HHS launches "OneHHS" AI strategy to modernize operations, research, and public health
The Department of Health and Human Services has released an agency-wide AI strategy aimed at putting practical artificial intelligence in the hands of the federal workforce. The focus: streamline internal operations, strengthen research, and improve public health capabilities across divisions.
HHS also plans to use C3 for an interagency data platform that connects disease-specific NIH data with Medicare, Medicaid, claims, and state registry datasets. The objective is better data quality, tighter governance at CMS, and faster insights across programs.
Why this matters for leaders in strategy, finance, and operations
- Operational efficiency: Standardized platforms and shared services reduce duplication and manual work.
- Security at scale: Centralized governance and risk controls cut exposure across multiple agencies and systems.
- Data leverage: Integrated datasets open the door to stronger analytics, forecasting, and policy evaluation.
- Clear guardrails: Governance and risk management are baked in, which accelerates responsible adoption.
What's in the "OneHHS" strategy
The strategy invites every HHS division-CDC, CMS, FDA, NIH, and others-to build on a single department-wide AI infrastructure. It focuses first on internal use cases as directed by the Office of Management and Budget, not on integrating AI into the delivery of services.
HHS also signals future collaboration with private-sector partners to co-create solutions once internal foundations are set. Expect common tooling, shared standards, and streamlined workflows that improve throughput without adding risk.
The five pillars HHS will revisit and refine
- Governance and risk management that builds public trust
- Infrastructure and platforms built for user needs
- Workforce development and burden reduction to boost efficiency
- Health research and reproducibility grounded in gold-standard science
- Care and public health delivery modernization for better outcomes
Leadership and policy context
The effort is led by acting Chief Artificial Intelligence Officer Clark Minor and delivers on the administration's AI Action Plan, related executive orders, and OMB guidance. For governance alignment, organizations can reference the NIST AI Risk Management Framework.
What to do now if you run strategy, finance, or operations
- Map priority use cases: Target high-volume, rules-based processes, quality reporting, fraud/waste/abuse monitoring, and research workflows.
- Stand up data foundations: Inventory datasets, set owners, define quality thresholds, and implement data access policies.
- Establish guardrails: Create model inventory, approval workflows, human-in-the-loop checkpoints, and incident response guidelines.
- Upskill your teams: Train analysts, clinicians, and operators on prompts, validation, and AI-enabled workflows. See role-based options at Complete AI Training - Courses by Job and Popular AI Certifications.
- Manage vendors: Require documented model lineage, testing evidence, monitoring plans, and clear data usage terms.
- Measure outcomes: Tie AI deployments to operating metrics-cost per claim, time-to-insight, error rates, and compliance findings.
Interagency data platform: what to expect
The C3-powered platform aims to link NIH research datasets with Medicare, Medicaid, claims, and state registries. Executives should anticipate tighter data governance standards, improved interoperability, and more consistent definitions across programs.
Plan for data-sharing agreements, lineage tracking, and validation processes that meet both security and audit requirements. The payoff is faster, cleaner analysis for public health and policy decisions.
What to watch
- Rollout timelines for the interagency data platform and initial pilots
- Cybersecurity requirements and continuous monitoring expectations
- Funding cycles tied to infrastructure, training, and model oversight
- Procurement language standardizing AI risk, testing, and reporting
- Coordination across CDC, CMS, FDA, NIH as shared services mature
Perspective from HHS
Deputy Secretary Jim O'Neill said the department is steering AI innovation toward patient-centered outcomes that can deliver meaningful public benefit-including longer, healthier lives. The direction is clear: build trust, improve efficiency, and use data to make smarter decisions at scale.
Bottom line
HHS is prioritizing internal modernization before external use. If you lead strategy, finance, or operations, now is the time to lock in data governance, workforce training, and vendor standards so you can move fast when shared services and platform access expand.
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