Articles: emergency-department.
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Stratifying risk of patients with acute coronary syndrome (ACS) in the emergency department (ED) remains a frequent challenge. When ST-elevation criteria are absent, current recommendations rely upon insensitive and time-intensive methods such as the electrocardiogram and cardiac enzyme testing. Here, we report on a series of cases, where emergency physicians used a simplified model for identifying regional wall motion abnormalities by point-of-care echocardiography in patients presenting with chest pain to the ED. With the use of a simplified model described herein, high-risk patients with ACS were identified rapidly in a cohort usually difficult to risk stratify.
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Checklists have successfully been used in intensive care units (ICUs) to improve metrics of critical care. Proper peri-intubation care including use of appropriate induction agents and postintubation sedation is crucial when performing endotracheal intubation (ETI) on critically ill patients, especially in the emergency department (ED). We sought to evaluate the impact of checklists on peri-intubation care in ED trauma patients. ⋯ Peri-intubation checklists result in higher rates of RSI in ED trauma patients but do not alter other measured metrics of peri-intubation care.
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This study was conducted to examine the characteristics of intentional fall injuries and the factors associated with their prognosis. ⋯ The risk of severe injury increased with fall height in the intentional group, and a high school level of education rather than a college level of education was associated with more severe injury.
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Implementation of a novel point-of-care ultrasound billing and reimbursement program: fiscal impact.
The aim of this study was to determine the fiscal impact of implementation of a novel emergency department (ED) point-of-care (POC) ultrasound billing and reimbursement program. ⋯ Within 1 year of inception, our novel POC ultrasound billing and reimbursement program generated significant revenue through ultrasound billing.
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The objectives of this study are to design an artificial neural network (ANN) and to test it retrospectively to determine if it may be used to predict emergency department (ED) volume. ⋯ The results of this study show that a properly designed ANN is an effective tool that may be used to predict ED volume. The scatterplot demonstrates that the ANN is least predictive at the extreme ends of the spectrum suggesting that the ANN may be missing important variables. A properly calibrated ANN may have the potential to allow ED administrators to staff their units more appropriately in an effort to reduce patient wait times, decrease ED physician burnout rates, and increase the ability of caregivers to provide quality patient care. A prospective is needed to validate the utility of the ANN.