OR leaders must sharpen questions, partnerships as AI reshapes perioperative care

Editor's Note

Artificial intelligence (AI) will not replace perioperative leaders, but it will demand sharper decision-making, closer collaboration, and thoughtful staff engagement, OR Manager coverage reports. AI is poised to transform perioperative practice, yet its value depends on how leaders choose, evaluate, and integrate these technologies.

Key insights the article highlights include:

OR leaders must first ask whether AI addresses a real need before considering adoption. Experts cited explained that AI should enhance existing systems, such as preventing equipment downtime, rather than serve as a stand-alone purchase. They also noted leaders should focus investments on costly, high-stakes areas like early sepsis detection instead of solutions that address problems the organization does not face. Effective evaluation hinges on vendor transparency about algorithm development, data sources, testing, and updates, with experts emphasizing the importance of data quality, source, and volume.

Building strong ties with IT departments, vendors, and even insurers is another critical step. Experts recommended that OR leaders collaborate closely with technology teams to exchange expertise, while also using discussions with suppliers to track emerging AI capabilities. Such networking helps leaders anticipate costs and capabilities before adoption.

Ongoing education is also vital. Leaders should assess AI maturity and cost-effectiveness through reading and conferences. Experts cautioned that clinicians must understand algorithm design, data inputs, and limitations to avoid blind reliance on predictive models.

The article also highlights the critical role of staff in shaping AI tools. Nurses, with their granular knowledge of OR processes, are well-positioned to annotate data for algorithm training, such as identifying steps in surgical videos. Involving frontline staff in both annotation and product evaluation ensures that AI reflects the unpredictable realities of patient care. Experts further note nurses should actively make the case for their insights when collaborating with developers.

What to ask vendors about AI:

• What can the product do with AI we couldn’t do without it, and why do we want that?
• How have you tested these capabilities, and what were the results?
• Do we have to change our processes or our organization to achieve comparable results and, if so, how?
• Where do you get the rules and logic that your AI is based on? What data did you use?
• How do you keep your logic current? How do you incorporate new rules? How often do you update rules and logic?
• Is there anything special that I need to teach my staff?
• How are we protected in the event that your AI makes a mistake? What are the liability provisions in your contract?

Regarding ethical implications of AI, some questions can include:

• Is the company compliant with data protection rules in its country and in the country where patients reside?
• Does the training data set represent the population in which the AI will be used?
• Can developers explain the logic behind the algorithm?
• What is the evidence for safety and efficacy?
• Have clinicians been involved in the process?
• What monitoring mechanisms are in place to assess the performance of the device in the clinical setting?