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Compression Injuries – Nursing Education Network

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Electronic medical records (EMR) provide access to data for nursing research purposes. However, the challenge for clinical nurses is find out how to obtain this data with out a painful manual process. In this case, you wish access to a knowledge analyst and statistician to make the method as painless as possible (and maybe consent to compile data for giant numbers and sorts of hospitals). Just a couple of hurdles and perhaps a pile of money, but let’s stay positive. Below are some examples of workarounds for machine learning and pressure injuries, probably the most common quality-focused metrics that “impact nursing.”

Pei, J., Guo, X., Tao, H., Wei, Y., Zhang, H., Ma, Y., and Han, L. (2023). Machine learning-based predictive models for pressure injuries: a scientific review and meta-analysis. International Journal of Wounds, 20(10), 4328-4339.

Alderden, J., Pepper, G. A., Wilson, A., Whitney, J. D., Richardson, S., Butcher, R.,… and Cummins, M. R. (2018). Predicting barotrauma in intensive care patients: a machine learning model. American Journal of Critical Care, 27(6), 461-468.

Padula, W. V., Armstrong, D. G., Pronovost, P. J., & Saria, S. (2024). Predicting the Risk of Compression Injuries in Hospitalized Patients Using Machine Learning with Electronic Health Records: A US Multilevel Cohort Study. BMJ open, 14(4), e082540.

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