Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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Diagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must make rapid decisions with limited information, often under cognitive overload. Artificial intelligence (AI) offers promising solutions to improve diagnostic errors in three key areas: information gathering, clinical decision support (CDS), and feedback through quality improvement. ⋯ Developing, validating, and implementing AI as a safe, human-centered ED tool requires thoughtful design and meticulous attention to ethical and practical considerations. Clinicians and patients must be integrated as key stakeholders across these processes. Ultimately, AI should be seen as a tool that assists clinicians by supporting better, faster decisions and thus enhances patient outcomes.
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Acute exacerbations of chronic obstructive pulmonary disease (COPD) in the emergency department (ED) involve dyspnea, cough, and chest discomfort; frequent exacerbations are associated with increased mortality and reduced quality of life. Noninvasive positive pressure ventilation (NiPPV) is commonly used to help relieve symptoms but is limited due to patient intolerance. We aimed to determine whether high-velocity nasal insufflation (HVNI) is noninferior to NiPPV in relieving dyspnea within 4 h in ED patients with acute hypercapnic respiratory failure. ⋯ In participants with symptomatic COPD, HVNI was noninferior to NiPPV in relieving dyspnea 4 h after therapy initiation. HVNI may be a reasonable treatment option for some patients experiencing moderate acute exacerbations of COPD in the ED.