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1rem

An increase in protocol complexity is paving the way for machine learning models to optimize trial design.

Clinical Trial Failure: A Data-Driven Perspective on Risk Mitigation

Study uncovers insights on the impact on safety, patient enrollment/retention, and compliance.

Benchmarking Risks Across Therapeutic Areas

The long-held practice is effective in pinpointing the emergence of known and unknown risks to clinical development, study shows.

Central Monitoring is Uncovering Emerging Quality Risks in Clinical Trials

Recent analysis showed 81% of clinical trial sites using RBQM statistical data monitoring experienced an improvement in quality.

Using Data Patterns to Proactively Improve Study Quality

A review of industry methods and adoption trends in those trial segments.

Central Monitoring Use in Early-Phase and Small Enrollment Trials 

In this video interview, Rob Jones, product manager, TMF practice area, Pharmalex, discusses how risk-based approaches must be prioritized amidst the increased demand for quality and compliance. 

Embracing Risk-Based Approaches with Clinical Trial Oversight

, Rob Jones, product manager, TMF practice area, Pharmalex, discussed challenges in the trial master file (TMF) space, highlighting outdated regulations such as ICH E6(R3). When it comes to innovation and compliance in the space, he stressed the importance of a risk-based approach, focusing on critical records and leveraging artificial intelligence for big data analysis. Moving forward, Jones believes the TMF community's collaborative nature can be viewed as a strength in navigating regulatory changes and technological advancements.

ACT: What should leaders in the TMF space be focused on the most in navigating the increased demand for quality and compliance?

There is a quote that anyone who's ever listened to any of my talks or webinars or blogs or anything. I love this quote, and it's in ICH E8(R1), I've got it written down here. “Perfection in every aspect of activity is rarely achievable, or can only be achieved by use of resources that are out of proportion to the benefit obtained.” Sounds like a Taylor Swift lyric, but realistically, what it's saying is, in order to get perfection, you're going to have to put in far more effort than it's worth, so focus where the focus needs to be. Not all records in your TMF are equal. They're not. A protocol amendment changing the fundamental part of your study because of an endpoint is far more important than the routine chit chat that you have and meeting minutes you kicked out. There's more risk at a high enrolling site that's got 1,000 patients, than there are at another site that's got one, so focusing on where the risk is and really embracing this idea of a risk-based approach, I think that's where we need to focus on. That's where we can really get to this new demand for quality and compliance and being able to say the rest is good enough. It does exactly what we need it to do. It's fit for purpose. The other thing with risk, though, we always need to remember is, I've seen people in the past use risk-based as a way to justify doing less. I think the thing we need to remember is, risk goes both ways. There's some times where the risk drops, and you can say, “Brilliant. Don't need to do as much work as I was doing yesterday. All my metrics are green. I've got everything that I need to. I've just had a call with the rest of the study team. Everything's flying through. Brilliant.” There's other times, though, when you have a bad week and the number's aren’t looking great, and at those times, you have to do more work than you thought, but balancing that being available and being able to pivot where needed, that's really what the leaders in this space need to do. I think again, going back to the AI, this idea of there's people who can really have a first mover advantage. Going to be the same here. There's going to be people who really embrace this and really make a difference to their organization, and there's going to be other people who say, “It's too risky. I don’t want to, and I'm going to check everything twice, just to be safe.” That, to me, is where really the leadership is going to be focused, and again, where quality, compliance, cost, everything that comes into it can really be tackled head on.

Videos

In this video interview, Rob Jones, product manager, TMF practice area, Pharmalex, highlights recent ICH guidelines and how risk should be an area of focus as trial designs continue to get more complex.

Understanding the True Meaning of Risk in Clinical Trial Operations

 I mentioned, not only have we got these new regulations, but things are changing at a rate that we've never seen before, we can't keep up with. There was a time back in the 2010s where it felt like there was new things happening all the time with, again, bringing in a real foundational level smartphones and tablets and suddenly, internet everywhere, but we're even past that now, it's moving even faster. I think the thing is, if you look at things like ICH E8(R1) that was released the other year, that was almost like a foundation for ICH E6(R3), and it really talked about quality by design. It really talked about thinking about the holistic and bigger picture. They talk in there that if you're thinking about going to certain countries, don't wait until further on down the line, just incorporate those countries into the trials you're doing. That means that trials are going to become more complicated. Complication brings cost, and fast and complicated bring risk. There's no getting around that. Risk is a big, big thing, but again, ICH E8(R1) really built in the idea of quality by design and risk being a big part of that. ICH E6(R3) also talks a lot about risk. I think the thing that we should be focusing on from a leadership point of view, is really understanding that word, risk-based has been a buzzword for as long as I've been around in the scene. We've talked about risk-based, we've talked about these different approaches, but I think it's time for us to really think what that means. Risk inherently means the potential of something bad happening. It's our job to understand that and do what we can to minimize it. In the past, terms like fit-for-purpose, good enough, they're almost dirty words. They were sins. “Good enough isn't enough.” Actually, it is now, that is enough.

