AI and Professional Nursing: On a Collision Course

In his wonderful and pragmatic new book, A Giant Leap, Dr. Robert Wachter cautions his professional colleagues that simply confiscating potential administrative and clinical staffing savings created by AI could foster a whirlwind of negative consequences for healthcare enterprises.

Nowhere is the explosive potential for reaction to AI incursions into care delivery greater than in nursing, hospitals’ largest single professional expense category. Hospitals employ more than 1.8 million Registered Nurses (RNs) and another 400 thousand non-RN nursing personnel. RNs alone are more than 30% of the hospital salaried workforce, and more than 40% of overall staff costs).

Nursing productivity is a central issue in overall hospital performance, and a key intervening variable both in clinical quality and patient satisfaction. So the capacity of AI to improve nursing productivity will be a core issue in determining AI’s effect on overall hospital operating performance.

There is clearly room for improvement. Studies have shown that nurses spend only 25-30% of their work hours in direct patient care activities. AI’s potential for alleviating the huge administrative burden damaging nursing productivity might be the biggest benefit AI could provide. AI could materially increase nursing time at the bedside, increasing both patient and nursing satisfaction.

However, AI could also reduce hospitals’ nurse headcount, a factor which could, in turn, reduce nursing union membership, the largest and fastest growing single category of hospital employees’ union membership. Almost 18% of all hospital employed RNs are members of labor unions (AFSCME, AFT Healthcare, National Nurses Union, etc. and their local affiliates). Union dues from nurses represent hundreds of millions in annual income to the unions that represent them.

Nursing unions’ most visible public policy initiative, which appeared first in California twenty years ago, was getting its state legislature to mandate nurse to patient staffing ratios in hospitals. These were designed to compel hospitals to hire more nurses with the intention of improving patient safety. What the ratios actually did was throw more nursing bodies at broken processes and systems. These laws had the important collateral benefit of assuring a “guaranteed income” in union dues from more nurses employed by hospitals subject to these ratios!

Formal (though less comprehensive) mandates for nurse staffing ratios have since spread to Oregon, Massachusetts and New York, with legislation pending in Maine, New Jersey, Pennsylvania. Michigan, Minnesota and Washington State. The research on the intended qualitative benefits of California’s state-mandated ratios confirm the expected benefits to patients, though the studies relied upon correlational analyses vs. states without the ratio mandate, not pre- and post- studies of the ratios’ effects on patient care.

Other studies concluded that the ratios pushed up both RN numbers and compensation vs other job categories as well as damaging hospitals’ operating margins relative to states lacking the mandates. The point-counterpoint of these studies gives one a sense of an issue rapidly becoming politicized.

AI joins other technology enabled initiatives such as telehealth-assisted virtual nursing, robotic medication dispensing and “hospital at home” remote monitoring (which enables earlier patient discharge from the inpatient setting) in threatening to undermine state-mandated nurse staffing ratios.

Nursing unions have noticed the AI threat and sounded the alarm. Consider the National Nursing Union warning: “The hospital industry, in cooperation with Silicon Valley and Wall Street, will use A.I. to further its dangerous effort to displace RNs from the physical care of their patients prioritizing low-cost or free labor over patient needs.”

On the threat posed by remote monitoring, likely to be structured and guided by AI, NNU warned: “This contributes to an ongoing effort by the hospital industry to maximize revenue by pushing care onto less-skilled medical workers, or even non-medical workers in remote settings (for example, the patient’s home). Over time, this will dramatically limit opportunities for nurses to care for patients in a hospital setting.”

Navigating this fraught labor relations and political landscape will impose constraints both on the design and implementation of nursing-related AI applications. AI architects and their hospital administrative partners will win plaudits for streamlining meds administration and eliminating pointless fiddling with the electronic health record ( which consume 30% or more of a nurse’s working hours). Both are sources of burnout and job dissatisfaction among nurses. Freeing up nursing time to spend in direct patient care will benefit both patients and caregivers.

Mistrust of administration and the fear that management will simply pocket the savings from AI-driven nursing productivity gains is what is driving union activism, hence the Wachter warning. One can expect the conditions surrounding AI implementation in nursing to rise to the top of the stack in collective bargaining negotiations as contracts enter the renewal cycle.

In a recent essay on AI implementation, Stuart Winter Tear makes a crucial point about AI: “You are not deploying agents. You are redesigning work”. In this spirit, how one structures the division between agentic action and human labor in dynamic work environments like nursing is the heart of the matter.

This means that the agency of human caregivers in that redesign is crucial to the legitimacy of the effort. If nurses feel their professional world is being restructured by a cabal of (largely male) AI technicians and finance folks, the seeds of a deep and bitter alienation will be sown. Labor management relations could markedly deteriorate, whether the facility is unionized or not

How healthcare executives navigate AI implementation in nursing will be one of the most complex and fraught issues in care delivery the next few years, amplified by the high level of anxiety about AI in the society at large. As Wachter said in Giant Leap: “ When doctors and nurses perceive that autonomous Al is safe for patients and can take onerous tasks off their plates, removing the clinician may go swimmingly. But if clinicians perceive a threat to their income, status, or employment, expect vigorous pushback.” The burden of proof regarding AI’s contribution to improving patient safety and the willingness to share power in AI implementation with direct care providers will rest squarely on management’s shoulders.

Acknowledgements: Bob Wachter, Bruce Vladek and Trevor Goldsmith read this essay and had constructive and helpful comments.