Annals of emergency medicine
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Multicenter Study
Use of Machine Learning to Develop a Risk-Stratification Tool for Emergency Department Patients With Acute Heart Failure.
We use variables from a recently derived acute heart failure risk-stratification rule (STRATIFY) as a basis to develop and optimize risk prediction using additional patient clinical data from electronic health records and machine-learning models. ⋯ Use of a machine-learning model with additional variables improved 30-day risk prediction compared with conventional approaches.
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Case Reports
Errors in Decisionmaking in Emergency Medicine: The Case of the Landscaper's Back and Root Cause Analysis.
Root cause analysis is often suggested as a means of conducting quality assurance, but few physicians are familiar with the actual process. We describe a detailed approach to conducting root cause analysis, with an illustrative case to explain the technique. By studying how root cause analysis is applied to the case of a missed epidural abscess, the reader will see how the process reveals systems improvements that reduce the risk that such a miss will happen again. Following this process will be helpful in using root cause analysis to fix not just individuals' issues but also but systemwide quality assurance issues to improve patient care.
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We evaluate the effect of implementing the out-of-hospital pediatric traumatic brain injury guidelines on outcomes in children with major traumatic brain injury. ⋯ Implementation of the pediatric out-of-hospital traumatic brain injury guidelines was not associated with improved survival when the entire spectrum of severity was analyzed as a whole (moderate, severe, and critical). However, both adjusted survival to hospital admission and discharge improved in children with severe traumatic brain injury, indicating a potential severity-based interventional opportunity for guideline effectiveness. These findings support the widespread implementation of the out-of-hospital pediatric traumatic brain injury guidelines.