In this video interview, Thierry Escudier, portfolio lead, Pistoia Alliance, talks the current landscape of sustainability in clinical trials and how stakeholders are beginning to pay more attention to carbon footprint amid increasing protocol complexity. 

A New Era for Sustainability in Clinical Research

, Theirry Escudier, Portfolio Lead, Pistoia Alliance, discussed the alliance’s newest initiative to assess the carbon footprint of clinical trials. The initiative aims to answer whether decentralized clinical trials positively or negatively impact carbon footprints. Escudier also discussed how clinical research stakeholders are slowly beginning to adopt more sustainability practices, focusing on reducing waste and costs.

ACT: In terms of sustainability, how have clinical research stakeholders been responding to challenges such as operational costs?

Except a few, let's say, front-edge companies, that have already looked to that domain for a couple of years, I guess most of the clinical leaders have never really considered the sustainability, because I think no one has really assessed in the past, in fact, the impact of the clinical trial implementation. Everyone is focused for decades about ethics, about safety, about regulatory compliance, keeping I would say those trials within the budget, within the timelines, make sure that patients will, I would say, accept to be part of those clinical trials, having good data, good quality, so all those things with an increasing complexity of the trials, this has been proven many times by those studies from Tufts, it's clear that the protocols are becoming more and more complex. There is more and more, I would say, demand on both the investigator side and on both the patient and everyone was focused on that, and it's only in the recent days that there was a new mindset that is appearing to say also, well, clinical trials are not equal to zero in terms of carbon footprint, and because this is an overall action and implementation, can we reduce that impact that now people are also looking and a couple of comments is, is not to oppose, I would say, the sustainability versus the cost, because, if you think also in a different manner, one of the other issues in clinical trials is the waste, waste of packaging, waste of blood samples that are not used and even they are sent to the site, some waste of the drugs even now with all the IRT system, you can really more control, I would say, the number of supplies you are sending, but still, there is a lot of waste and this waste is purely, I would say, carbon footprint, but also it's a waste of money.

In this video interview, Vera Pomerantseva, director of product management, RBQM, eClinical Solutions, talks FDA’s latest protocol deviation guidance and potential changes to the draft.   

Potential Updates to FDA’s Protocol Deviations Guidance

, Vera Pomerantseva, director of product management, RBQM, eClinical Solutions, discussed FDA's recent guidance on protocol deviations and its impact on clinical trial management. She highlighted the importance of quality by design (QbD) and critical to quality (CTQ) factors in reducing protocol deviations. Pomerantseva views this guidance as an official acknowledgment of existing practices and an opportunity for improvement.

ACT: How do you think FDA’s protocol deviations (PDs) guidance will affect the industry moving forward into the future?

I see it as a great first step in acknowledging the complexity of this process and providing more clarity to the industry, and therefore, I hope that all the industry professionals will take advantage of the opportunity to provide comments. I actually reviewed some comments a couple of days ago. There is not too many, so I would envision that we could collaborate much better on helping ourselves to have a more consistent and more concise document.

I would definitely expect more details in the guidance, and then it's going to evolve over time, but again, I'm a now person, let's see what we can do now, not waiting for another revision since we have the chance. For example, I would expect more guidance on a couple things, and I'm going to be very specific or what I haven't seen there, but I would want to. Serious breaches: at what point should important PDs be assessed for being serious breaches, if it should be assessed, and what role should be involved? Maybe it should reference a dedicated serious breaches guidance, but I haven't seen anything in there. Governance, metrics, and escalation: it is a big topic in ICH E6(R3), but I have not seen anything on this topic in the protocol deviation guidance.

In this video interview, Vera Pomerantseva, director of product management, RBQM, eClinical Solutions, discusses the importance of open dialogue during risk assessments. 

