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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We implemented a virtual observation unit in which emergency department (ED) patients receive observation-level care at home. Our primary aim was to compare this new care model to in-person observation care in terms of brick-and-mortar ED length of stay (inclusive of ED observation unit time) as well as secondarily on inpatient admission and 72-hour return visits (overall and with admission). ⋯ Virtual observation unit patients used fewer hours in ED and ED observation relative to on-site observation patients. This new care delivery model warrants further study because it has the potential to positively impact ED capacity.
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Compare physician gestalt to existing screening tools for identifying sepsis in the initial minutes of presentation when time-sensitive treatments must be initiated. ⋯ Among adults presenting to an ED with an undifferentiated critical illness, physician gestalt in the first 15 minutes of the encounter outperformed other screening methods in identifying sepsis.