Potential Pitfalls Without RBQM Adoption

Young: I'll start. Certainly, I guess an obvious one is regulatory risk, right? It's kind of funny because RBM (risk-based management) and all the components of it, including reduced SDV (source data verification), and a more targeted approach. Organizations resisted that 10-15 years ago, because they felt that there would be a regulatory [challenges]. Now it's the opposite. The regulators have not only endorsed it, it is now part of GCP (Good Clinical Practice). It's a strong expectation now. So if you're not doing it, I mean, realistically, honestly, it's becoming an increasing risk, if you're not implementing it from that perspective. To us, I think, more importantly, is that you're missing a significant opportunity to enable and drive higher quality clinical research, right, more reliable outcomes of your clinical research, and doing it more efficiently. It's like an amazing win-win proposition here that is being encouraged. You know, a lot of times you think, “Oh, well, I'm going to give up on efficiency, if I strive for higher quality.” Actually, in this case, it's a methodology that’s saying you're going to achieve higher quality, specifically, because you're being more efficient, you're being more targeted in your approach. And of course, companies should also ultimately save timelines and clinical development timelines should be more efficient as well. So really, those are the negative implications. If you don't do it, you're missing a huge opportunity.

Getz: I couldn't agree more. When Steve and I have given presentations on this topic, we often provide context, by just looking at the volume of data that we're collecting on a single Phase III protocol today. Over the last 10 years, it's grown by 400%. It's truly remarkable. We're looking at on average, and I think we've published some of this in Applied Clinical Trials, 3.5 million data points per Phase III protocol, it's closer to 4 million if we're looking just in oncology alone. We just don't have the physical capacity, the labor that is required. Using old approaches, arcane approaches, to verify every data point, for example, we have to use much smarter approaches that drive efficiency that will also lower costs as Steve was mentioning, and RBQM (risk-based quality management) really represents that win-win where we know the quality will be better and we're also addressing an unsustainable business model, if we don't leverage the prioritization of data review.