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Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events.

Georgantes ER, Gunturkun F, McGreevy TJ, et al. Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events. J Nurs Scholarsh. 2024;Epub May 21. doi:10.1111/jnu.12983.

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July 24, 2024
Georgantes ER, Gunturkun F, McGreevy TJ, et al. J Nurs Scholarsh. 2024;Epub May 21.
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Nurse sensitive indicators (NSI) can help organizations identify areas for improvement. Rates of three nurse sensitive indicators - falls, healthcare associated pressure injuries, and healthcare associated infections - in one hospital were analyzed to identify if disparities exist and to create a model for identifying patients at risk. Patients with at least one NSI were more likely to have been admitted emergently, admitted to the ICU, and have longer ICU and hospital stays than patients with no NSI. Race/ethnicity was not associated with the risk of experiencing an NSI.

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Georgantes ER, Gunturkun F, McGreevy TJ, et al. Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events. J Nurs Scholarsh. 2024;Epub May 21. doi:10.1111/jnu.12983.