Intelligent Trials AI Guide

Clinical trial sponsors and operations teams face more challenges today than ever before. Patient recruitment and retention remain top concerns — with 80% of studies missing enrollment targets, 44% of sites failing to recruit a single patient, and dropout rates continuing to rise. To compensate, many study teams have doubled the number of sites per trial, driving up onboarding and oversight demands.

At the same time, protocols have become increasingly complex, reflecting the shift toward rare and difficult-to-treat diseases. These studies require specialized site capabilities, compete for limited patient populations, and must balance patient burden with safety, accessibility, and retention needs. Despite decades of innovation, only 10–12% of therapies entering Phase I reach the market, even as R&D spending has surged — up 44% since 2012.

To address these pressures, real, standardized clinical data is essential. Consistent data collection and analysis enable reliable comparisons, predictive modeling, and smarter site selection. By leveraging factors such as historical enrollment, activation timelines, and site performance, operations teams can enhance forecasting and reduce trial risk.

Medidata is advancing this transformation by embedding AI directly into clinical workflows, helping sponsors identify high-value opportunities to accelerate trials, strengthen data integrity, and deliver better patient outcomes.