Mixed Outsourcing Models Are Spreading. Is Sponsor Oversight Keeping Up?

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.

Standardized measurement across five oncology centers found that clinical research coordinators rated eSource-enabled workflows positively (Overall Mean Satisfaction 5.79 of 7), with reduced manual entry and electronic data transfer earning strong marks, while perceived reduction in sponsor and monitor queries lagged as the one domain below threshold.

In this video interview, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, describes the industry’s trajectory toward a unified intelligence orchestration environment—and explains why the future isn’t one model that knows everything, but a trusted system that brings the right evidence to the right human decision.

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, explains why the real advantage of faster data isn’t speed—it’s shortening the distance between signals, human judgment, and the next best action.

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.

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.

Clinical trials capture data faster than ever, but the real bottleneck is the distance between data capture and a decision teams can trust.