Articles: emergency-services.
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Ambulance ramping, the delay to transfer of a patient arriving at an ED by ambulance into an ED treatment space and handover of care to ED clinicians, is a problem in all Australian states and territories and New Zealand. It is a symptom of ED overcrowding and access block and has been associated with adverse health outcomes for some patient groups. The questions arise, who might be legally responsible for the care of patients who are ramped and does their physical location matter? The short answers are 'everyone' and 'no', however, whether there will be a breach of duty depends on the reasonableness of responses and resource allocation considerations.
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Emerg Med Australas · Feb 2025
Low-acuity emergency department presentation characteristics and their association with Medicare-subsidised general practitioner services across New South Wales: A data linkage study.
Identify clinical and demographic characteristics of low-acuity presentations (LAPs) to the ED and analyse correlations between population rates of LAPs to ED and rates of Medicare-subsidised general practitioner (GP) services across statistical areas. ⋯ A relationship between LAPs to ED and Medicare-subsidised GP episodes of care exists for non-metropolitan but not metropolitan areas.
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Emerg Med Australas · Feb 2025
Triage gap? Analysis of admission rates, service utilisation and mortality for First Nations patients compared to non-First Nations patients, stratified by ED triage category.
First Nations patients often experience poorer health outcomes than non-First Nations patients. Despite emergency triage primarily focusing on severity, implying comparable outcomes for patients in the same triage group regardless of demographics, the precision of triage for First-Nations Australians may be undermined by multiple factors, although research in this area is scarce. ⋯ First Nations patients appear to have worse outcomes than non-First Nations patients in the same triage category. Socio-economic factors and high discharge against advice rates from wards may explain the significantly higher admission rate. Under-recognition of serious illness at triage could be attributed to communication issues or a 'well bias'. The results raise many questions and further investigation is required.
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Emerg Med Australas · Feb 2025
How useful was a paediatric physical abuse screening project in a rural Australian emergency department?
Children with non-accidental injuries have increased risk of future death. There is insufficient evidence for widespread physical abuse screening tool use in the ED. This study assesses the utility of a physical abuse project that includes the implementation of a screening tool with case-matching from multiple sources. It aims to confirm whether risk-screening in a medium-sized rural Australian ED is reliable and will improve outcomes. ⋯ Implementing this ED paediatric physical abuse project improved safety behaviours and best-practice documentation. The tool improved medical decision making without increased re-presentations. ED clinicians may use similar CPAs to help review safety concerns and facilitate discharge; however, resources are needed to investigate referrals flagged due to false-positive rates.
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Yonsei medical journal · Feb 2025
Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data.
Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using high-resolution biosignals collected within 4 h of arrival. ⋯ Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.