The American journal of emergency medicine
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Artificial intelligence (AI) in healthcare is the ability of a computer to perform tasks typically associated with clinical care (e.g. medical decision-making and documentation). AI will soon be integrated into an increasing number of healthcare applications, including elements of emergency department (ED) care. Here, we describe the basics of AI, various categories of its functions (including machine learning and natural language processing) and review emerging and potential future use-cases for emergency care. ⋯ AI could also help provide focused summaries of charts, summarize encounters for hand-offs, and create discharge instructions with an appropriate language and reading level. Additional use cases include medical decision making for decision rules, real-time models that predict clinical deterioration or sepsis, and efficient extraction of unstructured data for coding, billing, research, and quality initiatives. We discuss the potential transformative benefits of AI, as well as the concerns regarding its use (e.g. privacy, data accuracy, and the potential for changing the doctor-patient relationship).
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Randomized Controlled Trial Comparative Study
Ultrasound-guided pericapsular nerve block compared with IV opioids in hip injuries: A randomised controlled trial.
The study aimed to compare the analgesic effect of USG-guided PENG (Peri capsular nerve group) block with Intravenous Nalbuphine hydrochloride (IVN) in patients with hip fracture coming to the emergency department (ED). The purpose was also to monitor the adverse effects and rescue analgesic requirements in both treatment modalities. ⋯ The study provides evidence that the ultrasound-guided PENG block has a better analgesic effect and has fewer adverse events than IV opioids in patients with HF.
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Emergency physicians (EPs) navigate high-pressure environments, making rapid decisions amidst ambiguity. Their choices are informed by a complex interplay of experience, information, and external forces. While cognitive shortcuts (heuristics) expedite assessments, there are multiple ways they can be subtly manipulated, potentially leading to reflexive control: external actors steering EPs' decisions for their own benefit. ⋯ Recognizing these dangers empowers EPs to resist reflexive control through (1) critical thinking: examining information for potential biases and prioritizing evidence-based practices, (2) continuous education: learning about cognitive biases and mitigation strategies, and (3) institutional policies: implementing regulations to reduce external influence and to promote transparency. This vulnerability of emergency medicine decision making highlights the need for awareness, education, and robust ethical frameworks. Understanding reflexive control techniques is crucial for safeguarding patient care and promoting independent, ethical decision making in emergency medicine.