6 Ways Lab Data Can Improve Clinical Trials

Patients don’t experience primary endpoints, so trials that stop at biomarkers and rigid rating scales risk declaring success while leaving daily burden untouched, and digital tools only deliver meaningful evidence when they are co-designed with patients and deployed with restraint.

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.

In Episode 3 of The Human Side of Clinical Trials, hosts Brian S. McGowan, PhD, FACEHP, chief learning officer and co-founder of ArcheMedX, Inc., and Kelly Ritch, chief operating officer of ArcheMedX, Inc., speak with Jason Riis, PhD, CEO and chief behavioral scientist at Behavioralize, about why a well-written protocol doesn’t always translate into consistent execution, and how designing for the way people actually think and learn can reduce site burden and the need for costly unlearning.

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, 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.

Syneos Health and Optum outline a joint model for trial planning, recruitment and site access, with no financial terms or timelines disclosed.