Predictive and advanced analytics are the first such model-based capabilities fully integrated into an RBM solution on the market today, according to the company. These new capabilities improve precision in risk identification by better understanding underlying “white noise” from safety trigger processes.
“The ultimate goal of risk-based monitoring is to increase the efficiency of drug development while improving quality and patient safety,” said Margaret Keegan, senior vice president, data sciences and strategy, Quintiles. “The ability to cut through all the noise and predict potential risks before they occur means we can target the right action at the right place at the right time. It is a major step forward in improving quality and productivity in the next generation of RBM execution.”
Quintiles is currently engaged in more than 130 studies using RBM processes and policies, across more than 26,000 sites and 260,000 patients. Compliant with U.S. Food and Drug Association, European Medicines Agency and TransCelerate guidance, Predictive and Advanced Analytics use unique statistical algorithms specifically designed for optimal RBM execution. The capabilities combine inputs across multiple variables including operational performance and study data to provide unprecedented insights into potential study risks.
“Being able to target patients and sites with this level of accuracy takes RBM to a new level in terms of both speed and quality,” said Teresa Lamantia, senior vice president, knowledge management, Quintiles. “The ability to focus resources where they’re needed to protect patient safety and the integrity of the trial is a welcome advance for all of us working to transform clinical development.”