Articles: hospital-emergency-service.
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Pediatric emergency care · Feb 2023
Observational StudyRisk Factors for Pediatric Sepsis in the Emergency Department: A Machine Learning Pilot Study.
To identify underappreciated sepsis risk factors among children presenting to a pediatric emergency department (ED). ⋯ Machine learning models trained to identify pediatric sepsis using ED clinical and sociodemographic variables confirmed well-established predictors, including heart rate and mean arterial pressure, and identified underappreciated relationships between sepsis and patient age, immunization status, and demographics.
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Observational Study
Community First Responders' Contribution to Emergency Medical Service Provision in the United Kingdom.
We aimed to investigate community first responders' contribution to emergency care provision in terms of number, rate, type, and location of calls and characteristics of patients attended. ⋯ In the United Kingdom, community first responders contribute to the delivery of emergency medical services, particularly in rural areas and especially for more urgent calls. The work of community first responders has expanded from their original purpose-to attend to out-of-hospital cardiac arrests. The future development of community first responders' schemes should prioritize training for a range of conditions, and further research is needed to explore the contribution and potential future role of the community first responders from the perspective of service users, community first responders' schemes, ambulance services, and commissioners.
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Advanced practice providers (APPs) comprise an increasing proportion of the emergency medicine (EM) workforce, particularly in rural geographies. With little known regarding potential expanding practice patterns, we sought to evaluate trends in independent emergency care services billed by APPs from 2013 to 2019. ⋯ In 2019, APPs billed independent services for approximately one in six high-acuity ED encounters in rural geographies and one in 11 high-acuity ED encounters in urban geographies, and well over one-third of the average APPs' encounters were for high-acuity E/M services. Given differences in training and reimbursement between clinician types, these estimates suggest further work is needed evaluating emergency care staffing decision making.
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Severe hypertension can accompany neurological symptoms without obvious signs of target organ damage. However, acute cerebrovascular events can also be a cause and consequence of severe hypertension. We therefore use US population-level data to determine prevalence and clinical characteristics of patients with severe hypertension and neurological complaints. ⋯ In a nationally representative survey, one-in-sixteen ED patients had severely elevated BP and one-fifth of those patients had neurological complaints.
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A machine-learning model trained to recognize emergency calls regarding Out-of-Hospital Cardiac Arrest (OHCA) was tested in clinical practice at Copenhagen Emergency Medical Services (EMS) from September 2018 to December 2019. We aimed to investigate emergency call characteristics where the machine-learning model failed to recognize OHCA or misinterpreted a call as being OHCA. ⋯ Continuous optimization of the language model is needed to improve the prediction of OHCA and thereby improve sensitivity and specificity of the machine-learning model on recognising OHCA in emergency telephone calls.