- Cincinnati Children’s Tests AI-Based Clinical Trials Screening Tool
- Verizon Partnership Aims to Enhance Digital Health Infrastructure
“The clinical trial industry continues to demand new tools in big data and predictive analytics to improve trial planning and execution and accelerate clinical trials,” David M Johnston, PhD, senior vice president, and president, clinical research, Thermo Fisher Scientific, said in the press release.
“As a result of the vast site-level data amassed by PPD and Medidata, customers now have even greater visibility into how a trial is performing for any study our clinical research business is helping them run. We already have successfully deployed these machine learning-based insights to give us a competitive site selection advantage in the crowded trial landscape.”
PPD TrueCast provides biopharmaceutical companies real-time site-performance data from a vast global footprint of trial sites
In addition, the platform uses advanced artificial intelligence (AI) models to predict clinical cycle times and enrollment performance.
The application will automatically track performance and predicts enrollment risk during a study’s enrollment phase.
PPD TrueCast also delivers access to real-time site performance metrics to identify high-performing sites when modifications are needed.
“AI and advanced analytics play an increasingly critical role in a rapidly changing clinical trial environment, "Fareed Melhem, senior vice president, Medidata Acorn AI, said in a press release. "We’re delighted to be working side by side with Thermo Fisher’s clinical research business to combine our technology, data, and expertise. Together, we are now offering companies new ways to rethink their clinical trials through access to live, cross-industry site performance data.”
The platform consists of Thermo Fisher’s clinical research business data, which holds data from 40,000 sites, 50,000 investigators, and over 2,000 studies. The platform also includes Medidata’s datasets that contain more than 26,000 clinical trials and nearly 8 million patients across 140 countries.
Due to this large dataset and machine learning-based site recommendation models, the average Phase III study enrollment period was decreased by nearly 1.5 months in trial simulations. Additionally, Thermo Fisher predicts to achieve a 30 percent increase in the accuracy of forecasting trial milestones.
By 