RBQM is our future: Using holistic risk-based oversight, we can focus on a trial’s most critical factors

5 min

Let’s talk about the future of clinical trials. There’s no crystal ball, but we have plenty of data — and it shows the growing need for risk-based quality management (RBQM) in clinical research.

Recently, RBQM experts offered ideas of what’s ahead. RBQMLive, hosted by CluePoints, featured 18 industry leaders and welcomed more than 700 attendees from 27 countries. We discussed the current clinical trials landscape, the factors driving change, and why the industry is embracing the RBQM model.


Where are we now?

The Association of Clinical Research Organizations (ACRO) measures RBQM adoption using eight key elements, including initial and ongoing risk assessment, centralized monitoring, remote monitoring, and reduced source data verification and source data review. In ACRO’s 2019 survey of more than 6,500 studies, about one-third of trials included an initial risk assessment. Only 16 percent, however, included initial and ongoing risk assessment and centralized monitoring, and just nine percent implemented all eight RBQM elements.

While RBQM adoption across the industry is incomplete, we expect ACRO’s forthcoming survey will show a substantial increase in implementation. That’s due in part to the pandemic, which necessitated wider use of remote monitoring. But we’re also seeing overall increased interest in holistic risk-based trial oversight.


Why the shift?

First, drug developers and clinical researchers are more committed than ever to patient centricity, which means designing more decentralized clinical trials (DCTs). DCTs remove barriers to patient participation, helping ensure trial participants accurately represent the disease population.

Of course, as DCTs and hybrid trials reduce patient burden, they also introduce additional complexity to the process. And complexity in trials is only increasing. According to Ken Getz of the Tufts Center for the Study of Drug Development, the average total endpoints in a trial tripled between 2005 and 2020. In that same period, we’ve more than doubled total procedures per study and sites per study. Perhaps most notably, total data points per study have increased sevenfold — up to 3.56 million on average.

The growth of DCTs means we’re gathering data from a rapidly expanding range of digital sources. As Parexel’s Nick May shared, 70 percent of data volume in 2019 came from non-EDC sources (including mobile apps and wearables). The rise of targeted therapeutics and customized trials is also contributing to complexification.

Complexity is inevitable. Instead of struggling to reduce it, we need a strategy for managing it. That’s where RBQM comes in. By designing quality into trials, we focus on the most critical data and create a framework for efficiently identifying and addressing any risks to patients, the integrity of data, or regulatory compliance.


How will it happen?

Adopting RBQM will require change within our organizations, platforms, and systems.

First, we need an agile infrastructure that supports a risk-based model. During his keynote presentation on the second day, Parexel’s Sy Pretorius urged organizations to adopt interoperable technology platforms. Current operations models and data platforms can’t facilitate the kind of collaboration and standardization required for successful risk-based oversight. And they fail, he said, to derive maximum meaning from the massive volumes of multi-sourced data we currently collect.

Moving to agnostic, interoperable technology platforms will enable necessary collaboration among suppliers, research partners, and trial sponsors. Interoperable platforms will also allow us to optimize data sharing and data hubs so we can unify data and exploit its full value.

In an open architecture, we can add, remove, and replace technologies depending on the specifications of each study. As hybrid trials become more common, we’ll be able to mix and match solutions to best serve the needs of patients and operations — because those needs will change, trial by trial.

RBQM will also require a shared set of standards for data sets. In his talk, Nick May advocated for greater data stewardship, which will ensure accuracy, completeness, and security. Stewardship also helps simplify and speed analysis and decision-making as we turn data into business insights.

Data stewardship is the basis for knowledge management. To be most effective, we must understand how data is collected, curated, analyzed, and put into action. Through data stewardship and knowledge management, we can document discovered risks that will inform future study design.

Finally, we need to pursue technologies for advanced data comprehension. The old “collect and capture” mindset isn’t benefitting us. New solutions will help us focus on critical data so we can comprehend it, then share it and use it to drive conclusions.

AstraZeneca’s Łukasz Bojarski summed it up this way, using the acronym FAIR: our trial data should be findable, accessible, interoperable, and reusable, meaning we can use existing data to answer new questions.


What comes next?

Regulators continue to encourage adoption of RBQM. In its draft of E8(R1), ICH expands on the concepts of Quality by Design and the identification and review of Critical to Quality (CtQ) factors — principles that are key to RBQM. The draft guidance states that quality factors “should be prioritized to identify those that are critical to the study, at the time of the study design, and study procedures should be proportionate to the risks inherent in the study and the importance of the information collected.” As Bojarski shared, that kind of focus and prioritization is at the heart of RBQM.

And while new technologies and platforms will be important to adoption, RBQM will also require culture change. To successfully implement RBQM, we must embed risk-based thinking in each study team and organization. We’ll also need to focus on integration. During the event’s closing panel discussion, Mary Arnould of Astellas Pharma explained that silos are a primary obstacle to RBQM implementation and that fostering collaboration must be our next priority. Regeneron Pharmaceuticals’ Esther Huffman O’Keefe offered an example: the creation of key risk indicators has to be informed by risk assessments — and those assessments and indicators are only valuable if you can communicate them to sites and meaningfully intervene when necessary.

So are we ready for RBQM? During his Sy Pretorius conducted a live poll. Two-thirds of respondents said their organizations have either partially or fully implemented risk-based oversight. Those results might not be scientific, but they’re certainly encouraging. We’re looking forward to seeing — and helping — those numbers grow.

Return to Insights Center

Related Insights


AI Milestones: FDA’s ISTAND program accepts AI-based assessment tool for depression

Mar 19, 2024


Membership in Clinical Research Data Sharing Alliance enables Parexel to shape the future of clinical trial data

Sep 7, 2022


PBPK modeling solutions as a potential risk mitigation strategy for pH dependent DDIs

Mar 15, 2021


Putting AI to work in your safety program

May 21, 2021


Tencent-Backed Wedoctor Is Going Public: What Are the Next Steps for Chinese Digital Health Companies?

Jun 30, 2021


Evidence Generation Strategy under Germany’s Digital Healthcare: Is More Better?

Aug 6, 2021


Highlights from ISMPP Asia Pacific Meeting 2021

Sep 20, 2021


Capitalizing on sensors in clinical trials

Sep 20, 2021


Actigraphy advances a patient-first approach: Reduce patient burden — and make the most of valuable data

Oct 15, 2021


Clinical pharmacology, modeling and simulation to support FIH study design

Oct 21, 2021


CNS Summit Recap: The Future is Collaborative

Nov 22, 2021


How biotechs can strengthen their value story with advanced analytics

Feb 15, 2022