FDA Removes a Major Barrier to Using Real-World Evidence in Regulatory Reviews
The agency says it will now evaluate real-world evidence on a case-by-case basis, removing long-standing requirements for identifiable patient-level data in certain submissions.
Yesterday, the FDA announced a significant shift in how it will evaluate real-world evidence (RWE) in regulatory submissions—starting with medical devices and with plans to extend the approach to drugs and biologics.
For certain medical device submissions, the FDA will now accept real-world evidence without always requiring identifiable, individual-level patient data to be submitted as part of a marketing application.
Historically, FDA review standards required sponsors to submit source-level, individually identifiable clinical data (the same level of granularity expected in traditional clinical trial submissions). This requirement had increasingly been applied to RWE datasets as well.
Under the new approach, FDA reviewers will evaluate the strength of submitted RWE on a case-by-case basis, even when the underlying data come from de-identified, aggregated sources.
Why the FDA says the old standard was “impractical”
The agency explicitly acknowledged that requiring individual-level data made it unworkable to use many large, high-value RWE sources, including:
National disease registries
Insurance claims databases
Electronic health record (EHR) networks
Large hospital system datasets
These sources often contain valuable macro-level insights but cannot realistically be restructured to provide identifiable patient-level datasets suitable for FDA submission. The FDA stated that this expectation had become a major barrier to meaningful use of RWE, despite years of policy encouragement.
A departure from recent policy
This shift is notable because it walks back elements of the FDA’s recent RWE oversight framework, not just older policy. In August 2023, the FDA finalized guidance describing how it would oversee RWE studies, which reinforced expectations that sponsors would submit source-level data and outlined mechanisms (such as Drug Master Files) to enable FDA access.
That framework reflected the FDA’s longstanding view that granular data access was essential to independently validate analyses. The December 2025 announcement explicitly acknowledges that this standard, when applied to large RWE datasets, limits rather than expands RWE’s practical use.
What the FDA is saying now
FDA leadership framed the change as a pragmatic reform rather than a loosening of standards. According to FDA Commissioner Marty Makary:
“We’re removing unnecessary barriers that have prevented us from using powerful real-world evidence to get life-changing treatments to patients faster.”
The agency emphasized that data quality, relevance, and methodological rigor still matter, but that identifiable patient data will not always be required to demonstrate evidentiary strength.
The FDA also provided some revealing historical context:
Since 2016, 35 drugs, biologics, or vaccines have included RWE in applications.
Over 250 medical device premarket authorizations have included RWE during the same period.
Even in devices, the FDA noted that RWE-based authorizations have plateaued in recent years.
This underscores that while RWE has been widely discussed, actual regulatory uptake has been limited—a problem the agency now openly attributes in part to its own data submission expectations.
What comes next
The policy change for devices will be formalized in a forthcoming guidance document, which has already cleared White House review. The FDA has stated it intends to consider similar updates for drugs and biologics, though no drug-specific guidance has been published yet. Review decisions will be case-specific, rather than governed by a one-size-fits-all data submission requirement.
Our read of this: It’s not a blanket approval of de-identified data, but it is a clear signal that the FDA is willing to reassess long-held assumptions about data granularity and sponsors may have new flexibility in how RWE is sourced and presented. Methodology, governance, and data integrity will likely matter more than raw data ownership—and internal controls around data provenance, validation, and analytical transparency will be critical as expectations evolve.
We’ll keep you updated as more details come out of the agency.
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