By Clinical Research News Staff 

August 13, 2020 | Medidata launched Medidata Detect today, a centralized statistical monitoring solution that improves data quality and promotes patient safety in clinical trials for new medicines, vaccines, and medical devices. Medidata Detect is part of the Medidata Rave Clinical Cloud platform, and helps customers manage data quality, monitor site performance, and promote patient safety by uncovering and finding errors, trends and anomalies in data through statistical algorithms and tests.

“Medidata Detect is a new and vital machine learning tool designed to improve data integrity and reduce trial risk,” said Glen de Vries, co-founder and co-CEO of Medidata in a press release. “Detect is another example of our continuous innovation that helps advance clinical trials with increased data accuracy and speed. It’s another way we’re working to get important new vaccines and therapeutics to patients in safer, faster ways.” 

A report in the Journal of the American Medical Society (JAMA) found almost a quarter of new drug submissions required one or more resubmissions before they were approved, with a median delay to approval of 435 days following the first unsuccessful submission. Medidata Detect uses one central system for aggregation and review of any number of data sources, flagging data errors, trends, and anomalies in real time. This translates into better quality data and decisions throughout the trial and shortens each cycle review time by weeks.

According to the company, Medidata Detect identifies known and unknown risks, and triggers corrective actions, which proactively minimizes study delays and submission failures in trials. A foundational step in adopting a RBQM (risk-based quality management) approach to clinical development, Medidata Detect is able to define and manage study risks, expose unanticipated data anomalies and cut down on programmers’ time and effort through proprietary machine learning algorithms that require no customer programming.