Overcoming Issues Of Non-Enrolling Sites In Clinical Trials

By Dave Hiltbrand and Dave Berry, PPD clinical research business of Thermo Fisher Scientific

Explore the challenges of non-enrolling sites in clinical trials, focusing on the benefits of leveraging artificial intelligence (AI) and machine learning (ML) to proactively address common issues. Non-enrolling sites cause delays, increased costs, and inefficiencies, often due to the reactive nature of traditional solutions. In this text, you will learn how to enable targeted support strategies. A case study is utilized to highlight how predictive analytics identified high-risk sites early, allowing focused interventions that enhanced enrollment rates and streamlined trial operations. This study exemplifies how the adoption of advanced ML-driven strategies is critical to maintaining competitiveness in the rapidly evolving landscape of drug development. Sponsors can optimize resource allocation and enhance site performance by utilizing real-time data and continuous monitoring.