Where Real-World Evidence Fits Along the Clinical Research Spectrum

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: As FDA guidance evolves, where do you see real-world evidence most realistically complementing—or replacing—traditional trial data?

Lamppa: Let’s baseline real-world evidence here, because when you mention traditional trial data, real-world evidence itself is part of the traditional trial data landscape. It’s actually been there for a while.

Think about the pragmatic spectrum of clinical research. On one side, you have interventional studies designed to test investigational treatments, like drugs being developed to treat unmet needs. On the other end, you have observational studies or post-market registries meant to track long-term safety and efficacy.

Traditional trial data spans that entire spectrum and usually refers to data collected for that primary research purpose. Where we continue to see growth is in secondary real-world data—data collected for a primary purpose that later sees secondary use in real-world evidence.

That’s the kind of data we use a lot here at Inovalon. On the pragmatic spectrum, secondary data most realistically complements or replaces traditional trials on the observational side—post-market surveillance, label expansion, long-term safety assessments.

Medical device manufacturers have significant experience here, particularly given post-market reporting requirements. That’s why we’re seeing FDA guidance emerge first in this area.

There are also areas where prospective studies are challenging or not feasible, like rare disease, where patient populations are small and difficult to randomize. Those have always been important use cases for secondary real-world data, and we expect those applications to continue expanding.