Articles: hospital-emergency-service.
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To characterize patients who left without being seen (LWBS) from a Canadian pediatric Emergency Department (ED) and create predictive models using machine learning to identify key attributes associated with LWBS. ⋯ Our analysis showed that machine learning models can be used on administrative data to predict patients who LWBS in a Canadian pediatric ED. From 16 variables, we identified the five most influential model attributes. System-level interventions to improve patient flow have shown promise for reducing LWBS in some centres. Predicting patients likely to LWBS raises the possibility of individual patient-level interventions to mitigate LWBS.
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Appropriate decision-making is critical for transfusions to prevent unnecessary adverse outcomes; however, transfusion in the emergency department (ED) can only be decided based on sparse evidence in a limited time window. ⋯ In this single-center retrospective study, younger age and higher ED triage scores were associated with the appropriateness of RBC transfusions.