Nurses call for pause on healthcare AI

Nurses around the country continue to raise their concerns about artificial intelligence use in healthcare, with the country's biggest nursing union calling for a pause on the technology.

Hundreds of nurses protested AI April 22 in front of a Kaiser Permanente hospital in San Francisco, while nearly 100 gathered April 27 in Nashville, Tenn., in part to bargain for the "responsible implementation" of AI at Nashville-based HCA Healthcare, the nation's largest health system.

National Nurses United released the results of a survey of over 2,300 registered nurses and union members May 15 that found 60% didn't trust their employers to implement AI with a focus on patient safety.

"The survey and reports from nurses across the country demonstrate that we need an immediate pause on implementing AI in healthcare settings," said Deborah Burger, RN, president of National Nurses United, in a statement. "As patient advocates, it is our duty to assess the evidence in front of us and question the unfounded marketing claims that AI will complement our bedside skills or improve the quality of care for our patients."

Here are more findings from the survey, according to National Nurses United:

— Half of the respondents said their employers analyze EHR data with algorithms to deduce patients' acuity and predict the number of nurse hours needed. Of those nurses, 69% said their assessment doesn't align with the AI, as it doesn't take into account the "educational, psycho-social, or emotional needs" of patients.

— About 12% of nurses said their employers are using AI-assisted nurse handoffs. Of those respondents, 49% said their assessments don't match the computer's.

— In facilities that employ images and sounds to score pain and wound severity, 29% percent of nurses say they're unable to revise assessments or categorizations generated by AI.

— In facilities where AI predicts patients' outcomes, risk for a complication, or discharge timelines, 40% of nurses say they can't change the scores based on their own clinical judgment.

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