‘Nurses Are Essential’ to AI Integration in Healthcare

For Dr. Erich Huang, Duke Health’s chief data officer for quality, one issue often overlooked when discussing AI in healthcare is the importance of the user experience.

“It’s not just an abstract Westworld brain sitting out there,” Huang says. “It has to be well integrated with clinical workflow, and nurses are essential to that.”

With the Sepsis Watch early warning program, Huang says, nurses were able to apply their professional experience to kick off the cascade of actions that would follow an AI-produced alert.

“One of the big issues with electronic health records is fatigue from alerts,” he says. “If you have a human intermediary who can serve as a first line, that’s an important component, because we can then triage things appropriately.”

Huang adds that he’d like to see more nurse-initiated thinking about automated processes that would make nurses’ jobs easier.

“I’d like to hear nursing staff identify inefficiencies they deal with, and think about the things AI and ML would be helpful in improving, allowing them to spend more time with their patients,” he says. “All clinical staff really need to be well integrated into the development or selection of these AI-based apps.”

DISCOVER: Learn how to bridge the gap between nurses and IT teams.

Including Nurses in the AI Conversation

Nurses must ensure that advanced technologies such as AI don’t cause harm or compromise the nature of human interactions and relationships that are central to their job, says Liz Stokes, director of the American Nurses Association’s Center for Ethics and Human Rights.

“Nurses must also be sensitive to unintended consequences related to the development and use of AI technologies,” Stokes says. “As with any other advanced technology in practice, nurses should ensure that the AI being used is not biased, and they must express their concerns if there is potential or actual bias that is occurring.”

Though AI can produce efficiencies in processes, such as prediction and diagnosis, Stokes says it has the potential to lower efficiency and increase stress and burnout if the cognitive demand on clinical teams is higher.

Adequate training and education for nurses is imperative, Stokes adds, and IT leaders need to involve nurses during the development and consideration of AI-related technologies. Those leaders should also collaborate with nurse informaticists, ethicists, engineers and other stakeholders when AI implementation is considered.

Collaboration Across the Board for AI Success

Ed McCallister, CIO of University of Pittsburgh Medical Center, says healthcare organizations looking to use AI effectively should involve nurses as they define what a good experience would be for the patient. Nurses should also help identify the information needed to create that experience.

“They know the data, they know the patient more than anyone in the care team, so having them be able to trust the data is important,” he says.

It’s the CIO’s job to take the lead in ensuring nursing leadership has a seat at the table from the beginning for all discussions related to the design, implementation and rollout of AI- and ML-based applications, McCallister adds. “We work shoulder to shoulder with nursing leadership as we roll out the technology across UPMC,” he says.

Bringing together the data and clinical teams is critical, McCallister says, so that healthcare systems can understand how to deploy new technologies in meaningful ways and make the best use of the data to improve patient experience and outcomes.

“I’m happy to see a more formalized approach to nursing with AI. This is a natural transition to a more intelligent way of doing things, and that’s why it’s so important to have nurse leadership at the table,” he says. “If we want to create something that would be best for the patient at bedside with the nurses, we want the nurses’ experience driving whatever we are building.”