Jen Lamppa

See how real-world evidence is enabling smaller, smarter, and more efficient trial designs through hybrid models, external comparators, and continuous patient monitoring—while preserving the role of traditional clinical trials.

Assess the data quality, linkage, transparency, and auditability challenges that sponsors must overcome to make de-identified real-world evidence fit for regulatory submissions.

Understand where real-world evidence most effectively complements or substitutes traditional trial data, from post-market surveillance and label expansion to challenging areas such as rare disease research.

Explore how large-scale, de-identified real-world datasets enable more representative trial design, improve site selection, and support patient identification beyond the limits of traditional clinical study populations.

Examine how the FDA’s acceptance of de-identified real-world evidence shifts clinical operations workflows and why understanding the difference between pseudonymized and anonymized data is now critical for privacy, compliance, and evidence generation.

Robust data sets which can effectively represent diverse populations are key to driving greater inclusivity in trials.