Overview: What HHS Is Asking
In January 2026, the U.S. Department of Health & Human Services (HHS) and the Assistant Secretary for Technology Policy/Office of the National Coordinator for Health Information Technology (ASTP/ONC) published a Request for Information (RFI). The goal is clear: accelerate the adoption of artificial intelligence (AI) in clinical care settings across the United States.
The RFI aligns with priorities in the White House’s AI Action Plan and recent Executive Orders on AI. It asks the public a direct question — what concrete actions can HHS take to build a forward-leaning, industry-supportive, and secure approach to clinical AI adoption? The comment period closed on February 23, 2026, and HHS received nearly 500 public submissions.
This initiative signals a critical shift. Federal policy is moving from cautious observation to active facilitation of AI in healthcare. Therefore, providers, developers, and payers need to understand what is being proposed — and what may come next.
Three Core Policy Approaches
HHS and ASTP/ONC are seeking concrete, experience-based feedback from a wide range of stakeholders. These include AI tool developers, healthcare providers using AI today, and organizations facing barriers to adoption. The public input will shape an HHS-wide strategy built around three core pillars.
Regulation
HHS asks how current federal regulations affect AI adoption in clinical care. The RFI specifically seeks input on what regulatory adjustments would better support appropriate AI deployment. Many stakeholders note that clinical AI tools falling outside traditional medical-device pathways face unclear accountability standards. This uncertainty can slow or block deployment entirely. Clearer federal guidance on validation, verification, and post-deployment monitoring is a top ask from commenters.
Reimbursement
Payment policy is another major focus. The RFI seeks views on changes that would give payers the incentive and ability to promote access to high-value AI clinical tools. Moreover, it asks how payment reform can foster competition among AI developers and improve affordability. Commenters widely note that AI’s value — in efficiency, prevention, and care coordination — often goes uncaptured under current payment models. Thus, aligning incentives with real-world AI benefits is a priority.
Research and Development
Finally, HHS solicits ideas on how federal investment in research and development (R&D) can integrate AI into care delivery. This includes public-private partnerships and cooperative research agreements. The overarching goal is to create long-term market opportunities while improving health and wellbeing for patients and communities alike.
Additional Questions HHS Is Exploring
Beyond the three core pillars, the RFI goes further. It probes the limits of private-sector AI innovation and asks what the federal government must change to enable effective adoption at scale. Stakeholders can submit comments on several specific topics:
- Barriers to adoption — including data access, interoperability, and organizational capacity
- Regulatory, payment, and program priorities — what HHS should tackle first
- Legal and implementation issues — covering liability, privacy, and security concerns
- Evaluation methods — metrics, robustness testing, and workflow integration
- Support mechanisms — grants, contracts, prize competitions, certification, and accreditation
Interoperability emerges as a recurring concern. Several commenters stress that effective clinical AI depends on data that clinicians can exchange and meaningfully compare across settings. Others highlight the risk of context mismatch — where tools trained in one environment produce biased or misleading results when deployed elsewhere.
What Stakeholders Are Saying
The nearly 500 submissions come from professional associations, health systems, accrediting organizations, policy groups, and AI developers. Together, they paint a clear picture of the landscape.
Common Adoption Barriers
Commenters consistently identify two root-cause barriers: data and interoperability limitations and organizational capacity constraints. Many argue that without representative data and standardized exchange, even the best AI tools fail to perform reliably across diverse clinical environments.
Regulatory Clarity Is Urgently Needed
Multiple submissions point to regulatory uncertainty as a major obstacle. Clinical AI tools that fall outside standard medical-device pathways face unclear expectations around accountability, liability, and post-deployment monitoring. Commenters urge HHS to publish clearer standards for validation and ongoing surveillance.
Payment Models Must Catch Up
On the reimbursement side, many commenters describe misaligned incentives. AI often delivers value through efficiency gains, early prevention, and care coordination — outcomes that existing payment models do not adequately reward. Reforming payment policy to reflect these real-world benefits is a widely shared recommendation.
Building Trust Through Transparency
Several submissions urge HHS to invest in a trust and evaluation infrastructure. This includes standardized reporting through “model cards,” shared benchmarking resources, and accreditation guidance. Commenters also stress the need for patient-facing transparency — so that individuals know when and how AI tools influence their care — alongside strong protections for privacy and security.
Key Takeaways for Healthcare Providers
HHS and ASTP/ONC frame clinical AI adoption as both a policy challenge and an implementation challenge — not merely a technical one. Public comments reinforce this view strongly. Providers should anticipate that upcoming HHS action will focus on three areas:
- Clarifying validation and monitoring expectations — so providers can adopt AI with confidence
- Improving data access and interoperability — to enable reliable, representative AI performance
- Aligning payment policies with AI’s demonstrated value — in efficiency, prevention, and care coordination
If HHS executes effectively on these fronts, providers stand to gain a clearer path to adoption. Furthermore, patients could benefit from improved outcomes, and clinicians could experience reduced administrative burden. Staying informed on HHS’s next policy moves is essential for any organization planning to integrate AI into clinical workflows.
