CJEM
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Artificial intelligence (AI) offers opportunities for managing the complexities of clinical care in the emergency department (ED), and Clinical Decision Support has been identified as a priority application. However, there is a lack of published guidance on how to rigorously develop and evaluate these tools. We sought to answer the question, "What methodological standards should be applied to the development of AI-based Clinical Decision Support tools in the ED?". ⋯ These 11 recommendations provide guiding principles and methodological standards for emergency medicine researchers to rigorously develop AI-based Clinical Decision Support tools and for clinicians to gain knowledge and trust in using them.
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Pediatric resuscitations involving shock and trauma are rare but they are high-stakes events in the pediatric emergency department (ED). Effective use of point-of-care ultrasound (POCUS) can expedite diagnosis and treatment in such cases. This study aimed to assess the impact of a longitudinal pediatric emergency medicine simulation curriculum and high-fidelity POCUS simulator on residents' clinical practice, comfort level, and motivation to learn resuscitative ultrasound. ⋯ Our study demonstrated that a longitudinal, simulation-based curriculum focused on resuscitative ultrasound increased residents' confidence, their motivation and likelihood of using these skills in the clinical setting. Repeated simulation exposures to resuscitative ultrasound can help participants translate this critical skill into use at the bedside, especially in high-acuity low-occurrence events.