RBQM 101: What Is Risk-based Quality Management?

The adoption of RBQM in clinical trials has surged in recent years. According to a 2023 survey by the Tufts Center for the Study of Drug Development, sponsor and CRO companies are incorporating RBQM components in over half (57%) of their clinical trials. The most frequently cited challenges to RBQM implementation were a lack of awareness and knowledge, a lack of skills, and a lack of technology.

Read on to discover everything you need to know about risk-based quality management (RBQM) in clinical trials, including what it is, why you may be hearing more about it lately, and its purpose and application in clinical trials.

RBQM: A Solution to a Major Challenge in Today’s Clinical Trials

Clinical trials have undergone significant changes in past decades, transitioning from straightforward designs with single data sources to complex studies in both design and operation. These studies incorporate massive volumes of data from increasingly diverse sources that are often disconnected, resulting in data becoming fragmented across different locations. This has created new challenges for data management and monitoring teams, since traditional quality oversight processes are not scalable—leading to inefficient resource utilization, duplicate workflows, redundant data entry, prolonged timelines, and increasing clinical trial costs.

RBQM applied to clinical trials is not a niche or siloed functional area. RBQM is a modern, proactive, and comprehensive framework for managing quality throughout a clinical trial. It extends beyond traditional risk-based monitoring (RBM) by integrating risk assessment and mitigation strategies throughout the entire clinical trial lifecycle, from initial protocol design to study closeout. RBQM is truly a cross-functional activity whose objectives are to enhance patient safety, ensure data integrity, improve operational efficiency, and reduce costs by focusing on critical-to-quality (CTQ) factors.

The Evolution of RBQM

The shift to RBQM has been spurred by the need for more efficient and effective trial quality management. Key milestones in this evolution include the release of regulatory guidance widely adopted by regulators worldwide to interpret and enforce RBQM, thereby enhancing trial quality and safeguarding patient safety:

• FDA’s risk-based monitoring guidelines in 2011 (supporting the use of RBQM to enhance trial efficiency and patient safety)
• ICH E6(R2) guidelines in 2016 (supporting the use of RBQM)
• ICH E8(R1) guidelines in 2021 (emphasizing quality by design (QbD) and risk-based strategies)
• ICH E6(R3) guidelines in 2024 (refining the principles for ongoing innovation)

ICHE6(R3) represents a significant advancement in the field as it supports enhanced methods for identifying protocol, good clinical practice (GCP), and regulatory deviations. This aligns well with the current advancement in technology. It also supports the implementation of technology in clinical trials—particularly those that can be adapted to fit participant characteristics and specific trial designs. Additionally, it supports the application of QbD to make sure that quality is proactively integrated into the study from the outset.

Today, the adoption and advancements in RBQM are accelerating quickly. AI is increasingly being leveraged to assist in trial monitoring and data quality through powerful anomaly detection and smart visualizations.

RBQM Core Principals

The core principles of RBQM are based on the following elements:

• Quality by Design (QbD): Embeds quality into the study design from the start
• Proactive Risk Identification: Continuously identifies, eliminates, and limits risks before they impact outcomes
• Continuous Risk Monitoring: Uses real-time data analytics to track and respond to trial risks immediately
• Focus on Critical-to-Quality (CTQ) Factors: Prioritizes patient safety and data integrity to ensure efficient resource allocation
• Adaptive Approach: Constantly refines risk management strategies based on real-world data

Key Components of an Effective RBQM Strategy

A robust RBQM strategy involves several key components, including risk assessment and categorization tools (RACT) that systematically identify and categorize risks at the study, site, and patient levels. Key risk indicators (KRIs) serve as metrics to detect potential risks early, and quality tolerance limits (QTLs) are predefined thresholds that prompt corrective actions when exceeded. Centralized monitoring uses real-time, remote monitoring to detect data anomalies, safety issues, and site performance problems before they escalate.

Transform Your Clinical Trial by Implementing RBQM

Proactive and early planning is important before implementing an RBQM strategy, and consulting with RBQM experts is an excellent first step. Ultimately, implementing RBQM in clinical trials requires a structured approach across three key phases:

• Planning Phase: Conduct comprehensive risk assessments during protocol development to identify potential challenges, establish monitoring strategies based on the complexity of the trial, and identify CTQ factors that should be prioritized. 
• Execution Phase: Leverage real-time analytics for continuous risk detection, dynamically adjust monitoring intensity in response to emerging risks, and conduct targeted site visits to address specific issues.
• Review Phase: Evaluate the effectiveness of risk controls implemented during the trial, analyze data to identify areas for improvement, and incorporate lessons learned to refine risk management strategies and enhance the design and execution of future trials.

Sponsors and CROs can leverage advanced solutions that support RBQM to reap the benefits of optimized trial design, continual data quality review, early risk and issue detection, and improved relationships with sites on a scale not seen historically. Medidata RBQM is an example of a configurable and scalable solution that’s being used to transform studies towards risk-based study execution models.

Benefits and Challenges of RBQM

Adopting an RBQM approach offers several benefits:

• Improved Patient Safety: Early detection and limiting of risks make sure that patient safety is consistently prioritized
• Higher Data Quality: Emphasizing CTQ factors and leveraging real-time analytics helps maintain high data integrity and reduces data errors
• Reduced Trial Costs: Targeted, data-driven monitoring eliminates unnecessary visits, thereby lowering overall trial costs
• Faster Trial Timelines: Efficient risk-based decision-making allows for quicker adjustments and streamlines trial processes, speeding up timelines
• Better Regulatory Compliance: By aligning with international guidelines and regulatory expectations, it facilitates better compliance and smoother regulatory processes

There are also some challenges that should be taken into consideration: 

• Resistance to Change: Sponsors and CROs can be resistant to change from their traditional monitoring methods
• Regulatory Uncertainty: Some companies struggle with interpreting RBQM guidelines
• Technology Barriers: Limited adoption of AI and analytics in some organizations
• Lack of Skilled Personnel: Teams may lack RBQM-specific expertise

Future Trends

Several trends are shaping the future of risk-based assessments in clinical trials:

• AI & ML: Enhancing predictive analytics for risk detection and automating anomaly detection in trial data; AI- and ML-driven solutions will drastically reduce human error and data integrity problems, enabling a shift of resources to more strategic and valuable tasks
• RBQM to RBDM (Risk-based Data Management): Adapting the risk-based strategies from RBQM to clinical data management can help eliminate existing redundancies in current data management practices
• Real-world Data Integration: Enhancing risk assessment with real-world data (RWD), such as electronic health records and wearable devices

Conclusion

RBQM is transforming clinical trials by enhancing patient safety, data quality, and trial efficiency. The adoption of RBQM continues to grow, fueled largely by advancements in AI and machine learning. But while RBQM offers significant benefits, sponsors and CROs must also be aware of potential challenges. Leveraging modern solutions that facilitate your RBQM strategy is key for achieving successful RBQM implementation and maximizing the success of your clinical trial.