Emergency medicine Australasia : EMA
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Emerg Med Australas · Feb 2024
Prevalence of alcohol and other drug detections in non-transport injury events.
To measure the prevalence of alcohol and/or other drug (AOD) detections in suspected major trauma patients with non-transport injuries who presented to an adult major trauma centre. ⋯ AOD detections were common in trauma patients with non-transport injury causes. Population-level surveillance is needed to inform prevention strategies that address AOD use as a significant risk factor for serious injury.
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Emerg Med Australas · Feb 2024
Paediatric diabetes-related presentations to emergency departments in Victoria, Australia from 2008 to 2018.
Despite significant treatment advances in paediatric diabetes management, ED presentations for potentially preventable (PP) complications such as diabetic ketoacidosis (DKA) remains a major issue. We aimed to examine the characteristics, rates and trends of diabetes-related ED presentations and subsequent admissions in youth aged 0-19 years from 2008 to 2018. ⋯ Although the rates of diabetes-related ED presentations declined, PP diabetes-related presentations and subsequent hospitalisation remain high. Patient level research is required to understand the increased DKA presentations in rural youth.
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Emerg Med Australas · Feb 2024
Machine learning in clinical practice: Evaluation of an artificial intelligence tool after implementation.
Artificial intelligence (AI) has gradually found its way into healthcare, and its future integration into clinical practice is inevitable. In the present study, we evaluate the accuracy of a novel AI algorithm designed to predict admission based on a triage note after clinical implementation. This is the first of such studies to investigate real-time AI performance in the emergency setting. ⋯ Our study showed the diagnostic evaluation of a real-time AI clinical decision-support tool became less accurate than the original. Although real-time sensitivity and specificity of the AI tool was still acceptable as a decision-support tool in the ED, we propose that continuous training and evaluation of AI-enabled clinical support tools in healthcare are conducted to ensure consistent accuracy and performance to prevent inadvertent consequences.