Technology in the perioperative space is no longer a future-state concept. It is an active and accelerating force reshaping how surgical care is delivered. The conversation has moved beyond disruption to one of adoption and transformation. From AI-assisted robotics to predictive analytics, new tools are arriving faster than ever and influencing everything from clinical workflows to operational decision-making.
In a recent guest editorial in the AORN Journal, Sharon Giarrizzo-Wilson explores this shift.1 She highlights how generative artificial intelligence (AI) and other advanced technologies are reshaping perioperative practice while reinforcing the central role of nurses in ensuring these tools are implemented safely, ethically, and effectively.
What’s changing for perioperative teams
Giarrizzo-Wilson’s editorial underscores a clear reality. Technology is changing the OR quickly, and the question is no longer whether to adopt it. The focus now is on understanding what each tool does, when to use it, and how to integrate it into real-world workflows.
AI is already supporting perioperative teams in meaningful ways. Predictive models help anticipate patient risks and identify individuals who may face complications, allowing nurses to intervene earlier and more proactively. Automated documentation tools are reducing time spent on charting, giving clinicians more time to focus on direct patient care. Innovations such as computer vision and robotic guidance are enhancing surgical precision in real time, while analytics are helping teams better manage supplies, staffing, and OR schedules.
These are not theoretical advancements. They are actively shaping the day-to-day decisions and workflows that perioperative teams navigate.
Balancing innovation with accountability
With rapid innovation comes an equally important responsibility to ensure these tools are safe, trustworthy, and aligned with clinical realities.
As highlighted in the editorial, AI systems are not infallible. They can produce misleading outputs, which makes clinical judgment indispensable. Successful adoption requires close collaboration across perioperative teams, including nurses, surgeons, anesthesia providers, and IT stakeholders. This collaboration ensures that technologies are usable, accurate, and supportive of existing workflows.
Ethical considerations are just as critical. Health systems must remain vigilant about bias, transparency, and patient safety so that new technologies enhance care without introducing unintended risk. Even the most advanced tools are only valuable if they support clinicians rather than complicate their work.
Building trust and partnership
For perioperative leaders, trust and partnership are not abstract ideas. They are operational requirements.
AI-enabled tools must earn trust by delivering reliable, transparent, and clinically meaningful insights. Decision support should enhance clinical and operational judgment, not replace it. This requires making the reasoning behind recommendations visible so nurse leaders can validate insights and confidently apply them within their teams.
This philosophy underpins how predictive and prescriptive analytics are designed and deployed. The goal is to provide decision support that strengthens accountability while enabling teams to act with greater confidence and clarity.
Partnership is equally essential. Perioperative environments are highly interdependent, and successful technology adoption requires alignment across nursing leadership, surgical services, anesthesia, and operational teams. Collaborative implementation (we call this “Transformation as a Service” at LeanTaaS) that reflects real-world workflows and incorporates continuous feedback helps ensure that technology supports care delivery rather than disrupts it.
Supporting the realities of perioperative operations
At a practical level, AI and advanced analytics can directly address the challenges perioperative leaders manage every day.
Perioperative staffing is a clear example. Within iQueue for Operating Rooms, predictive staffing capabilities help leaders anticipate demand patterns and align nursing and anesthesia coverage more effectively. By combining historical utilization, schedule trends, and forward-looking case demand, these models support more precise staffing decisions. They also help match nurses to cases based on their skills and highlight opportunities for cross-training, which strengthens both flexibility and team development.
This shift enables safer care delivery while reducing avoidable strain on teams. It also brings greater fairness and transparency into staffing decisions. With clearer visibility into workload distribution and future demand, leaders can create more balanced assignments and support more sustainable staffing practices. Instead of reacting to last-minute changes, teams can plan proactively and with greater confidence.
Health systems are already seeing measurable results from using AI in this way. At Oregon Health & Science University (OHSU), leaders using these workforce optimization capabilities within iQueue have reduced the time spent on staffing coordination by 20 hours per week for service line coordinators and 5 hours per week for charge nurses. This time savings allows teams to shift their focus away from manual schedule building and toward clinical leadership and patient care.
At the same time, other opportunities are emerging upstream in the surgical journey. AI-driven approaches are beginning to help teams identify and reduce barriers to patient readiness for surgery earlier in the process. This moves beyond static checklists and toward more dynamic, proactive preparation that can improve outcomes and reduce day-of-surgery disruptions.
What comes next for perioperative leaders
As AI and advanced analytics become more embedded in perioperative care, nursing leaders have an important role in shaping how these tools are used.
The goal is not automation for its own sake. It is smarter operational support that removes friction from patient readiness, scheduling, and staffing so clinicians can focus more fully on patient care and team leadership. What comes next is not optional adoption, but intentional integration. Predictive insights and data-driven decision support will become standard components of perioperative operations.
Organizations that take this approach will be better positioned to expand access, support their workforce, and deliver more reliable surgical care at scale.
References
- Giarrizzo-Wilson, S. (2026), Technological Evolution in the Perioperative Environment. AORN J, 123: 6-8. https://doi.org/10.1002/aorn.70016
