Jonathan Andrus, co-CEO of CRIO, outlines how disconnected systems and inconsistent data collection across sites create risk, while centralized eSource approaches present a major opportunity.
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: From a data management perspective, where do you see the biggest risk—and opportunity—for clinical trials this year?
Andrus: The biggest risk is fragmented, disconnected data collection across sites. If you think about a large phase 3 trial with 150 sites, each site may be collecting data differently—paper, EHR, hybrid approaches—creating significant variability in how data originate.
From a clinical data management perspective, that inconsistency creates downstream challenges in data quality, reconciliation, and efficiency.
The opportunity is the opposite of that: purpose-built eSource systems that reduce fragmentation and minimize transcription. When sites can document research activities once in a system designed specifically for clinical research, you eliminate a major source of data discrepancies and delays.
It also enables remote monitoring and verification, improves CRO resource utilization, and allows teams to identify data issues much earlier—at the point of capture rather than after data entry into EDC systems. That shift has a significant impact on both efficiency and data quality.
