Looking to the future, RBQM can be applied across studies, programs, and organizations alongside machine learning and artificial intelligence to identify program, organization, or industry trends. Learning from historical data can help make future decisions to support study setup, risk assessment, analytical tools, and signals management.

The DIA GCP-QA Community previously introduced Who Should Be in the Room? to define which functions should contribute to RBQM activities during protocol development, study conduct, or regulatory submission. The 2024 Content Hub focused on defining CtQ factors to guide study teams on what these functions should discuss “in the room.”

RBQM applies this principle to the entirety of a clinical trial, allowing sponsors to manage data quality continuously. Advanced statistical methods have been highly effective at powering centralized monitoring of data from sites in near real-time. Statistical monitoring works on the principle that data from all sites should, aside from the ...

Since all quality management processes are dynamic, the 2013 EMA Reflection Paper on Risk-Based Quality Management (RBQM) in clinical trials recommends that risks be monitored continuously. It further recommends establishing risk-based approaches at the program level first, followed by determining approaches at the protocol level, with sponsors ...

Now, it is about Risk-Based Quality Management (RBQM), an ICH- and FDA-advocated approach to managing risk for the entire clinical trial lifecycle. Improving data quality and patient safety, while controlling the spiralling costs of drug development research, were the primary objectives behind the shift towards RBM over the last eight years.

The FDA advocates lean principles and risk-based quality management (RBQM) to continually align data pipelines with patient needs. Data quality KPIs should embed across the product life cycle. Technology selection prioritizes scalability and interoperability to future-proof for exponential data growth.

CluePoints Co-Founder and Chief Product & Technology Officer François Torche discusses the challenges of collecting clinical trial data with digital or remote tools (eCOA, ePRO, etc.) without putting the quality of that collected data at risk in the below interview.

The clinical research industry is currently in a transition as it moves to adopt Risk-Based Quality Management (RBQM) and Risk-Based Monitoring (RBM) as its primary quality management methods.