Nursing informatics leaders say the most meaningful patient safety improvements tied to AI in nursing workflows so far have come from mature, predictable decision-support tools — while more experimental applications, including generative AI, remain largely unproven at the bedside.
Marc Benoy, BSN, RN, chief nursing information officer at Summa Health in Akron, Ohio, first cautioned that the term “AI” is often applied too broadly, obscuring critical differences between traditional predictive analytics, embedded machine-learning models and generative AI — each with distinct risk profiles, governance needs and levels of clinical maturity.
At his organisation, generative AI is not currently operationalised in bedside nursing workflows. Any measurable safety gains have instead come from established decision-support tools and predictive risk scoring embedded in the electronic health record.
“When implemented well, they can support safer care by reinforcing consistency, reducing variation and nudging standardized actions in safety-sensitive workflows,” Mr. Benoy said, emphasising that such tools remain supplements to, not replacements for, clinical judgment.
Because these systems behave predictably, he said, they can be validated, monitored and governed over time — a key requirement in evidence-based nursing practice. By contrast, he warned that opaque or poorly understood AI tools can unintentionally shift cognitive burden back onto nurses, introducing new safety risks rather than reducing them.
He also pointed to operational constraints, noting that successful implementation requires staffing, informatics capacity, capital investment and sustained governance — resources that many health systems lack, particularly when returns on newer AI initiatives remain uncertain.
Source: Becker's Health IT, 29 January 2026
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