A Policy Leader with a Technologist’s Lens
At the 2025 AI for PI Summit hosted by the AI for Payment Integrity (AI for PI) Community, Congressman Jay Obernolte delivered a powerful keynote, emphasizing the urgent change needed in healthcare cost containment and AI—and that the time to shape AI policy is not tomorrow, but right now.
The Congressman dispelled common misperceptions around AI in healthcare and shared the latest happenings on active federal and state-level AI legislation, FDA developments, and innovation strategy.
Representative Obernolte, who started as a computer scientist and then small business owner, brings a unique perspective to his legislative role. Now as co-chair of the bipartisan House Task Force on Artificial Intelligence, he is helping define the United States’ AI regulation policy. The Congressman previewed a compelling policy roadmap, strongly reiterating legislation should be bipartisan, practical, and support—not slow—innovation.
For an audience of healthcare payment integrity leaders, his message was clear: it’s time to lead, not lag on AI-driven claims processing for cost savings. Here were some of the top takeaways.
1. Misperception: “AI in healthcare is still a future concern” Reality: AI is already regulated—and widely used
“It might shock you to hear me say the FDA has already issued over a thousand permits for the use of AI in medical devices.”
AI isn’t coming—it’s here. The FDA is already quietly leading in AI regulation and has approved over 1,000 AI-powered medical devices, many of which are already in use. The Congressman emphasized that the FDA has been able to navigate this space successfully for the healthcare industry, showing that high-risk AI use cases can be regulated effectively, with sector-specific oversight when existing agency stakeholders are empowered with the right resources.
2. Healthcare cost reform and AI innovation are not two separate goals
“We have the best providers on the planet—and the highest healthcare costs on the planet.”
Obernolte framed AI as the missing ingredient that could resolve the U.S. healthcare paradox, particularly for payment integrity: world-class providers paired with unsustainable per capita spending. With AI automation and advanced analytics reducing administrative costs and enabling real-time, proactive intervention in payment integrity processes, payers’ and providers’ decision-making could improve as healthcare costs decline.
3. We need to regulate outcomes, not tools
“In the United States, we believe in regulating outcomes, not regulating tools.”
Unlike the EU, which is regulating the use of AI and AI-enabled predictive analytics broadly and preemptively, the Congressman emphasized a U.S. approach based on how AI is used, not what AI is. Rather than building an entirely new legal ecosystem, Obernolte advocates for applying existing laws to AI advancements. This includes financial fraud, discrimination, and other misuse—all of which already have legal consequences.
4. A hub-and-spoke model will empower sector regulators
Congressman Obernolte proposed a hub-and-spoke regulatory architecture for AI-enabled solutions, where existing agencies like CMS, FDA, and NHTSA would regulate AI in their domains, supported by a federal hub of AI experts, tools, and resources:
- Spokes = agencies like CMS, FDA, FAA
- Hub = centralized AI tools, testing protocols, and regulatory sandboxes
This streamline approach avoids duplicating decades of domain knowledge while equipping regulators to handle emerging AI-specific risks. The hub would provide technical talent, model validation tools, and shared infrastructure, improving workflows and operational efficiency.
“It makes more sense to teach the FDA about AI than to teach a new AI agency everything the FDA knows about patient safety.”
5. Fragmented state AI laws are a looming threat
“There are over 1,000 AI regulation bills in state legislatures this year alone.”
Unless Congress enacts a federal AI framework soon, states may enact conflicting laws like they did with digital privacy, creating hurdles for startups and burdening providers and payers with costly, inconsistent compliance requirements.
Without federal regulation, the Congressman warned that states like California and Colorado are advancing their own AI laws—some of which are already set to take effect in 2025.
6. AI will reduce waste and create jobs—not just eliminate them
Obernolte took direct aim at the narrative that AI means mass layoffs for healthcare organizations. Citing radiology as a proof point, the Congressman showed that automation of routine tasks actually increases service demand by making diagnostics more affordable and accessible. Similar trends are likely in medical coding, claims auditing, and care coordination.
“If the cost of the test is lower, more people will get them done. That means more cases and more need for radiologists—not fewer.”
He added that generative AI could even lead to personalized entertainment, and by extension, personalized healthcare communication or education tools for healthcare systems.
7. Data fragmentation could undermine AI fairness
“If only some people say yes to data use, scientists have to ask: what do those who said no have in common? The answer is almost never nothing.”
The Congressman explained how privacy frameworks like HIPAA are a strong foundation, but raised a subtle but critical issue: uneven or opt-in-only data access can cause algorithms bias in model training for payment integrity processes. He warned that missing subpopulations create blind spots for healthcare payers and can compromise claims data and payment accuracy.
“This phenomenon was a lot less well understood five years ago. We now know it’s not just about having data—it’s about having the right, representative data.”
8. Misperception: “Only tech giants can afford to innovate in AI.” Reality: A national AI research resource could democratize innovation.
Obernolte cautioned that more AI advancements are occurring within private companies than in academia, due to access barriers around compute and data. The healthcare ecosystem benefits from transparency—shared benchmarks, reproducibility, and oversight. Preserving a public research pipeline is vital to trust and safety in AI deployment, including for payment integrity.
“Only large companies can afford the compute and data to create frontier AI algorithms. That shuts out entrepreneurs.”
To level the playing field, Obernolte championed the CREATE AI Act, a bipartisan bill that would establish a federal research hub offering data and compute access to startups, academic researchers, and small innovators. He noted that companies are willing to donate unused compute and training resources to such a pool, and the federal government would simply act as an access manager.
Final Thought: The window to shape policy is now—or risk being sidelined
Congressman Obernolte’s keynote dispelled the notion that AI’s future hinges solely on technology. It also depends on mindset, urgency, and the choices leaders make today about regulation, infrastructure, and innovation equity.
Obernolte emphasized that his bipartisan Congress task force has already developed a 270-page, bipartisan task force report with 85 policy recommendations. But if federal legislation stalls this year, state laws will dominate—and the chance to create coherent national policy may be lost. Regulatory clarity and thoughtful policy are critical to keeping healthcare cost containment and payment integrity strategies aligned with the pace of innovation.
Why it matters: Leaders in healthcare, payment integrity, Medicare, Medicaid, and providers who want regulatory clarity and scalable innovation should engage now—through policy feedback, industry coalitions, or public-private collaboration.
For more, see the video highlighting Congressman Obernolte’s session. Follow us for more AI Summit insights: Join the AI for PI Community Group and follow the AI for PI Community LinkedIn page.
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