Artificial intelligence has the potential to reduce documentation burdens, improve access to clinical protocols and preserve institutional knowledge in the operating room, but successful implementation should start with identifying a problem – not simply purchasing the latest technology.
That was a central message from a recent OR Today webinar sponsored by C8 Health. The discussion featured Vangie Dennis, MSN, RN, CMLSO, CNOR, FAORN, FAAN, perioperative nurse executive and consultant with Perioperative Consultants LLC; Sicknee Daher, BSN, RN, clinical practice director, anesthesiology, at Henry Ford Hospital; and Galia Fisher, RN, vice president of customer success at C8 Health.
The panel examined practical applications of AI in perioperative nursing, including documentation, clinical knowledge, onboarding, workflow integration, governance and patient safety.
Giving Nurses More Time for Patient Care
Asked what persistent perioperative problem she would like AI to address, Daher pointed to documentation and information burden. Although individual documentation requirements may be necessary, collectively they can take nurses away from direct patient care.
Daher said she would like AI to reduce repetitive charting and surface information nurses need to act on. “The goal is not to chart less responsibly,” she said, but to make appropriate documentation easier and more meaningful.
Dennis identified risk stratification as another promising application. She discussed AI’s potential to support clinical decision-making by helping assess individual risks for postoperative complications, intensive care admission and hospital length of stay.
Putting Perioperative Knowledge at Nurses’ Fingertips
Finding the right information when it is needed remains a challenge throughout the perioperative continuum. Daher described nurses searching multiple areas of a patient’s chart during pre-anesthesia testing and navigating protocols involving antibiotic timing and glucose management.
“I think the opportunity there is to put the right information in front of the nurse at the right point of time within the work that they’re doing, rather than expecting them to stop, look for it and then make a decision,” Daher said.
AI could also help preserve institutional knowledge as new nurses enter perioperative practice. Dennis noted that OR education has traditionally relied heavily on apprenticeship and knowledge passed from experienced clinicians to newer staff. Technology could potentially make that expertise more accessible at the point of care.
However, the panel stressed that AI should not replace orientation, competency assessment or nursing judgment.
What Should Leaders Look for in AI?
For perioperative leaders evaluating AI technology, Daher outlined several priorities: clearly define the clinical or operational problem, meaningfully involve nurses in design and evaluation, integrate the technology into existing workflows and establish measurable outcomes.
Organizations should also determine who owns the content, how information will remain current and how problems with the technology will be addressed.
Dennis emphasized safety, reliability, data governance and privacy. She also stressed that AI needs to account for local workflows, surgeon preferences and institutional knowledge.
Frontline nurses should have a meaningful role in those decisions because they understand where workflows succeed and where they break down. Other stakeholders should include leadership, education, IT, quality and appropriate clinical partners, the panel said.
Finding Measurable Value
Surgical preference cards emerged repeatedly as one potential application. Keeping cards current can be challenging as surgeon practices change. AI could potentially make updated information easier to access without requiring nurses to interrupt their workflow.
Dennis also pointed to inventory management, supply tracking, surgical tray location and supply usage logs as opportunities for AI.
Measuring results is critical. Potential metrics discussed during the webinar included efficiency, waste, staff onboarding and the amount of time required to gather operational data.
The panel also acknowledged that nurses may be skeptical of another technology platform. Daher said that skepticism can be understandable when previous tools have added work instead of reducing it. Successful implementation requires involving nurses early, testing workflows with actual users and responding to their feedback.
“It has to remove the work,” Daher said. “We have to make sure that we are bringing them value where they need it.”
Throughout the discussion, panelists emphasized that AI should support rather than replace clinical expertise. Dennis cautioned that AI-generated information still requires human judgment and must align with patient safety, ethical practice and locally validated information.
The webinar was sponsored by C8 Health, an AI clinical performance platform focused on implementing best practices at the point of care.
