Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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To achieve high-quality emergency care for pediatric patients nationwide, it is necessary to define the key elements for pediatric emergency medicine (PEM) education and scholarship that would: 1) close the gaps in fundamental PEM education and 2) promote systems and standards that assure an ongoing communication of best practices between tertiary pediatric institutions, general (nonchildren's) hospital emergency departments, and urgent care centers. A working group of medical educators was formed to review the literature, develop a framework for consensus discussion at the breakout session, and then translate their findings into recommendations for future research and scholarship. The breakout session consensus discussion yielded many recommendations. The group concluded that future progress depends on multicenter collaborations as a PEM education research network and a unified vision for PEM education that bridges organizations, providers, and institutions to assure the best possible outcomes for acutely ill or injured children.
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Pediatric patients attended to by emergency medical services (EMS) but not transported to the hospital are an at-risk population. We aimed to evaluate risk factors associated with nontransport by EMS in pediatric patients. ⋯ Pediatric nontransports are associated with traumatic, respiratory, and toxicologic complaints and older age. These findings can facilitate development of refusal protocols and research on outcomes of these at-risk patients.
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The receipt of remote clinical care for children via telecommunications (pediatric telemedicine) appears to improve access to and quality of care in U.S. emergency departments (EDs), but the actual prevalence and characteristics of pediatric telemedicine receipt remain unclear. We determined the prevalence and current applications of pediatric telemedicine in U.S. EDs, focusing on EDs that received telemedicine from clinicians at other facilities. ⋯ Few EDs receive telemedicine for the delivery of pediatric emergency care nationally. Among EDs that do use telemedicine for pediatric care, many report process concerns. Addressing these barriers through focused education or interventions may support EDs in further developing and optimizing this technological adjunct to pediatric emergency care.
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Emergency care providers share a compelling interest in developing an effective patient-centered, outcomes-based research agenda that can decrease variability in pediatric outcomes. The 2018 Academic Emergency Medicine Consensus Conference "Aligning the Pediatric Emergency Medicine Research Agenda to Reduce Health Outcome Gaps (AEMCC)" aimed to fulfill this role. ⋯ Topics that were explored and deliberated through subcommittee breakout sessions led by content experts included 1) pediatric emergency medical services research, 2) pediatric emergency medicine (PEM) research network collaboration, 3) PEM education for emergency medicine providers, 4) workforce development for PEM, and 5) enhancing collaboration across emergency departments (PEM practice in non-children's hospitals). The work product of this conference is a research agenda that aims to identify areas of future research, innovation, and scholarship in PEM.
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Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization. Earlier identification of need for hospital-level care could triage patients more efficiently to high- or low-resource ED tracks. Existing tools to predict disposition for pediatric asthma use only clinical data, perform best several hours into the ED stay, and are static or score-based. Machine learning offers a population-specific, dynamic option that allows real-time integration of available nonclinical data at triage. Our objective was to compare the performance of four common machine learning approaches, incorporating clinical data available at the time of triage with information about weather, neighborhood characteristics, and community viral load for early prediction of the need for hospital-level care in pediatric asthma. ⋯ Three of the four machine learning models performed well with decision trees preforming the worst. The gradient boosting machines model demonstrated a slight advantage over other approaches at predicting need for hospital-level care at the time of triage in pediatric patients presenting with asthma exacerbation. The addition of weight, SES, and weather data improved the performance of this model.