Innovation in U.S. healthcare is often narrated as a story of disruption. Think: bold startups, cutting-edge artificial intelligence (AI) and innovative technology.
But that narrative, while appealing, obscures a more accurate and more useful truth. In healthcare, innovation rarely begins with disruption. Instead, it begins, by design, with regulation.
As underscored this week by the launch of Claude for Healthcare on Sunday (Jan. 11), data, control and the ability to innovate within strict and often fragmented regulatory boundaries are the realities that need to be bridged for healthcare innovation to scale.
Unlike consumer technology or media, healthcare operates inside one of the most demanding regulatory environments in the economy. Patient safety, data privacy, reimbursement integrity and clinical efficacy are enforced through overlapping federal and state rules, payer requirements and professional standards. For many innovators, these constraints may feel like friction. In reality, they are the scaffolding that makes durable innovation possible.
Hhealthcare’s most enduring breakthroughs often emerge not in spite of regulation, but because innovators design directly into it. Electronic prescribing scaled because it aligned with controlled substance rules. Telehealth expanded rapidly once reimbursement and licensure frameworks adapted. Even clinical AI tools that gain traction today do so by demonstrating regulatory literacy, such as with Anthropic’s approach with Claude for Healthcare, not regulatory avoidance.
The lesson is straightforward: in healthcare, compliance is not a tax on innovation. It is increasingly the price of admission.
See more: PYMNTS’ 2025 Healthcare Coverage Followed the Money and the Friction
Healthcare Interoperability Is a Governance Problem
Healthcare regulation in the U.S. is fragmented by design. Clinical oversight flows through agencies like the Food and Drug Administration. Payment and coverage rules are governed by the Centers for Medicare & Medicaid Services. Data privacy is shaped by HIPAA and a growing patchwork of state-level protections. Layered on top are accreditation bodies, professional guidelines and contractual obligations imposed by health systems and insurers.
Health systems worry about data misuse and competitive risk. Payers focus on compliance exposure. Vendors guard proprietary models. Regulators, appropriately, prioritize patient protection over speed. The result is a system where technically possible integrations are institutionally fragile.
Against this backdrop, if regulation is the foundation, data is the obstacle where innovation most often stalls.
Healthcare is awash in data but starved of usable information. Regulatory compliance shapes how data is captured, stored and shared. Privacy rules constrain movement. Security standards add friction. Consent requirements vary by use case. Each of these constraints is rational in isolation. Together, they create a landscape where innovation cannot scale unless data flows are deliberately engineered across organizational and regulatory boundaries.
“I had an MRI done recently, and the results were literally handed to me on a CD-rom,” said Aditya Bansod, co-founder and CTO of Luma Health, during a November discussion hosted by PYMNTS CEO Karen Webster.
For years, interoperability has been framed as a standards challenge. If only systems adopted the same APIs, the logic went, data would flow freely. But the more challenging barrier has proven to be governance: who is allowed to share what data, with whom, for which purpose, and under what liability.
These questions are not answered by code. They are answered by contracts, policies and regulatory interpretation.
Read also: Longevity Rewrites the Economics of Inheritance
Scaling Innovation in a Fragmented System
Looking ahead, healthcare will not become less regulated. If anything, scrutiny around data use, AI and patient rights is intensifying. The question is not whether innovators can avoid regulation, but whether they can scale within it.
As data moves beyond direct care into analytics, AI, population health and commercialization, the meaning of consent shifts. Patients may agree to share data for treatment but not for research. They may consent to one app but not another. They may revoke permission entirely.
Healthcare institutions sit at the center of this complexity. They are responsible not only for capturing consent, but for enforcing it across systems, partners and time. This is operationally hard and legally fraught. When consent models are unclear, institutions default to restriction.
Products that integrate seamlessly into electronic health records, payer workflows and compliance reporting systems reduce institutional risk. They are easier to adopt, easier to defend internally and harder to replace.
As covered here last year, research shows that close to half of healthcare and life-sciences organizations have generative AI in production use, in many cases for documentation, administrative work and early-stage clinical summaries.
Healthcare’s fragmentation is often framed as a barrier to innovation. In reality, it is also a source of defensibility. Solutions that successfully navigate regulatory nuance, contractual complexity and institutional risk build moats that are hard to cross.
