The debate over billing codes for artificial intelligence (AI)-enabled clinical services has exposed a longstanding problem in healthcare payment: Nursing creates substantial clinical and economic value, but much of that value remains difficult to see in the way healthcare is financed.

Nurses assess patients, recognize changes in condition, prevent complications, coordinate care, educate patients and families, and make clinical judgments that influence outcomes and healthcare utilization. In hospitals, however, nursing is generally financed through facility payments rather than identified as a separate professional service. Hospitals receive resources intended to support nursing care, but those resources aren’t separately attributable to nursing or necessarily reinvested in the nursing workforce.

The emergence of proposed coding for clinically meaningful algorithmic analyses makes that problem more visible. If healthcare can develop mechanisms to identify and potentially reimburse an algorithm’s clinical analysis, nursing is justified in asking why comparable nursing assessment and judgment remain financially obscured.

Separate nursing billing codes may be part of the solution. But they don’t address the more fundamental question: How should healthcare measure, allocate, and govern the value created by both its workforce and its technology?

As AI becomes more integrated into care, nursing should focus on four priorities: transparency, reinvestment, workforce protection, and meaningful authority over AI governance.

Make AI’s costs and benefits visible

Healthcare organizations should be required to demonstrate the value AI actually creates rather than relying on projected efficiencies.

That requires distinguishing among gross revenue, net revenue, projected savings, verified savings, avoided costs, and released clinical capacity. Organizations also need to account for what they spend to acquire, implement, validate, monitor, and maintain AI systems, including payments to technology vendors.

Equally important is understanding what happens to clinical work.

Consider an AI tool that reduces nursing documentation time. The recovered time has value, but it isn’t necessarily a financial savings. If nurses have to spend additional time verifying the accuracy of the notes and correcting omissions and inaccuracies, this additional time and cognitive burden can offset the initial time savings, and so it needs to be subtracted to determine actual time saved. After that’s done, if nurses use that time to provide additional patient care, the organization has created clinical capacity. If, instead, staffing is reduced, this may become a financial savings—but one achieved by reducing nursing resources.

Importantly, an algorithm may automate an analysis while creating downstream nursing responsibilities and cognitive workload that includes assessing its relevance, responding to alerts, explaining results to patients, correcting errors, coordinating follow-up, and monitoring outcomes. These downstream impacts must be accounted for.

Any credible calculation of AI’s value must account for work that’s eliminated, created, and transferred.

Reinvest verified gains in patient care

When AI produces verified financial gains or additional clinical capacity, healthcare organizations should determine prospectively how some of that value will be reinvested in patient care.

Investment could include staffing, retention, continuing education, safer work environments, evidence-based practice, quality improvement, and nurse-led innovation.

The distinction between cost and investment matters here.

Nursing is frequently represented on organizational balance sheets primarily as a labor expense. But nursing resources also are productive assets: They contribute to preventing complications, reducing missed care, improving outcomes, and maintaining the clinical capacity of healthcare organizations.

Technology investment shouldn’t come at the expense of the human capital necessary to make that technology clinically useful.

Don’t automatically translate efficiency into fewer nurses

AI will change nursing work. That doesn’t mean every existing task or position should remain unchanged. But automation shouldn’t automatically become a justification for reducing clinical capacity.

Before eliminating nursing positions, organizations should examine unmet patient needs and determine whether released capacity can be redirected.

Time recovered from documentation could support direct patient care. More efficient discharge processes could create additional capacity for patient education, medication reconciliation, and follow-up. Nurses whose work changes substantially could move into care coordination, chronic disease management, population health, AI oversight, or safety monitoring.

The relevant question isn’t simply, “How many nursing hours can AI eliminate?” Rather, it’s, “How can AI allow existing nursing human capital to produce greater value for patients?”

Give nurses authority, not just responsibility

Nurses frequently become responsible for managing the downstream consequences of technologies they had little role in selecting.

That model needs to change.

Nurses should have formal decision-making roles before AI systems are purchased and throughout implementation and evaluation. They should help determine how tools fit clinical workflows, whether they increase or decrease workload, who’s responsible for acting on algorithmic output, how errors and bias are handled, how patients are informed, and how technology affects staffing and skill mix.

Nurses also should have a role in determining how verified financial gains and released clinical capacity are used.

Therefore, the AI billing-code debate presents nursing with a larger opportunity. Nursing can continue pursuing payment mechanisms that make its services more visible, but billability alone shouldn’t define economic value.

The stronger goal is a healthcare payment system that makes nursing investment visible, measures technological value accurately, and creates accountability for how both are used.

If AI creates value, the question shouldn’t focus on how much money it saves. We should ask whether that value is being used to strengthen the workforce, expand clinical capacity, and improve care.

And nurses should have the authority to help answer that question.

Olga Yakusheva Logan, PhD, MSPE, FAAN is a professor at Johns Hopkins School of Nursing, Baltimore, MD.