Achieving Optimal Adoption of Risk-Based Quality Management

Steve Young, Chief Scientific Officer, CluePoints

For more than a decade, the FDA and European Medicines Agency have encouraged the use of risk-based quality management (RBQM) and its predecessor, risk-based monitoring (RBM), to promote highest-quality outcomes of clinical research. ICH E6 (R3) calls for even greater support of RBQM principles throughout clinical trial planning and execution.

Despite this regulatory support, however, the industry has been slow to adopt RBQM approaches. This may be, in part, because even the term RBQM elevates the risk aversion inherent in our industry. There were some people who had a visceral negative reaction to the name itself without really understanding the benefits it could bring in terms of driving efficiency and quality by prioritizing, preventing, and mitigating those risks most associated with essential safety and efficacy data.

During the pandemic, we saw greatly increased use of RBQM components out of necessity. A survey of Association of Clinical Research Organizations (ACRO) member companies found 77% of ongoing clinical trials at the end of 2020 had implemented at least one RBQM component, up from 47% of ongoing clinical trials in 2019.¹

However, in the wake of the pandemic, as the necessity of these approaches started to wane, some of the more risk-averse companies returned to traditional methods. To reach optimal RBQM adoption, we need to increase the education of its benefits, celebrate use cases, and embrace new technologies that enable a more targeted approach.

Current RBQM industry adoption

RBQM adoption is back on the rise. A comprehensive assessment of RBQM adoption in clinical trials conducted by the Tufts Center for the Study of Drug Development, in collaboration with CluePoints and PwC, found, on average, companies are now implementing RBQM in 57% of clinical trials.² While this does not match the peak recorded by ACRO in 2020, it is still far higher than pre-pandemic levels.

The same analysis also highlighted variations in adoption across trial stage and company size. Adoption of RBQM components in 2023 was highest in the documentation and planning and design stages (60% and 56%, respectively) and lowest in the execution stage (52%).

Larger companies also had higher overall RBQM adoption rates. Organizations conducting more than 100 clinical trials per year had an average 63% adoption rate compared to 59% for those conducting 25 to 100 clinical trials and 48% for those conducting fewer than 25 studies per year. European companies had significantly higher adoption rates than the rest of the world (64% compared to 45%).

While the overall data presents a promising picture, these variations highlight areas where we can do more to help stimulate and drive adoption.

Achieving optimal adoption

Optimal use of RBQM will not necessarily be equal to 100%. There are certain components with which we measure with RBQM that might not be applicable to certain studies. For instance, some aspects of statistical data monitoring may not be pragmatic on small-size or very-quick duration studies. When we talk about optimal adoption, we are likely talking about 90% to 95%.

To reach that optimal adoption level, we need to reduce barriers to adoption—both real and perceived; increase knowledge of cross-functional benefits; and celebrate successes. There are no technical limitations to RBQM adoption; the supporting technology can get better but there is no fundamental infrastructure problem. It is about change management. RBQM adoption requires a cultural shift not just at an organizational level but for the entire industry. Wherever you are, whatever stage of study design, planning, or execution you are in, the mindset needs to change to a risk-based one.

The Tufts survey identified key suggestions about how we can start to change that mindset. Firstly, we need to do a better job of publicizing internally and sharing more broadly the real success stories that have come from RBQM implementations and use cases. In some organizations, the impact is known by a core group, but it is not receiving visibility with senior management and making it into the functions that are more peripheral. Secondly, we heard smaller companies often do not take the time to build a coherent change management strategy. Oftentimes, they implement a set of components that are novel, but they don’t become standardized practice. That really highlights the need for training, support, and infrastructure to ease teams into mainstream usage.

Breaking the value proposition into key areas also allows us to identify the nuances of perceived RBQM benefits and where there are pockets of mixed perception that need targeted education. For example, the expectation and experience of value in areas such as quality improvement have been relatively high. However, if you look at RBQM’s ability to lower cost and drive efficiency, you see more variation depending on the functional area where the respondent sits. Areas such as clinical operations are often more risk-averse to trying these approaches.

Finally, we need to continue to conduct and publish analyses that reveal the value of RBQM approaches. Analysis of source data verification conducted 10 years ago showed it had very little meaningful impact on the overall quality of clinical data.³ That evidence has been used across the industry as a compelling way to convince colleagues that we do not have to hold on to old approaches and can move forward with RBQM. However, we need even more of this type of evidence that exposes RBQM’s cross-functional value.

Adapting methodologies

As additional components are introduced, others see rising levels of adoption and some become less critical. Over time, we will need to change our methodologies for measuring RBQM adoption. For example, the Tufts survey revealed very low use of artificial intelligence (AI) and machine learning to support RBQM. We were also surprised to see very low patient input into protocol design. However, both are projected to significantly increase in usage over the next three to five years.

AI is already moving very rapidly and impacting processes across the clinical development space. It is starting to show more and more dramatically the ability to automate traditional processes. When we dug deeper into the Tufts survey results, we started to see areas where there is still resistance that need to be addressed. One of those is organizations having difficulty changing company culture to move away from labor-intensive approaches to more targeted RBQM approaches. Combining statistical methodologies with new technologies can help to demonstrate the value proposition of RBQM and the ease with which it can be implemented across functional areas. This will become increasingly vital as the amount of clinical trial data continues to increase. RBQM is an approach that thrives on data, and it only stands to become more effective with the use of AI.

Examples of how new technologies are already being used to support RBQM approaches can be found in repetitive, but essential, tasks, such as medical coding and risk detection. In medical coding, a deep learning model removes the need for first-line medical coders and synonym-list maintenance, providing researchers with the correct corresponding dictionary term with more than 90% accuracy in seconds. In risk detection, natural language processing allows study teams to prioritize signal review and ensure effective follow-up and documentation of findings by screening free-text and flagging signals that lack required documentation or have an unreliable root cause selected by the user.

We have reached an inflection point in RBQM adoption and momentum for continued growth is building. Given the regulatory landscape, increasing volume of clinical trial data, and need to leave behind labor-intensive manual processes, adoption is now almost inevitable. However, culture change and education will be crucial to help us reach optimal adoption as swiftly as possible.

Steve Young is Chief Scientific Officer at CluePoints and a member of Applied Clinical Trials’ Editorial Advisory Board

References

1. Stansbury, N.; Barnes, B.; Adams; A. et al. Risk-Based Monitoring in Clinical Trials: Increased Adoption Throughout 2020. 2022. Ther Innov Regul Sci. 56, 415–422. https://doi.org/10.1007/s43441-022-00387-z

2. Dirks, A.; Florez, M.; Torche, F.; et al. Comprehensive Assessment of Risk-Based Quality Management Adoption in Clinical Trials. 2024. Ther Innov Regul Sci. 58, 520–527. https://doi.org/10.1007/s43441-024-00618-5

3. Sheetz, N.; Wilson, B.;Benedict, J. Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials. Ther Innov Regul Sci. 2014. 48 (6), 671-680. https://journals.sagepub.com/doi/pdf/10.1177/2168479014554400