Overview
ENGRAFT's multidisciplinary collaboration focuses on transforming chronic disease management through the application of advanced data science techniques. By leveraging machine learning and AI, her research aims to model disease trajectories, helping to predict health outcomes and inform personalized care plans. Dr. Pruinelli's work emphasizes the integration large health data to tailor care strategies, improving patient outcomes and quality of life. Her efforts in this space seek to enhance how clinicians understand chronic disease progression, ultimately driving more effective, individualized care management for patients with complex conditions.
Specifically, Dr. Pruinelli's work in pain management focuses on identifying risk factors and determine the best tailored interventions with the potential to prevent chronic pain development for specific sub-populations.
Selected References
Park, J. I., Johnson, S., & Pruinelli, L. (2025). Optimizing pain management in breast cancer care: Utilizing “All of Us” data and deep learning to identify patients at elevated risk for chronic pain. Journal of Nursing Scholarship : An Official Publication of Sigma Theta Tau International Honor Society of Nursing, 57(1), 95–104. https://doi.org/10.1111/jnu.13009
Tsuma Gaedke Nomura, A., Pruinelli, L., Lucena, A. de F., Pasin, S., Klein, C., Nabinger Menna Barreto, L., & De Abreu Almeida, M. (2024). An Information Model on Pain Management: Cultural Validation and Refinement. Studies in Health Technology and Informatics, 315, 8–13. https://doi.org/10.3233/SHTI240097
Stalter, N., Ma, S., Simon, G., & Pruinelli, L. (2023). Psychosocial problems and high amount of opioid administration are associated with opioid dependence and abuse after first exposure for chronic pain patients. Addictive Behaviors, 141, 107657. https://doi.org/10.1016/j.addbeh.2023.107657
Usher, M. G., Tourani, R., Webber, B., Tignanelli, C. J., Ma, S., Pruinelli, L., Rhodes, M., Sahni, N., Olson, A. P. J., Melton, G. B., & Simon, G. (2022). Patient Heterogeneity and the J-Curve Relationship Between Time-to-Antibiotics and the Outcomes of Patients Admitted With Bacterial Infection. Critical Care Medicine, 50(5), 799–809. https://doi.org/10.1097/CCM.0000000000005429
Nomura, A. T. G., de Abreu Almeida, M., & Pruinelli, L. (2021). Information Model on Pain Management: An Analysis of Big Data. Journal of Nursing Scholarship : An Official Publication of Sigma Theta Tau International Honor Society of Nursing, 53(3), 270–277. https://doi.org/10.1111/jnu.12638
Nomura, A. T. G., Pruinelli, L., Barreto, L. N. M., Graeff, M. D. S., Swanson, E. A., Silveira, T., & Almeida, M. de A. (2021). Pain Management in Clinical Practice Research Using Electronic Health Records. Pain Management Nursing : Official Journal of the American Society of Pain Management Nurses, 22(4), 446–454. https://doi.org/10.1016/j.pmn.2021.01.016
Gaedke Nomura, A. T., de Abreu Almeida, M., Johnson, S., & Pruinelli, L. (2021). Pain Information Model and Its Potential for Predictive Analytics: Applicability of a Big Data Science Framework. Journal of Nursing Scholarship : An Official Publication of Sigma Theta Tau International Honor Society of Nursing, 53(3), 315–322. https://doi.org/10.1111/jnu.12648
Park, Y.-S., & Pruinelli, L. (2021). Developing Information Model of Central Line-Associated Bloodstream Infection (CLABSI) Prevention. Studies in Health Technology and Informatics, 284, 408–413. https://doi.org/10.3233/SHTI210760
Mello, B. S., Almeida, M. de A., Pruinelli, L., & Lucena, A. de F. (2019). Nursing outcomes for pain assessment of patients undergoing palliative care. Revista brasileira de enfermagem, 72(1), 64–72. https://doi.org/10.1590/0034-7167-2018-0307
Westra, B. L., Johnson, S. G., Ali, S., Bavuso, K. M., Cruz, C. A., Collins, S., Furukawa, M., Hook, M. L., LaFlamme, A., Lytle, K., Pruinelli, L., Rajchel, T., Settergren, T. T., Westman, K. F., & Whittenburg, L. (2018). Validation and Refinement of a Pain Information Model from EHR Flowsheet Data. Applied Clinical Informatics, 9(1), 185–198. https://doi.org/10.1055/s-0038-1636508
Pruinelli, L., Westra, B. L., Yadav, P., Hoff, A., Steinbach, M., Kumar, V., Delaney, C. W., & Simon, G. (2018). Delay Within the 3-Hour Surviving Sepsis Campaign Guideline on Mortality for Patients With Severe Sepsis and Septic Shock. Critical Care Medicine, 46(4), 500–505. https://doi.org/10.1097/CCM.0000000000002949
Westra, B. L., Christie, B., Johnson, S. G., Pruinelli, L., LaFlamme, A., Sherman, S. G., Park, J. I., Delaney, C. W., Gao, G., & Speedie, S. (2017). Modeling Flowsheet Data to Support Secondary Use. Computers, Informatics, Nursing : CIN, 35(9), 452–458. https://doi.org/10.1097/CIN.0000000000000350
Pruinelli, L., Yadav, P., Hangsleben, A., Johnson, J., Dey, S., McCarty, M., Kumar, V., Delaney, C. W., Steinbach, M., Westra, B. L., & Simon, G. J. (2016). A Data Mining Approach to Determine Sepsis Guideline Impact on Inpatient Mortality and Complications. AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science, 2016, 194–202