AI Gets Billing Codes Before Nurses Do — What the CMAA Development Means for Healthcare AI Governance Programs

AI Industry Watch

A quiet but consequential policy process is underway at the American Medical Association that could reshape how American hospitals make financial decisions about AI and nursing for the next generation. The AMA is developing a new class of billing codes — called Clinically Meaningful Algorithmic Analyses, or CMAA codes — that would allow hospitals to bill directly for clinical work performed entirely by software, with no physician at the point of care. At the same time, the approximately 1.9 million registered nurses working in US hospitals still have no billing codes of their own, their work bundled invisibly into the daily room charge as it has been for roughly a century.

The policy window to influence this is open right now. Two comment deadlines — August 10 for the AMA's process and September 14 for CMS's proposed 2027 payment rule — mean the rules governing AI reimbursement in healthcare are being written this summer. For healthcare AI governance program leaders, this is a story worth understanding before those rules are finalized.

What CMAA Codes Are and Why They Matter

CPT codes are the standardized billing language of American healthcare — more than 11,000 codes maintained by an AMA-convened panel that describe every service a hospital or provider can charge for. If a service has a code, it generates revenue. If it doesn't, the payment system cannot see it.

CMAA is a proposed new category for situations where an algorithm analyzes patient data — a scan, a lab result, a heart rhythm — and produces a result that changes care, even when no physician does traditional work at the point of service. The AMA's own language describes the new codes as serving services "whether performed by physicians, other qualified health care professionals or solely provided by AI-enabled algorithms." Bedside nurses do not appear in that sentence.

The timeline is already moving:

2021: AMA adds Appendix S to the CPT code set — a framework classifying clinical AI as assistive, augmentative, or autonomous based on how much of the work the software does
September 2025: The CPT Editorial Panel holds its first formal discussion of the CMAA framework for services that don't require physician work at all
January 1, 2026: The CPT 2026 code set takes effect with 418 changes, including new codes for AI-enabled services like coronary plaque analysis and algorithmic ECG reading — early forms of AI billing are already live
May 2026: The panel accepts revisions to Appendix S sharpening the definition of a "clinically meaningful output" — widely read as groundwork for CMAA codes that could eventually reach Medicare
July 14, 2026: CMS releases its proposed 2027 payment rules, which for the first time includes a temporary payment category for clinical AI tools called "Software as a Medical Service"
August 10, 2026: AMA comment deadline for the next CPT Editorial Panel cycle
September 14, 2026: CMS comment deadline for the proposed 2027 payment rule — open to any nurse, no application required

This is not a slow-moving hypothetical. The AMA says the panel is already receiving a steady increase in applications for services that rely entirely on algorithmic analysis, without traditional physician work at the point of care. The demand for a way to bill for autonomous AI is real, and the infrastructure is being built to meet it.

The Nursing Billing Gap — In Numbers

To understand why the CMAA development is generating concern across nursing, you need to understand the current billing structure for nursing care. The Commission for Nurse Reimbursement, a nonprofit working to modernize how nursing is paid for, reviewed price transparency data from one academic medical center with more than 1,000 beds. It found 158,475 individual billable line items and not a single inpatient nursing charge.

That gap scales to an enormous number. Nurses account for roughly 30% of hospital labor spending — approximately $266 billion a year. That entire contribution appears in hospital finances as a cost to be managed, not a service that generates revenue. Nursing care has been bundled into the daily room-and-board charge for approximately a century — priced the way a hotel prices housekeeping. The bed is billed. The nurse comes with it.

Advanced practice nurses — nurse practitioners, CRNAs, clinical nurse specialists — can and do bill under CPT codes. The gap is specifically bedside RN care. Oregon allows RN billing under Medicaid; North Carolina's Institute of Medicine has recommended RN billing codes. But as a rule, bedside nursing work has no presence in the CPT billing architecture.

The Commission's Rebecca Love, who first raised the CMAA concern publicly in a widely shared LinkedIn post more than a year ago, framed the financial dynamic that governance leaders need to understand: "No CEO will ever sign a memo that says 'We're cutting nurses for AI.' But the reimbursement model will make that decision anyway, one budget cycle at a time."

The Unbundling Argument

The standard response to proposals for direct nursing reimbursement has been that nursing work is too continuous, too interwoven with everything else at the bedside, too complex to break into discrete billable units. CMAA is the AMA solving essentially that same problem for software. If the argument was that algorithms' clinical contributions were also too diffuse and complex to bill for separately, CMAA resolves it — and in doing so, removes the primary objection to nursing billing codes as well.

