medicine
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Observational Study
Diagnostic accuracy of artificial intelligence for identifying systolic and diastolic cardiac dysfunction in the emergency department.
Cardiac point-of-care ultrasound (POCUS) can evaluate for systolic and diastolic dysfunction to inform care in the Emergency Department (ED). However, accurate assessment can be limited by user experience. Artificial intelligence (AI) has been proposed as a model to increase the accuracy of cardiac POCUS. However, there is limited evidence of the accuracy of AI in the clinical environment. The objective of this study was to determine the diagnostic accuracy of AI for identifying systolic and diastolic dysfunction compared with expert reviewers. ⋯ When compared with expert assessment, AI had high sensitivity and specificity for diagnosing both systolic and diastolic dysfunction.
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Each year millions of children seek care in emergency departments, many of whom are from marginalized and minoritized groups who lack access to primary and preventive care. Law enforcement personnel are also commonly present in pediatric and adult emergency departments serving in a range of roles. Therefore, pediatric emergency departments sit at the nexus of the health system and the legal system for many vulnerable youth and families. ⋯ Pediatric clinicians, highly trained professionals in the medical and social care of youth and children, are often unaware of legal rules and procedures that guide law enforcement interaction with youth. This lack of knowledge may result in unknowing and unwitting violations of patients' rights while also compromising the quality of health care provided. Therefore, it is imperative that clinicians are educated on their roles and their institutions' roles in safeguarding patients' privacy and autonomy while still promoting effective collaboration with law enforcement.
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This study investigated the feasibility of using the Roth score in the emergency setting to make hospitalization or discharge decisions for patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). ⋯ The Roth score (only counts) increased in discharged patients after AECOPD treatment. It appears to be a viable method for predicting hospitalization or discharge decisions in patients with AECOPD who present to the emergency department.
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80 % of Americans wish to die somewhere other than a hospital, and hospice is an essential resource for providing such care. The emergency department (ED) is an important location for identifying patients with end-of-life care needs and providing access to hospice. The objective of this study was to analyze a quality improvement (QI) program designed to increase the number of patients referred directly to hospice from the ED, without the need for an observation stay and without access to in-hospital hospice. ⋯ In this largest study to date on direct ED-to-hospice discharges, a QI program focused on workflow optimization, education, and EMR modification was insufficient to significantly impact ED-to-hospice discharges. Future efforts to increase hospice transitions from the ED should investigate methods to improve patient identification, the impact of in-hospital hospice programs, and coordination with hospital and community teams to support home-based care for those desiring to remain there.