Quantifying the Financial Value of RBQM: Q&A with Sylviane de Viron, CluePoints, and Abigail Dirks, Tufts CSDD

For decades, the clinical research industry has operated on the intuition that risk-based quality management delivers meaningful financial returns—without the empirical evidence to prove it. A new collaborative study between CluePoints and Tufts CSDD is changing that.

To explore this further, Applied Clinical Trials spoke with Sylviane de Viron, data and knowledge manager at CluePoints, and Abigail Dirks, MS, senior data scientist at Tufts CSDD, about what made this study possible, why time savings dominates the financial model, and what the findings mean for sponsors that have been piloting RBQM for years without achieving scale.

ACT: What has made it so difficult to quantify the financial value of RBQM until now, and what did this study do differently to address that gap?

Dirks: Quantifying the financial value of RBQM is still challenging—as it is with any novel or innovative practice. It's really challenging at first because there's just little data on actual use when you're early in adoption. But then you have senior management eager for that ROI assessment, for good reason. Once there's actual use data, you can finally model that financial value, which is exactly what was done here.

The key difference is that we had the combination of CluePoints data characterizing the actual deployment of RBQM according to their experience, along with Tufts CSDD benchmarking data and our experience with expected net present value modeling and financial modeling across the drug development space. Since we had this data, we were able to quantify enough of the underlying drivers of value—time savings and cost savings—to actually inform the assumptions that model RBQM deployment. One of the difficulties of measuring financial value of anything in drug development is that trial execution can vary so broadly. For that reason, we focused on one therapeutic area: oncology. We chose it because it's typically costly to develop in and has lower relative commercial potential, so we considered it a more conservative case.

de Viron: Quantifying the financial value is one part. It's also difficult to quantify the quality impact, and that's something we tried to tackle earlier with a paper showing how much data quality improved with key risk indicators, data quality assessment, and statistical data monitoring. That's very complementary, because having RBQM is good, but it's important to show the value to sponsors so that they start adopting it. As with any new way of working, change management is always difficult. If you can show them that there is a real influence on data quality, on flagging issues earlier, and that it will have a direct impact on cost and time savings—getting the product to market sooner—then you have a very compelling story to start using it broadly.

ACT: The study found time savings to be the largest driver of financial value. What does that tell us about how sponsors should be framing the case for RBQM investment internally?

Dirks: Time savings was the largest driver, and I think Sylviane will be able to expand on where that savings comes from, given CluePoints' direct experience. But something I can touch on is that our culture in drug development is so focused on time savings across functions. While the financial value—the expected net present value metric—is the broader picture, understanding the value of the investment is a really important adoption driver, especially for leadership. A focus on time savings can have a real impact on internal alignment.

In terms of the actual model, when you run a financial model on any drug development program, time savings is almost always the largest driver. Timelines are so long, and there's high commercial potential for drugs and biologics. The eNPV metric takes into account the time value of money—how that value decreases over time—so even a small time savings can have a very large impact. In a more practical sense, this really just emphasizes that leadership needs to be patient and look beyond short-term cost savings to justify major innovations like RBQM. The bigger picture—how implementing a solution affects your commercial potential and development timeline as one large picture—is much more meaningful.

de Viron: Practically, what we expect with RBQM is that as we find issues earlier, act on them, and have everything centralized, that should have an impact on database lock as well. As soon as a study reaches its end, there should not be that many changes remaining in the data. That was an intuition we had—that using RBQM should help achieve faster drug approval—and it's very nice that we were able to show that time savings was a critical driver here, confirming that hypothesis.

ACT: The study modeled direct financial returns but excluded broader benefits like reduced rework, improved data integrity, and inspection readiness. How significant could those additional factors be?

de Viron: Very significant—and it goes even further than that. If you flag an issue very early in a trial, versus traditional monitoring where you might find it only at the end or even after the trial ends, in some cases you will need to exclude data, patients, or even entire centers. Sometimes those are big centers, which means you paid for all of that data collection and then can't use it. And it may also have an impact on your study results—you may not have enough endpoint data to prove your drug is safe and effective.

For that kind of case, it's very difficult to assess cost. The same is true for reduced rework and improved data quality—there are so many contributing factors, and the impact will vary from one program to another. We don't know what we missed, and we don't know what the impact would have been. If we flag an issue in a trial today, we don't know when traditional monitoring would have flagged it, or how much damage would have accumulated in the interim. That's the core of it, for me.

Dirks: That's such a great point. There are so many ways this could play out, and you don't know what would have happened if an issue hadn't been caught early. That's also exactly why these elements weren't included directly in the model—they're just too difficult to quantify reliably. But I will note that the eNPV metric is risk-adjusted, including for regulatory risk. Inspection readiness and improved data integrity are all things that would improve regulatory success, and if we were able to quantify their impact, there would be a much larger eNPV delta. We took a conservative case in this study and made no assumptions based on improved regulatory success—but if that were included, the value would be much greater.

de Viron: And the more you use RBQM, the more benefit you should see. At the very beginning, it's slightly more limited because you're learning at the same time. But the more you apply it, the more cost and time savings you should expect.

Dirks: If we could have an unlimited amount of data to understand how these ROI metrics and eNPV metrics change as a company gets more comfortable with RBQM and deploys it over a longer period of time—that would be so interesting. Because of course it's different when you're first adopting a solution versus when you've been implementing RBQM for years and are seeing those benefits compound.

ACT: What does the evidence from this study mean for sponsors that are currently struggling to justify RBQM adoption and scale it across their portfolios?

Dirks: Many organizations have been piloting RBQM for decades but have been unable to gain traction and scale that adoption. We've held multiple roundtables on this topic over the years, and two themes come up consistently: people want to know what the real impact is, and they want regulatory encouragement and guidance supporting adoption. Now that we have ICH E6(R3) providing that regulatory encouragement, and now that we have empirical evidence of the net financial impact from this study, sponsors still struggling to make the case have the support they need to be more persuasive at building the multi-functional buy-in that's essential for successful RBQM deployment.

de Viron: For me, it's all about pieces of the puzzle—quantifying the quality improvement is one piece, quantifying the time and cost savings is another, and having true value stories and real cases is a third. For example, we were able to show how RBQM, applied retroactively to an old study where we knew there had been fraud, would have flagged the issue much earlier. Those kinds of concrete cases showing how much RBQM makes a difference are the key elements we should continue sharing with sponsors so they understand the value with real numbers and real situations.

And it's important to note: if you use RBQM but don't change your traditional monitoring or ways of working, you will not gain enough value. If you have 100% SDV plus RBQM, the cost will be higher by definition because you've added something on top. RBQM is really key when it's associated with targeted SDV—focusing on what matters most and where there is risk, rather than looking at everything at the same time. If you implement RBQM correctly, you will see value across different areas of your program.