Unlabelled: Disparate clinical outcomes have been reported for patients with Limited English Proficiency (LEP) in the emergency department setting, including increased length of stay, diagnostic error rates, readmission rates, and dissatisfaction. Our emergency department had no standard processes for LEP patient identification or interpreter encounter documentation and a higher rate of 48-hour LEP return visits (RV) than English proficient patients. The aim was to eliminate gaps by increasing appropriate interpreter use and documentation (AIUD) for Spanish-speaking LEP (LEP-SS) patients from 35.7% baseline (10/17-05/18) to 100% by October 2020.
Methods: LEP-SS patient data were reviewed in the electronic medical record to determine the AIUD and RV rates. Using the Model for Improvement and multiple Plan-Do-Study-Act (PDSA) cycles, a multi-disciplinary team encouraged stakeholder engagement and identified improvement opportunities, implemented an electronic tracking board LEP icon (PDSA1), standardized documentation using an LEP Form linked to the icon (PDSA2), and included color changes to the icon for team situational awareness (PDSA3).
Results: The mean of LEP-SS patients with AIUD improved from 35.7% to 64.5% without significant changes in balancing measures. During the postintervention period (6/1/2018-10/31/2020), no special cause variation was noted from the baseline 48-hour emergency department RV rates for LEP patients (3.1%) or English proficient patients (2.6%).
Conclusions: While the RV rate was not affected, this project is part of a multi-faceted approach aiming to positively impact this outcome measure. Significant improvements in AIUD were achieved without affecting balancing measures.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8677944 | PMC |
http://dx.doi.org/10.1097/pq9.0000000000000486 | DOI Listing |
Int J Surg
January 2025
Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou; Chang Gung University, Taoyuan, Taiwan.
Background: Detecting kidney trauma on CT scans can be challenging and is sometimes overlooked. While deep learning (DL) has shown promise in medical imaging, its application to kidney injuries remains underexplored. This study aims to develop and validate a DL algorithm for detecting kidney trauma, using institutional trauma data and the Radiological Society of North America (RSNA) dataset for external validation.
View Article and Find Full Text PDFJAMA
January 2025
Division of Hematology, Brigham and Women's Hospital, Boston, Massachusetts.
JAMA Intern Med
January 2025
Harvard Medical School, Boston, Massachusetts.
JAMA Intern Med
January 2025
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Importance: There are no validated decision rules for terminating resuscitation during in-hospital cardiac arrest. Decision rules may guide termination and prevent inappropriate early termination of resuscitation.
Objective: To develop and validate termination of resuscitation rules for in-hospital cardiac arrest.
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