Articles: emergency-services.
-
Individuals experiencing homelessness (IEH) tend to have increased length of stay (LOS) in acute care settings, which negatively impacts health care costs and resource utilisation. It is unclear however, what specific factors account for this increased LOS. This study attempts to define which diagnoses most impact LOS for IEH and if there are differences based on their demographics. ⋯ Homelessness significantly increases the LOS of individuals within both ED and inpatient settings. We have identified several diagnoses that are associated with increased LOS in IE; these should inform the prioritisation and development of targeted interventions to improve the health of IEH.
-
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.
-
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.
-
Emergency departments (ED) must perform patient care at a safe and efficient pace, which requires an effective care team. Communication and workplace practices that foster identification as part of an emergency healthcare team have not been previously demonstrated. ⋯ The findings emphasize the importance of fostering positive communication practices to enhance team dynamics, cohesiveness, and overall well-being within ED healthcare teams. Future research may delve into specific aspects like naming conventions and the role of friendships in healthcare communication.
-
Emerg Med Australas · Feb 2025
Impact of socioeconomic status on utilisation of a Virtual Emergency Department: An exploratory analysis.
To explore whether utilisation of a Virtual Emergency Department (VVED) differs according to socioeconomic status (SES). ⋯ The present study demonstrated a relatively even utilisation of the VVED service across SES population groups. The use of healthcare provider pathways, such as ambulance paramedics, may increase equitable access to telehealth. Clinical attention should be directed toward specific social groups in the emergency care setting.