Using Artificial Intelligence-Driven Risk Prediction and CRA Intervention to Improve Enrollment Outcomes at Clinical Trial Sites

About this session

Sponsored by Thermo Fisher Scientific. Non-enrolling clinical trial sites increase costs, delay timelines, and strain resources, often identified only after issues emerge. This session introduces the Non-Enrolling Site Tool (NEST), a machine learning–based predictive analytics platform that assesses site-level enrollment risk at pre-SIV, activation, and 60 days post-activation, using operational, site, investigator, and real-time trial data to guide targeted CRA interventions.

Speaker

• TM

  Tim Mudric

  Vice President, Clinical Development Strategic Enablement · Thermo Fisher Scientific

  Speaker