A Perspective on Risk Based Monitoring

Risk-based quality management depends on knowing what to monitor and on interpreting what the signals mean.

In Episode 4 of The Human Side of Clinical Trials, host Brian S. McGowan, PhD, FACEHP, chief learning officer and co-founder of ArcheMedX, Inc., speaks with Jason Riis, PhD, CEO and chief behavioral scientist at Behavioralize, about how capturing friction closer to the moment it happens can reveal unclear instructions, technology friction, and workarounds that retrospective surveys and debriefs tend to miss.

Data capture is no longer the bottleneck, and whether the lag between a signal and a decision shrinks depends on signal design, AI governance, and vendor orchestration.

In this Q&A, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, discusses what changes when AI is embedded across protocol strategy, data infrastructure, and workflow tools at once, where governance frameworks tend to fall short, and why CROs must orchestrate AI ecosystems rather than simply aggregate them.

The FDA’s RTCT initiative transmits predefined, clinically meaningful signals to regulators during active trials, so its value depends entirely on the maturity of sponsors’ upstream RBQM signal intelligence, KRIs, QTLs, and root-cause context, rather than on data speed.

What sponsors and sites should know about the AstraZeneca-Oracle AI collaboration on trial recruitment, safety monitoring and data governance.