FDA RWE Guidance Redefines Data Use in Clinical Operations

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 is the biggest clinical operations implication of the FDA allowing real-world evidence submissions without identifiable patient data?

Lamppa: So, to dig into this question a bit, I think it’s important—I’ve had this discussion with many clinical operations folks over past lives—to understand and really appreciate the distinction between pseudonymized patient data versus anonymized patient data.

Traditional clinical trial data are pseudonymized data, and these data may not contain information on a patient that directly identifies them, like a name, address, or phone number. However, pseudonymized data can still be linked back to an individual patient and is therefore identifiable data.

What we’re talking about here is de-identified patient data—patient data that has been de-identified or anonymized, like the real-world research datasets we have at Inovalon, that cannot be re-identified at all. The essential difference here has important implications for privacy, regulation, and how organizations handle patient-level data.

Clinical development stakeholders such as clinical operations, data management, biostatistics, and programming are all really adept at working with pseudonymized clinical trial data. The implication now is that it’s important for these teams to consider how anonymized patient data can and should be integrated across the spectrum of clinical development operations and applications, especially as part of the data package.