The parallel goes deeper. Autonomous clinical AI does not operate without human response. When an algorithm identifies deterioration, flags an abnormal rhythm, or produces a clinically actionable output, someone has to act on it. In hospitals, that person is overwhelmingly a nurse. Under the emerging reimbursement structure, the algorithm's contribution would be directly billable. The nurse acting on its output remains a cost.

"If the AMA can build an entirely new billing category for AI, it can build one for nursing. For decades, health systems have been told nursing work is too complex to unbundle nurses from the room rate and bill. However, the very process used to develop AI's CMAA codes allows us to develop a CMAA equivalent for nursing. We must act now, for if we don't, AI will be reimbursed for the very work nursing has long done but was never able to bill for."

What This Means for Healthcare AI Governance Programs

Reimbursement Structures Are AI Governance Inputs

Healthcare AI governance programs typically focus on the clinical and security dimensions of AI deployment — model validation, data governance, HIPAA compliance, safety monitoring. The reimbursement architecture that governs what AI work gets paid for directly shapes which AI tools get purchased, how they are deployed, and what clinical workflows they are positioned to replace or supplement. A governance program that does not account for the financial incentive structure being built around AI is missing a significant variable in how deployment decisions actually get made.

The CMAA development is not primarily a technical or security story. It is a financial architecture story — and financial architecture drives organizational behavior. Healthcare AI governance leaders who understand the reimbursement trajectory will be better positioned to anticipate which AI investments their institutions prioritize, which workflows get targeted for automation, and what the downstream workforce implications are for the clinical teams their AI programs support.

Nursing Presence on AI Governance Committees Is a Structural Question

The Nurse.org analysis documents 127 confirmed AI deployments across 106 large US health systems, spanning ambient documentation, predictive deterioration scoring, and virtual nursing platforms. Most of that AI runs directly within nursing workflows — augmenting, alerting, or automating tasks that bedside nurses perform. Yet only a small number of the tracked health systems have AI language in nurses' union contracts, and the billing codes being written this summer are being developed largely without nursing at the table.

Healthcare AI governance committees that do not include clinical nursing representation are making decisions about tools that predominantly operate in nursing workflows, evaluated by people who are not the primary users of those tools. That is a governance gap independent of any reimbursement consideration. The CMAA debate makes it visible in financial terms — but the representation question is real whether or not CMAA codes are adopted.

The "AI as Revenue, Nursing as Cost" Dynamic Affects Security Program Staffing

Healthcare security programs depend on clinical nursing cooperation for a range of security functions: security awareness training for clinical staff, reporting of phishing attempts and suspicious activity, participation in incident response exercises, and implementation of security controls in clinical workflows. A hospital financial model that treats AI tools as revenue-generating assets and nursing as a cost center subject to management creates workforce conditions that affect clinical staff capacity for security-adjacent work — and potentially accelerates attrition in the clinical workforce that security programs rely on for distributed security awareness.

This is not a near-term operational concern for most healthcare security teams today. It is a medium-term organizational dynamics concern worth tracking as CMAA codes advance and budget decisions begin to reflect the new reimbursement incentives.

The CMS Comment Window Is Directly Actionable for Healthcare Organizations

Unlike the AMA's process — which requires Interested Party access approval, making it more accessible to nursing organizations than to individuals — the CMS comment process for the proposed 2027 payment rule is open to anyone without application or approval. The proposed rule, designated CMS-1848-P, is open for public comment at regulations.gov through September 14, 2026.

Healthcare organizations that have views on how clinical AI reimbursement should be structured — including whether the "Software as a Medical Service" temporary payment category is appropriately designed, whether nursing work that supports AI-generated outputs should have its own reimbursement pathway, and whether the CMAA framework reflects the actual clinical value contributions being made by AI in hospital settings — can submit those views directly to CMS before the September 14 deadline. Healthcare AI governance leaders who want their institution's perspective reflected in the 2027 payment rule need to act in the next six weeks.

The Governance Question Behind the Policy Question

The CMAA story is, at its core, a story about who gets counted in the financial architecture of AI-augmented healthcare. The payment system is being asked to recognize and compensate AI's clinical contributions at exactly the moment when AI is being integrated into clinical workflows that nurses have owned and operated for decades. The governance question healthcare organizations need to answer is not just what the right reimbursement policy is — it is who is at the table when their institution decides what position to take on that policy, and whether clinical nursing voices are part of that conversation before the rules are written rather than after.

For healthcare AI governance programs, the CMAA development is a prompt to ask whether your governance structure is positioned to see this kind of upstream policy risk — not just the technical and security dimensions of AI deployment, but the financial and workforce architecture that will shape how AI is used in clinical settings for the next decade.

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