Consider all angles when choosing AI technology

By: Cynthia Saver, MS, RN

Artificial intelligence (AI) is positioned to be a game changer in perioperative care. Some innovations are already here, and others lie in the future. What can OR leaders be doing now to ensure that they and their organizations keep up with AI trends and properly evaluate products claiming to have AI? The answers include asking the right questions, making connections, learning, and engaging staff.

Ask the right questions

OR leaders need to ask the right questions before purchasing an AI product. As with any new product, the first question is whether it’s needed. “You don’t buy AI by itself,” says John Glaser, PhD, senior vice president of population health at Cerner in Kansas City, Missouri. “AI is part of something else, and it should help that something else perform better.” For example, if AI in an MRI machine can detect problems earlier and solve them before the machine fails, that would provide value.

“You want to be sure you’re using AI for the right reasons,” adds Whende Carroll, MSN, RN-BC, founder of Nurse Evolution, a company that looks at how healthcare technology, data analytics, and innovation concepts can be used to improve health and how healthcare is delivered. “Consider what question you are trying to answer and what you can get out of it [AI].” For example, if staffing isn’t an issue, an OR leader should not spend money on computerized AI-based staffing models. Instead, focus on areas where care is more expensive and intense, for instance, integrating AI into the electronic health record so that sepsis is detected earlier.

Glaser says that one of the goals when questioning vendors about products with AI is to determine whether the company engaged in good manufacturing processes. “You want to know what processes are being used to make sure [the product] is as good as it can be,” he says. Those should include using subject matter experts when developing algorithms, conducting extensive testing, and tapping into large volumes of quality data.

Carroll says that data questions boil down to three considerations: sources, quality, and volume. “The questions we need to be asking are: Who is giving us the data? What is the quality of the data? and What is the volume of data?” she says.

Make connections

“AI is just one of the many technical challenges OR leaders have,” says Patricia Seifert, MSN, RN, CNOR, FAAN, independent cardiac consultant in Falls Church, Virginia. She recommends OR leaders build relationships with information technology (IT) staff.

Patricia Seifert, MSN, RN, CNOR, FAAN

Patricia Seifert, MSN, RN, CNOR, FAAN

“Befriend technology people,” she says. “Invite them to talk to staff.” OR leaders can then offer to send a staff member to talk to IT personnel to provide insight into the OR. “Education doesn’t solve everything, but sharing your knowledge is one of the greatest gifts you can give someone,” Seifert says.

Seifert also recommends talking to current vendors about what they have planned related to AI, and to insurers, who usually stay current on developments like AI that can impact costs. “The next time you have lunch with your major suppliers, ask them what role they see AI playing,” Glaser adds.

Learn

“Anytime there is a major technology shift [as is the case with AI], you want to start getting some education about how mature the technology is, whether it’s worth the expense, and other information,” Glaser says. Reading and conferences can help boost knowledge.

“Clinicians should know enough about AI systems to know where they’re reliable and where they’re weak,” says Wendell Wallach, senior advisor to the Hastings Center in Garrison, New York, author of A Dangerous Master: How to Keep Technology from Slipping Beyond Our Control, and chair of technology and ethics studies at the Yale Interdisciplinary Center for Bioethics in New Haven, Connecticut.

Danton Char, MD, assistant professor of anesthesiology, perioperative and pain medicine at Stanford University Medical Center in Stanford, California, says knowledge will help in assessing products and their appropriate use. “They [clinicians] must adequately understand how algorithms are created, critically assess the source of the data used to create the statistical models designed to predict outcomes, understand how the models function, and guard against becoming overly dependent on them,” he says.