From RBM To RBQM: How Analytics Are Shaping The Future Of Clinical Trial Risk Management

For a while now, the industry has utilized risk-based monitoring (RBM) to better detect and intervene in potential risks early on, safeguarding data quality, patient safety, and reducing costs and study delays. With advancements in clinical trial technology, it’s since embraced risk-based quality management (RBQM), a comprehensive methodology that extends RBM throughout the entire study execution process, prioritizing key factors that truly matter. But how does RBQM work in practice and what differentiates it from RBM?

Unlike traditional monitoring methods that rely on exhaustive Source Data Verification (SDV), RBM leverages data-driven decisions to optimize trial outcomes. Extensive studies have demonstrated that SDV alone is not a guarantee of data quality, making RBM a superior alternative. By embracing centralized monitoring, statistical assessments, and advanced technical capabilities, RBM enables sponsors and CROs to streamline site visits, reducing the burden on Clinical Research Associates (CRAs) who no longer need to check every data point against source records on-site.

Recently, RBQM has emerged from the principles of RBM to a comprehensive level within clinical trials. Using a combination of technology, analytics, and statistics, RBQM enables real-time detection of data quality issues, empowering sponsors and CROs to proactively address potential concerns before they compromise the integrity of study data. This practice is recognized and endorsed by regulatory authorities such as the FDA, EMA, and the International Committee on Harmonization (ICH). Learn more about how RBQM works in practice, what differentiates it from RBM, and where it’s headed by accessing this article.