Annals of emergency medicine
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Review
Mental Practice: Applying Successful Strategies in Sports to the Practice of Emergency Medicine.
Emergency physicians are expected to learn and maintain a large and varied set of competencies for clinical practice. These include high acuity, low occurrence procedures that may not be encountered frequently in the clinical environment and are difficult to practice with high fidelity and frequency in a simulated environment. ⋯ In this article, we review the literature on mental practice in sports and medicine as well as the underlying neuroscientific theories that support its use. We review best-known practices and provide a framework to design and use mental imagery scripts to augment learning and maintaining the competencies necessary for physicians at all levels of training and clinical environments in the practice of emergency medicine.
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Inappropriate antibiotic prescribing for acute respiratory tract infections is a common source of low-value care in the emergency department (ED). Racial and socioeconomic disparities have been noted in episodes of low-value care, particularly in children. We evaluated whether prescribing rates for acute respiratory tract infections when antibiotics would be inappropriate by guidelines differed by race and socioeconomics. ⋯ Our results suggest that although overall inappropriate prescribing was relatively low, White patients and patients from wealthier areas were more likely to receive an inappropriate antibiotic prescription.
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In the coming years, artificial intelligence (AI) and machine learning will likely give rise to profound changes in the field of emergency medicine, and medicine more broadly. This article discusses these anticipated changes in terms of 3 overlapping yet distinct stages of AI development. It reviews some fundamental concepts in AI and explores their relation to clinical practice, with a focus on emergency medicine. In addition, it describes some of the applications of AI in disease diagnosis, prognosis, and treatment, as well as some of the practical issues that they raise, the barriers to their implementation, and some of the legal and regulatory challenges they create.
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The workload of clinical documentation contributes to health care costs and professional burnout. The advent of generative artificial intelligence language models presents a promising solution. The perspective of clinicians may contribute to effective and responsible implementation of such tools. This study sought to evaluate 3 uses for generative artificial intelligence for clinical documentation in pediatric emergency medicine, measuring time savings, effort reduction, and physician attitudes and identifying potential risks and barriers. ⋯ Pediatric emergency medicine attendings in our study perceived that ChatGPT can deliver high-quality summaries while saving time and effort in many scenarios, but not all.
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This study aimed to (1) develop and validate a natural language processing model to identify the presence of pulmonary embolism (PE) based on real-time radiology reports and (2) identify low-risk PE patients based on previously validated risk stratification scores using variables extracted from the electronic health record at the time of diagnosis. The combination of these approaches yielded an natural language processing-based clinical decision support tool that can identify patients presenting to the emergency department (ED) with low-risk PE as candidates for outpatient management. ⋯ This study demonstrates that a natural language processing-based model utilizing real-time radiology reports can accurately identify patients with PE. Further, this model, used in combination with a validated risk stratification score (sPESI), provides a clinical decision support tool that accurately identifies patients in the ED with low-risk PE as candidates for outpatient management.