US-based software company Medidata has announced an expanded partnership with contract research organisation Worldwide Clinical Trials to expedite clinical trials and enhance the patient experience.
Medidata’s AI technologies and its wide-ranging historical data set will be deployed across Worldwide’s operations to improve data-driven decision-making.
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This approach is expected to aid Worldwide’s customers in reducing trial timelines and enhancing site selections.
Worldwide Clinical Trials president and CEO Peter Benton said: “For more than ten years, we’ve partnered with Medidata to help our customers gather advanced, data-driven insights so they can make confident and well-informed decisions about the future direction of their clinical trials.
“Incorporating Medidata AI Intelligent Trials into our studies will allow us to more systematically plan, design, and execute research so we can deliver a better experience for patients and sponsors.”
Medidata chief technology officer Tom Doyle said: “Worldwide will accelerate clinical research and advance the life sciences industry by leveraging site-level granularity and real-time actionable observations provided by Medidata AI’s turnkey solution.”
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By GlobalDataThe agreement follows Medidata’s recent renewal of its collaboration with Thermo Fisher Scientific‘s PPD clinical research business in February, which aimed to expedite innovation in clinical trials.
The collaboration with PPD aims to enhance drug development programmes by making use of the comprehensive Medidata Platform, along with Medidata Adjudicate and additional Medidata Rave offerings.
Based in New York, Medidata’s solutions have been used in more than 32,000 trials involving nine million patients to date.
Last November, the company introduced two new data integration offerings, Clinical Data Studio and Health Record Connect, which are designed to optimise clinical data workflows by offering an open, interconnected and scalable data design.
The software solutions aim to provide a more comprehensive view of patients by incorporating data from various third-party sources such as labs, electronic health records and sensor data.
