Artificial intelligence (AI) is poised to reshape the healthcare landscape by generating new value, yet there is a pressing need for nursing to play a central role in governing this transformation. This highlights a critical issue in the healthcare payment system: while nursing contributes substantial clinical and economic benefits, much of this value remains hidden in current financing models.
Nurses engage in a wide range of activities—patient assessments, condition monitoring, complication prevention, care coordination, and patient education—that significantly influence patient outcomes and healthcare resource utilization. However, in most hospital settings, nursing is funded through general facility payments, rather than being recognized as a distinct professional service. Although healthcare institutions receive funding aimed at supporting nursing care, this financing is often not allocated specifically for nursing or reinvested in the nursing workforce.
The introduction of new coding systems for AI-driven clinical analyses sheds light on this issue. If healthcare organizations can establish mechanisms for recognizing and reimbursing algorithmic assessments, nursing can rightfully question why its own evaluations and judgments are not given similar financial recognition.
While the creation of distinct billing codes for nursing services might address some concerns, it does not tackle a more critical issue: how should the healthcare system evaluate, allocate, and manage the value generated by both its workforce and technological advancements?
As AI is integrated more deeply into healthcare, nursing should prioritize four key areas: ensuring transparency, advocating for reinvestment, protecting the workforce, and asserting meaningful governance over AI technologies.
Making the costs and benefits of AI transparent is essential. It is crucial that healthcare organizations not only provide projected efficiencies but demonstrate the actual value generated by AI implementations. This entails breaking down statistics into categories such as gross revenue, net revenue, projected versus verified savings, avoided costs, and improved clinical capacity, in addition to disclosing expenses related to the acquisition, implementation, validation, monitoring, and maintenance of AI technologies.
Moreover, attention must be paid to the implications for clinical practice. For example, while an AI tool that reduces documentation time appears beneficial, if nurses are then tasked with verifying the accuracy of entries and rectifying errors, the initial time savings may diminish. Any effective assessment of AI's value must factor in not only the time saved but also the work created and transferred.
Once verified financial gains or clinical improvements arise from AI, healthcare organizations should plan to reinvest a portion of that value back into patient care. This reinvestment may include support for staffing, staff retention, ongoing education, enhanced work environments, adherence to evidence-based practices, quality improvements, and innovations led by nurses.
It is vital to differentiate between cost and investment. Nursing roles are frequently viewed solely as labor costs on an organizational balance sheet, but they also function as productive assets that help minimize complications, reduce missed opportunities in care, and boost overall patient outcomes.
AI will undoubtedly transform nursing practices, but this shouldn't automatically lead to staff reductions. Organizations should first assess existing patient needs and reallocate resources, rather than simply reducing the workforce. For instance, time saved from documentation could be redirected towards direct patient care or other valuable nursing tasks.
Crucially, nurses must not only bear responsibility for managing the consequences of technologies they did not select but should also have a say in the decision-making process surrounding AI implementations. Their involvement is essential from the selection phase through to evaluation, ensuring that the integration of these tools is aligned with clinical workflows and that the implications for workload and staffing are thoroughly considered.
Thus, the ongoing dialogue regarding AI billing codes presents a significant opportunity for nursing. While advocating for payment mechanisms that enhance the visibility of nursing services is important, the overarching goal should be to establish a healthcare payment system that accurately reflects the investment in nursing, captures the technological value, and holds stakeholders accountable for its application.
Instead of merely asking how much money AI saves, the focus should shift to whether that value is being utilized to bolster the nursing workforce, enhance clinical capacity, and improve patient care—and nurses should have the authority to guide these important discussions.
Olga Yakusheva, a distinguished professor at Johns Hopkins School of Nursing in Baltimore, MD, emphasizes these critical issues in her advocacy for nursing’s role in the future of healthcare.
