Upstream Recruitment and Downstream CLM: The Constraints Holding Back Trial Speed

In a recent interview with Applied Clinical Trials, Tom Cowen, head of healthcare and life sciences at Conga, highlighted the significant bottlenecks that slow down clinical trials, emphasizing that nearly half of all study delays stem from contracting and budgeting processes. 

While his focus is on how Conga's AI-driven Contract Lifecycle Management (CLM) platform streamlines these downstream administrative tasks, his broader points about the urgent need for efficiency, automation, and centralized data resonate deeply with the core mission of Trially.ai. The operational friction that Conga tackles in legal and finance departments is mirrored upstream in patient recruitment, an even more critical driver of delays. According to Deloitte, a staggering 80% of studies are delayed due to insufficient patient recruitment, and an equal percentage of research sites fail to meet their enrollment goals. Trially's platform directly addresses these foundational challenges, providing a complement to the efficiencies offered by platforms like Conga by solving recruitment bottlenecks long before contracts become an issue.

The complexity that drives companies to adopt platforms like Conga for managing dozens of clinical trial agreements (CTAs) is also present at the site level, where manual patient identification creates crippling delays. Research sites often dedicate over 250 hours per month to manually reviewing Electronic Health Records (EHRs)—a process that is time-intensive and error-prone. Just as Conga uses AI to centralize historical contracts to enable faster, more informed negotiations, Trially confronts this challenge with its Trially Match product. Trially Match uses a proprietary AI engine to instantly analyze data from any source—including EHRs, CTMS, and PDFs—to identify and rank eligible patients with approximately 95% accuracy. This automation has been shown to reduce manual chart review time by up to 91% and cut screen failure rates by 73%, ensuring clinical teams focus on the highest-quality candidates from day one.

Also, just as Conga helps biotech firms efficiently scale operations and manage partnerships with CROs, Trially's AI agent, Margo, enables research sites to scale their enrollment efforts without adding headcount. Identifying a patient is only the first step; converting that lead into an enrolled participant is where many trials falter due to over-burdened staff. Margo acts as a "Super-Coordinator" to automate patient engagement, initiates timely, multi-channel outreach, conducts intelligent pre-screening using IRB-approved scripts, and seamlessly manages appointment scheduling with automated reminders. This level of automation directly alleviates site staff burden, empowering a single coordinator to achieve the enrollment velocity of a team of twenty. This operational lift mirrors the impact Conga has on legal teams, where investigator onboarding times have been cut by 50% through AI-driven CLM tools.

Tom Cowen's discussion correctly identifies the industry's need for proven, scalable solutions like Conga to streamline complex processes and accelerate the delivery of new therapies. While Conga optimizes the critical path of contracting and budgeting, the principles of automation and data-driven decision-making are universal. Trially’s platform, with Trially Match and the AI agent Margo, exemplifies how these same principles can be applied to solve the foundational challenges of patient recruitment and enrollment. By transforming slow, manual efforts into a fast, precise, and automated operation, Trially addresses the root causes of the trial delays that affect the entire industry, making it an essential component in creating a more efficient clinical trial ecosystem.