Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions.

Return visit admissions (RVA), which are instances where patients discharged from the emergency department (ED) rapidly return and require hospital admission, have been associated with quality issues and adverse outcomes. We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. Study data were extracted from EHR data in 2019 from three urban EDs.

Use of Physician-Estimated and Patient Self-Reported Weights to Guide Initial Fluid Resuscitation in Emergency Department Patients With Suspected Sepsis.

Background: Knowledge of patient weight is required to guide initial intravenous fluid therapy for patients with sepsis-associated hypotension or elevated lactate. Previous studies have shown patients are better estimators of their weight than medical providers are; critically ill patients, however, may be unable to provide this information.

Objectives: This study compares the accuracy of physician-estimated and patient self-reported weights to subsequent inpatient bed/stretcher scale weights for guiding initial protocol-based intravenous fluid therapy in the treatment of emergency department patients with suspected sepsis.