Pacing obesity trial enrollment, anchoring eligibility to stable real-world therapies, and building site and physician relationships improve retention and data quality more than aggressive recruitment or restrictive exclusion criteria do.

In this video interview, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, argues that CROs must act as orchestrators rather than aggregators—and that the real test of any AI ecosystem is whether it reduces sponsor coordination burden or just hands it back to them.

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

In this video interview, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, identifies three recurring failure modes in AI governance frameworks—and explains why responsible AI must be an ongoing operating discipline, not a one-time approval gate.

In this video interview, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, explains why the shift from narrow AI tools to a continuous learning clinical operating system is where the real operational value lies—and what it takes to get there.

In this Q&A, Saamir Pasha and Carole Berini, PhD, of Ontada discuss what it takes to harmonize fragmented oncology data into reliable real-world evidence, how lab markers can proxy for missing functional status data, and where the biggest remaining barriers to consistent RWE generation lie.

Clinical trial endpoints define what success looks like, but patients and caregivers are rarely the ones setting that definition, and the gap between what trials measure and what patients actually value is showing up in post-approval evidence, regulatory submissions, and the design rooms where protocols are written.

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.