With electronic medical records (EMRs) comes the opportunity to access data for nursing research purposes. However, a challenge for clinical nurses is how to get this data without it being a painful manual process (something I have experienced so far during PhD research work!). For this, you will need access to a data analyst and statistitian to make the process as painless as possible (and maybe agreement to collate across a large number and types of hospitals). Just a few hurdles and maybe a pot of cash, but lets keep things positive. Below are some examples of work around using machine learning and pressure injuries, one of the most common ‘nursing influenced’ quality focused measure.

Resources

Pei, J., Guo, X., Tao, H., Wei, Y., Zhang, H., Ma, Y., & Han, L. (2023). Machine learning‐based prediction models for pressure injury: A systematic review and meta‐analysis. International Wound Journal, 20(10), 4328-4339.

Alderden, J., Pepper, G. A., Wilson, A., Whitney, J. D., Richardson, S., Butcher, R., … & Cummins, M. R. (2018). Predicting pressure injury in critical 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 pressure injury risk in hospitalised patients using machine learning with electronic health records: a US multilevel cohort study. BMJ open, 14(4), e082540.