AI In Data Management

Growing amounts of collected clinical data augment the importance of reassessing the methods used for data processing and managing. Risk-based monitoring of the collected data is an integral part of any quality management program, it is an obligatory demand of the regulatory agencies and it substantially contributes to reducing the probability of costly failures (Hawwash, Applied Clinical Trials, 2018). However, typically, it is slow and expansive. The industry has been successful in reducing costs with the risk-based monitoring approach, where only sites who carry high risk, or their data has shown to fall outside expected monitoring trends, are targeted for regular monitoring visits. Based on this success, discussions have started around the thought of risk-based data management activities.