Articles: emergency-medicine.
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Review Meta Analysis
Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.
Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting. ⋯ Our analysis revealed a generally favorable performance accuracy in using NLP within EM research, particularly in the realm of radiologic interpretation. Consequently, we advocate for the adoption of NLP-based research to augment EM health care management.
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Indigenous health equity interventions situated within emergency care settings remain underexplored, despite their potential to influence patient care satisfaction and empowerment. This study aimed to systematically review and identify Indigenous equity interventions and their outcomes within acute care settings, which can potentially be utilized to improve equity within Canadian healthcare for Indigenous patients. ⋯ Acute care settings, serving as the primary point of access to health care for many Indigenous populations, are well-positioned to implement health equity interventions such as cultural safety training, Indigenous knowledge integration, and optimization of waiting room environments, combined with sustainable evaluation methods. Participatory discussions with Indigenous communities are needed to advance this area of research and determine which interventions are relevant and appropriate for their local context.
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Comparative Study
Comparative analysis of ChatGPT, Gemini and emergency medicine specialist in ESI triage assessment.
The term Artificial Intelligence (AI) was first coined in the 1960s and has made significant progress up to the present day. During this period, numerous AI applications have been developed. GPT-4 and Gemini are two of the best-known of these AI models. As a triage system The Emergency Severity Index (ESI) is currently one of the most commonly used for effective patient triage in the emergency department. The aim of this study is to evaluate the performance of GPT-4, Gemini, and emergency medicine specialists in ESI triage against each other; furthermore, it aims to contribute to the literature on the usability of these AI programs in emergency department triage. ⋯ In conclusion, our study shows that both GPT-4 and Gemini can accurately triage critical and urgent patients in ESI 1&2 groups at a high rate. Furthermore, GPT-4 has been more successful in ESI triage for all patients. These results suggest that GPT-4 and Gemini could assist in accurate ESI triage of patients in emergency departments.
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Case Reports
Persistent headache without neurologic deficit from a spontaneous vertebral artery dissection: A case report.
Non-traumatic headache is a common complaint seen in the emergency department (ED), accounting for 2.3% of ED visits per year in the United States (Munoz-Ceron et al., 2019). When approaching the workup and management of headache, an emergency medicine physician is tasked with generating a deadly differential by means of a thorough history and physical exam to determine the next best steps. ⋯ Vertebral artery dissection should remain high on the differential for an emergency medicine physician when history is suggestive of a new onset headache, preceded by vertiginous symptoms. An absence of recent trauma and a normal neurologic examination does not eliminate the diagnosis.
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Proficiency in Quality Improvement and Patient Safety (QIPS) methodologies has been identified as a standard of residency training. However, there is no consensus on how to achieve these competencies. We used Kern's model of curricular development to create a QIPS curriculum for the local Emergency Medicine (EM) residency training program. ⋯ The curriculum was delivered to a mix of local transition to practice residents and faculty members. Participants reported favorable outcomes and objectively demonstrated QIPS knowledge acquisition. This curriculum serves as a model that could be adapted by other residency training programs seeking to implement their own QIPS curricula.