By Drs. Raga Bjarnadottir and Tamara Macieira
Good clinical practice depends on good evidence. But the volume of clinical evidence is enormous and growing at an exponential pace, and it can feel impossible for any one nurse to stay on top of the best available evidence for a given practice question. AI tools add another layer to that challenge. Healthcare organizations are rapidly adopting ambient documentation assistants, sepsis-prediction algorithms, staffing tools, fall-risk scores and more, often with a vendor demo and a go-live date rather than a peer-reviewed trial in a setting that looks like yours.
We argue nurses have the skill set for using generative AI to support the evidence-based practice (EBP) process and to evaluate clinical AI tools in their practice.
EBP is a cornerstone of the nursing profession
Nurses have practiced some version of Sackett and colleagues’ five-step EBP cycle for decades: ask a focused clinical question, acquire the best available evidence, appraise it critically, apply it in context and evaluate the outcome (Sackett et al., 2000). Melnyk and colleagues (2014) built this into formal competencies for practicing RNs, not just APRNs or researchers, because the questioning happens at the point of care, shift after shift. The habit of asking, “Why do we do this?” is not just an academic exercise, but the very mechanism that catches drift between what a practice claims to do and what it actually does for our patients.
AI raises the pace… and the stakes
The evidence base for clinical AI often lags behind its deployment. A tool can be released, adopted and embedded into workflow before independent validation across diverse populations and settings exists (Char et al., 2018), and translational research shows formal evidence typically takes years to reach practice under ideal conditions (Morris et al., 2011). AI does not close that gap. If anything, it widens the space where a diligent, engaged nurse is the only real check between a confident vendor claim and a patient outcome. Topaz and colleagues (2025) flag bias, hallucination and deskilling as still-underexamined risks, while Ulrich and colleagues (2026) frame that evaluative work as central to nurses’ standing as moral agents. AI-generated summaries and citations need to be verified against the original sources.
What this requires
Critical appraisal is a skill, and skills need practice, mentorship and protected time, not good intentions alone. Units that build EBP councils, journal clubs and QI mentorship into the workday are building the infrastructure AI governance now depends on. That is also why we built this installment of the FloGatorAI Prompt Cookbook around Sackett’s five steps: to give nurses a concrete way to use AI as a thinking partner in each stage of appraisal, without ever handing the judgment itself to the tool.
Nurses have always translated evidence into care at the bedside. As AI enters that picture, that skill becomes one of the most critical safety guardrails we have. Asking the question, checking the evidence and evaluating the outcome are ours to practice, ours to teach and ours to demand the time for.
References
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care – addressing ethical challenges. New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMp1714229
Melnyk, B. M., Gallagher-Ford, L., Long, L. E., & Fineout-Overholt, E. (2014). The establishment of evidence-based practice competencies for practicing registered nurses and advanced practice nurses in real-world clinical settings. Worldviews on Evidence-Based Nursing, 11(1), 5-15. https://doi.org/10.1111/wvn.12021
Morris, Z. S., Wooding, S., & Grant, J. (2011). The answer is 17 years, what is the question: Understanding time lags in translational research. Journal of the Royal Society of Medicine, 104(12), 510-520. https://doi.org/10.1258/jrsm.2011.110180
Sackett, D. L., Straus, S. E., Richardson, W. S., Rosenberg, W., & Haynes, R. B. (2000). Evidence-based medicine: How to practice and teach EBM (2nd ed.). Churchill Livingstone.
Topaz, M., Peltonen, L. M., Michalowski, M., Pruinelli, L., Ronquillo, C. E., Zhang, Z., & Babic, A. (2025). The overlooked dark side of generative AI in nursing: An international think tank’s perspective. Journal of Nursing Scholarship, 57(4), 559-562. https://doi.org/10.1111/jnu.70016
Ulrich, C. M., Oh, O., You, S. B., Topaz, M., Rahemi, Z., Stokes, L., Pruinelli, L., Demiris, G., & Flatley Brennan, P. (2026). What does moral agency mean for nurses in the era of artificial intelligence? Hastings Center Report, 56(1), 18-23. https://doi.org/10.1002/hast.70030