Collaboration Must Start at Protocol Design

In a recent video interview with Applied Clinical Trials, Jonathan Andrus, co-CEO of CRIO, discussed how 2026 is expected to mark a continued shift toward site-based technologies and protocol-driven eSource to improve data quality, compliance, and trial efficiency. He emphasized the importance of capturing high-quality data at the point of patient encounter, reducing fragmentation across sites, and enabling real-time data access and monitoring. Andrus also highlighted the growing need for cross-functional collaboration during protocol design, stronger governance across the data lifecycle, and increased use of AI to streamline study build, data review, and operational workflows.

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 changes in cross-functional collaboration will be required as trials become more data-driven and technology-enabled?

Andrus: Collaboration really needs to start earlier—during protocol development, not after the fact. Clinical operations, data management, and technology teams need to align on source workflows before sites are activated and enrollment begins.

If organizations want to embrace a central eSource, site-first model, they need to design protocols with those workflows in mind. That also means involving sites as design partners, not just end users.

At the end of the day, sites have the greatest influence on data quality because they are the ones generating the data. Their input during study setup helps prevent downstream compliance and efficiency issues.

There also needs to be strong collaboration across sponsors, CROs, and technology vendors, along with a clear focus on measurable outcomes—such as time to database lock, query resolution timelines, and reduction in site burden.

Finally, deeper integration between eSource and downstream systems like EDC is critical. Enabling data to move between systems without manual transcription is a key step toward improving efficiency.