Annals of emergency medicine
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Improved understanding of factors affecting prolonged emergency department (ED) length of stay is crucial to improving patient outcomes. Our investigation builds on prior work by considering ED length of stay in operationally distinct time periods and using benchmark and novel machine learning techniques applied only to data that would be available to ED operators in real time. ⋯ This study identified granular capacity, flow, and nurse staffing predictors of ED length of stay not previously reported in the literature. Our novel methodology allowed for more accurate and operationally meaningful findings compared to prior modeling methods.
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Observational Study
Rapid Electroencephalography and Artificial Intelligence in the Detection and Management of Nonconvulsive Seizures.
Nonconvulsive status epilepticus is a commonly overlooked cause of altered mental status. This study assessed nonconvulsive status epilepticus prevalence in emergency department (ED) patients with acute neurologic presentations using limited electroencephalogram (EEG) coupled with artificial intelligence (AI)-enhanced seizure detection technology. We then compared the accuracy of the AI EEG interpretations to those performed by an epileptologist. ⋯ Limited AI-enhanced EEG can detect nonconvulsive status epilepticus in the ED; however, the technology tended to overestimate seizure burden in our cohort. This study found a lower nonconvulsive status epilepticus prevalence compared to prior literature reports.