HHS seeks public input to speed responsible AI adoption in clinical care
On December 19, 2025, the U.S. Department of Health and Human Services (HHS) issued a Request for Information (RFI) to accelerate the use of artificial intelligence in clinical care for all Americans. The goal is straightforward: improve outcomes and experiences, cut provider burden, and deflate costs across the system.
The Office of the Deputy Secretary is leading this work using three levers-regulation, reimbursement, and research & development-to move from promising pilots to standard practice. This effort advances the Administration's commitment to American leadership in AI and supports the vision to Make America Healthy Again.
What HHS wants feedback on
- Regulation: How digital health and software frameworks should evolve for AI-driven tools while keeping patients safe. This includes expectations for validation, post-market performance, and updates to learning systems.
- Reimbursement: How payment structures can be simplified and better matched to technologies that improve efficiency and lower total cost of care.
- Research & implementation: Where to invest to strengthen implementation science, evidence generation, and best practices-especially for complex or high-acuity settings.
- Interoperability and privacy: How to ensure secure, lawful data use under HIPAA, improve data liquidity, and design trust into how information moves across settings.
- Future needs: Medium- and long-term priorities for conditions expected to grow in prevalence, such as frailty and dementia.
Why this matters for healthcare leaders
- Clearer guardrails: Practical guidance on clinical validation, updating models, and monitoring performance can reduce uncertainty for health systems and vendors.
- Better incentives: Payment updates can reward outcome gains and efficiency, making sustainable business cases for AI-supported workflows.
- Data you can trust: Interoperability work aims to give patients true access and give clinicians reliable inputs, while protecting privacy and security.
- Burden reduction: Thoughtful automation can remove low-value tasks, help close care gaps, and support quality improvement without adding clicks.
Who should respond
- Clinicians, care teams, and clinical leaders with frontline use cases and workflow needs
- Health systems, payers, and value-based care organizations with cost and quality data
- EHR vendors, data platforms, and AI developers building and integrating tools
- Patient advocates and caregivers, especially in chronic, frailty, and dementia care
- Researchers and quality bodies focused on safety, validation, bias, and outcomes
How to make your input useful (practical checklist)
- Define the clinical problem and target population. List the top outcome and process metrics you will move.
- Specify the workflow. Who uses the tool, when, and within which system? What training is required?
- Detail validation and safety plans: datasets, performance thresholds, external testing, monitoring, and rollback.
- Address data needs: sources, interoperability standards, consent, de-identification, and bias mitigation.
- Propose reimbursement models: codes, quality measures, episodes, or shared savings that fit the use case.
- Highlight implementation evidence: pilots, RCTs, or real-world data, plus time-to-value and cost of ownership.
- Map risks and guardrails: privacy, security, clinical liability, over-reliance, and escalation to human review.
- Call out system-level barriers: procurement, credentialing, change management, and EHR integration.
Data, trust, and interoperability
HHS is emphasizing secure, lawful use of patient data and interoperability that serves patients and clinicians. Expect focus on data liquidity, access, and safeguards that help AI tools strengthen care without compromising privacy.
For background on privacy requirements, see HIPAA resources from HHS: HIPAA Privacy Rule.
"OneHHS" approach
This RFI extends the HHS AI Strategy beyond internal operations to the broader health care system. The aim is coordinated action across HHS divisions so regulatory clarity, payment policy, and R&D investments move in step.
How to respond
Details and submission instructions are available here: HHS RFI on accelerating AI in clinical care.
Optional resource for teams building skills
If your organization is planning workforce upskilling for AI-enabled care, you can explore role-based learning paths: AI courses by job role.
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