Compression Injuries – Nursing Education Network

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.