Clinical Trial Analytics | Intelligent Trials | Acorn AI

Clinical Trial Analytics | Intelligent Trials

Enabling more agile and confident decision making to optimize clinical trials

The clinical trial landscape is more complex and challenged than ever. Past success and practices are no longer sufficient predictors of future success. Medidata Acorn AI Intelligent Trials brings AI enabled technology, partnered with advanced analytics, to meet the challenges of this ever changing landscape. Our clinical trial analytics solution brings together cross-industry real-time performance metrics, predictive models, and forecasting capabilities to give you a competitive edge in trial planning and execution.

Medidata’s data scientists went above and beyond the initial expectations of the project…by over-delivering on the project and instilling a sense of trust behind the data and the data science to support an important trial.

Head of Clinical Ops, Mid-Sized Sponsor

Why Intelligent Trials

Use real-time, cross-industry data

Work with real-time data <1 month old on over 6k active trials, with access to cross-industry trials (50% Oncology, 50% non-Oncology). The data set is unique, standardized, and built to complement your company and real-world data. Utilizing this broader set of data allows you to measure the impact of change and adjust your trial as needed.

Gain a competitive edge

Identify the best site locations today for successful patient recruitment to meet and accelerate target patient enrollments, diversify your targets, and assess rates of congestion at sites. Thus giving you the ability to quickly assess the impact of trials on patient burden and safety.

Stay ahead of the disrupted trial landscape

Make more agile and confident decisions to keep your trial on track in the midst of disruption. By using information gathered from the real-time data, you will be well equipped to make decisions to minimize site congestion, relieve patient burden, and reduce protocol amendments.

Explore Intelligent Trials Use Cases