Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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Randomized Controlled Trial
Improving Follow-Up Attendance for Discharged Emergency Care Patients Using Automated Phone System to Self-Schedule: A Randomized Controlled Trial.
Automated phone appointment reminders have improved adherence with follow-up appointments in a variety of hospital settings, but have mixed results in patients discharged from the emergency department (ED). Increasing adherence to follow-up care has been a priority in the ED to improve patient outcomes and reduce unnecessary future visits. ⋯ An automated self-scheduling phone system significantly improved follow-up adherence after ED discharge, but did not decrease ED revisits.
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Meta Analysis
Machine Learning versus Usual Care for Diagnostic and Prognostic Prediction in the Emergency Department: A Systematic Review.
Having shown promise in other medical fields, we sought to determine whether machine learning (ML) models perform better than usual care in diagnostic and prognostic prediction for emergency department (ED) patients. ⋯ Our review suggests that ML may have better prediction performance than usual care for ED patients with a variety of clinical presentations and outcomes. However, prediction model reporting guidelines should be followed to provide clinically applicable data. Interventional trials are needed to assess the impact of ML models on patient-centered outcomes.
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Randomized Controlled Trial
Rapid Administration of Methoxyflurane to Patients in the Emergency Department (RAMPED) Study: A Randomised controlled trial of Methoxyflurane vs Standard care.
The objective was to evaluate the effectiveness of methoxyflurane versus standard care for the initial management of severe pain among adult emergency department (ED) patients. ⋯ Initial management with inhaled methoxyflurane in the ED did not achieve the prespecified substantial reduction in pain, but was associated with clinically significant lower pain scores compared to standard therapy.
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The COVID-19 pandemic has placed acute care providers in demanding situations in predicting disease given the clinical variability, desire to cohort patients, and high variance in testing availability. An approach to stratifying patients by likelihood of disease based on rapidly available emergency department (ED) clinical data would offer significant operational and clinical value. The purpose of this study was to develop and internally validate a predictive model to aid in the discrimination of patients undergoing investigation for COVID-19. ⋯ The derived predictive models offer good discriminating capacity for COVID-19 disease and provide interpretable and usable methods for those providers caring for these patients at the important crossroads of the community and the health system. We found utilization of the logistic regression model utilizing exposure history, temperature, WBC, and chest X-ray result had the greatest discriminatory capacity with the most interpretable model. Integrating a predictive model-based approach to COVID-19 testing decisions and patient care pathways and locations could add efficiency and accuracy to decrease uncertainty.