Applied Clinical Trials at the 2026 DIA Global Annual Meeting: Conversations on Data, Outsourcing, and the Patient Voice

Real-time clinical trials require standardized digital data capture at the point of care through scaled eSource-EDC-EHR integration, but progress stalls because competing standards lack regulatory enforcement and one-off system integrations cannot scale beyond single studies without industry-wide alignment and legal clarity on data ownership.

Trialbee CEO Matt Walz explores the current impact of AI in delivering practical gains in clinical development and reflects on its future potential, where enrollment predictability may be the biggest prize of all.

Radiopharmaceutical therapy is exposing a broader challenge for clinical development: scientific innovation can advance only as fast as the infrastructure, data, and cross-functional decisions required to deliver it.

AI agents in clinical operations acquire broader autonomous capability through expanded permissions, tools, memory, and delegated authority, requiring governance focused on whether effective capability has shifted outside approved boundaries rather than whether software has changed.

RWE is finding a role earlier in drug development than ever before, but the data quality, integration, and organizational alignment required to make it regulatory-grade are still catching up to the ambition.

In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, describe what operationalizing regulatory-grade quality frameworks requires—and why fit-for-purpose evidence generation is a transparency and documentation discipline, not just a data cleanliness exercise.

Real-time RBQM requires proportionate analysis frequency matched to data arrival and issue criticality, not continuous reanalysis, because signal churn from over-frequent runs creates alert fatigue and operational noise that obscures meaningful deterioration.

In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, explain how building trust into the data foundation—through source lineage, standardization, and reconciliation—is what makes reliable real-world evidence generation in oncology possible.