Annals of emergency medicine
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Comparative Study Observational Study
A Prospective Study of Intramuscular Droperidol or Olanzapine for Acute Agitation in the Emergency Department: A Natural Experiment Owing to Drug Shortages.
Intramuscular medications are commonly used to treat agitation in the emergency department (ED). The purpose of this study is to compare intramuscular droperidol and olanzapine for treating agitation. ⋯ We found no difference in time to adequate sedation between intramuscular droperidol and olanzapine.
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Observational Study
Impact of Atrial Fibrillation Case Volume in the Emergency Department on Early and Late Outcomes of Patients With New Atrial Fibrillation.
To define the association between atrial fibrillation case volume in the emergency department and death or all-cause hospitalization at 30 days and 1 year in patients with new atrial fibrillation. Secondary objectives examined repeat ED visits and the management of atrial fibrillation within 90 days. ⋯ Treatment in higher volume EDs was associated with significantly lower admission rates and repeat ED visits but no difference in survival.
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Tasked with identifying digital health solutions to support dynamic learning health systems and their response to COVID-19, the US Department of Health and Human Services Office of the Assistant Secretary for Preparedness and Response partnered with the University of New Mexico's Project ECHO and more than 2 dozen other organizations and agencies to create a real-time virtual peer-to-peer clinical education opportunity: the COVID-19 Clinical Rounds Initiative. Focused on 3 "pressure points" in the COVID-19 continuum of care-(1) the out-of-hospital and/or emergency medical services setting, (2) emergency departments, and (3) inpatient critical care environments-the initiative has created a massive peer-to-peer learning network for real-time information sharing, engaging participants in all 50 US states and more than 100 countries. One hundred twenty-five learning sessions had been conducted between March 24, 2020 and February 25, 2021, delivering more than 58,000 total learner-hours of contact in the first 11 months of operation.
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Multicenter Study
Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.
This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU at each hour (up to 24 hours) of an emergency department (ED) encounter. The secondary goal was to provide a framework for the operational implementation of these machine learning models. ⋯ Machine learning models were developed to accurately make predictions regarding the probability of inpatient or ICU admission throughout the entire duration of a patient's encounter in ED and not just at the time of triage. These models remained accurate for a patient cohort beyond the time period of the initial training data and were integrated to run on live electronic health record data, with similar performance.
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In a large-scale disaster, recruiting from all retired and nonworking registered nurses is one strategy to address surge demands in the emergency nursing workforce. The purpose of this research was to estimate the workforce capacity of all registered nurses who are not currently working in the nursing field in the United States by state of residence and to describe the job mobility of emergency nurses. ⋯ There is an additional and reserve capacity available for recruitment that may help to meet the workforce needs for nursing, specifically emergency nurses and nurse practitioners, across the United States under conditions of a large-scale disaster. The results from this study may be used by the emergency care sector leaders to inform policies, workforce recruitment, workforce geographic mobility, new graduate nurse training, and job accommodation strategies to fully leverage the potential productive human capacity in emergency department care for registered nurses who are not currently working.