How Real-World Evidence Will Change Trials Without Replacing Them

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: Do you expect this shift to reduce reliance on certain trials, or primarily change how trials are run?

Lamppa: The shift toward non-identified patients and real-world evidence use cases is less about eliminating trials and more about making them smarter, smaller, and more efficient.

We expect more hybrid designs that combine randomized controlled trial data with real-world evidence, smaller control arms supplemented by external comparators, fewer large traditional post-market studies, and greater reliance on continuous real-world monitoring.

That monitoring allows us to see what’s happening to patients at scale and make informed decisions not just about approvals, but about how treatments are managed post-market.

Certain low-risk or mature devices may see reduced trial requirements, but the broader trend is toward augmenting—not replacing—traditional evidence. Real-world data are powerful and representative, but also complex, and must be used thoughtfully with the right data for the right purpose.