Operational and Governance Barriers to Regulatory-Grade RWE

In a recent video interview with Applied Clinical Trials, Jen Lamppa, vice president of commercial strategy at Inovalon, discussed the clinical operations impact of the FDA’s evolving guidance on real-world evidence submissions using de-identified patient data. Lamppa explained the critical distinction between pseudonymized and anonymized data and outlines how large, de-identified datasets are reshaping trial design, site strategy, and patient selection. She described where real-world evidence most effectively complements traditional trials—particularly in observational and post-market settings—while highlighting the operational, data governance, and methodological hurdles that still limit broader regulatory adoption. Lamppa concluded by explaining how real-world evidence is poised to augment, rather than replace, traditional trials by enabling smarter, more efficient, and more representative evidence generation.

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

ACT: What operational or data governance hurdles still limit broader use of de-identified RWE in regulatory submissions?

Lamppa: First, let’s remember that the current guidance is for medical device submissions, so there’s still progress to be made for drugs and biologics.

Beyond that, there are long-standing hurdles. Data variability and quality are key. Not all real-world data sources meet regulatory expectations for completeness, accuracy, and traceability. De-identified data must still uphold the rigor required by the FDA.

There are also linkage challenges—maintaining longitudinal integrity without personally identifiable information requires sophisticated techniques like tokenization, normalization, and careful limitation of data transformation.

Methodological transparency is critical. Sponsors must document how cohorts were built, how missingness was handled, and how bias was mitigated. Real-world data are messy, and that means more accountability in explaining how data were cleaned and analyzed.

Finally, governance and auditability matter. Regulators expect clear provenance and reproducibility. Large datasets are powerful but also more complex.

Not all secondary data are created equal. At Inovalon, we control primary data sourcing and own normalization, quality assurance, and transparent provenance, which helps make de-identified real-world data more submission-ready. But enabling use goes beyond encouragement—it requires all of these foundational steps.