Strategies for Embedding Protocol Flexibility 

ACT: What are some strategies that stakeholders can use for embedding protocol flexibility during risk assessments?

The strategy to embed flexibility, I think, should be an essential part of protocol design and risk assessment. This is the one of the key QBD activities, so a cross-functional team must be involved and an environment which fosters open dialogue should be created. We call it quality culture. Again, I know it's easier to say than to create it, and by the way, in 2022, MHRA issued a guidance clarifying lots of questions about the protocol risk assessment and monitoring oversight and I think they did an excellent job outlining the roles to be involved in the protocol risk assessment and basically how it should be done. The protocol should be reviewed by this cross-functional team to assess it from the different angles looking at the complexity, the burdens, unjustified restrictions, and maybe as an example, here are some questions which can be discussed by this team during their meetings, so what requirements can be less restrictive? Is everything that is in the protocol scientifically justified. Do we really need all these assessments in this protocol, and if we need them, do we need them in this particular format and with that particular frequency? How flexible should your visit windows be? Again, if you decided it's going to be plus/minus days without the need, you just created a problem for yourself, you're going to have a lot of protocol deviations. Do they really impact anything? Maybe not. It's, again, quality by design thinking.

Strategies for Embedding Protocol Flexibility

In this video interview, Dominique Demolle, CEO of Cognivia, discusses how understanding the patient journey from the very beginning of a study can help mitigate risk and improve patient centricity. 

Best Practices for Reducing Risk Upfront in a Clinical Trial

, Dominique Demolle, CEO of Cognivia, discussed challenges in improving patient adherence in clinical trials. She highlighted the reliability of adherence tracking methods, such as pill counts and ingestible sensors, and the need for tailored patient engagement strategies. Some potential solutions to these challenges include the use of artificial intelligence and designing more patient-centric studies.

ACT: What are some best practices sponsors can use to make their trials more patient-centric and mitigate risk even further upfront?

I think that most of them are doing what I'm going to say, and hopefully I would say and repeat, proactive thinking in terms of patient engagement. There has been in the US and Europe a great initiative like the CTTI in the US, or PARADIGM in Europe about providing some guidance about patient engagement. Clearly, from the very beginning of the protocol development, but up to and including the survey at the end about the patient experience is something that is definitely going to help, and as I mentioned, even more now that we are going to embrace an even larger diversity of patients, so for sure, patient engagement is a must. The second thing is, yes, but. Not everything fits all, so the more we are going to be able to tailor the support that we are going to provide to a patient, the more successful we will be. We have mentioned AI and machine learning that could be a complementary strategy to tactics and solutions that are already put in place, but really to ensure a higher probability for those patients to remain engaged and understand why they take the drug and understand why it is important and why it could be beneficial for them. Proactive thinking, integrated strategy, and taking the best of each solution to really map and try to tackle the problem.

Future of RBQM

ACT: What key takeaways do you hope to share with industry leaders at SCOPE 2025, and how do you see Cluepoints shaping the future of RBQM?

I think RBQM is becoming a much bigger opportunity. It's really about treating data as an entire ecosystem. It's not just individual data points now, it's the totality. It's about looking at patients, looking across patients, looking across studies, and data is the common element of that. For us, as we s we talk to different partners, customers, sponsors, it's really coming down to four pillars. Are you cost motivated? Are you time motivated? Are you resource motivated, or are you quality motivated? And everyone wants to say the politically correct answer, well, it's all four, but it can't be all four. You have to pick one. I’m not saying you sacrifice the others. What I'm saying is you're going to pick your priorities, and what we’re here to talk about is the fact that we are fueling all four.

 At SCOPE this year, the key objective is to advance the conversation around risk-based quality management (RBQM), transitioning it from a theoretical concept to an actionable reality. The conference provides a collaborative platform to discuss the evolution of RBQM, particularly in light of emerging technologies like AI, which were not even part of the industry's vocabulary a decade ago. The goal is to validate and expand discussions, ensuring that RBQM is implemented effectively across clinical trials.

A significant regulatory shift impacting RBQM is the imminent release of the updated ICH GCP E6(R3) guidelines. These changes are expected to transform how sponsors approach RBQM, potentially even redefining the concept of “risk” itself. Rather than being viewed as a negative factor, risk management could shift toward a more data-driven, informed decision-making process. These guidelines will influence aspects such as chain of custody and the integration of modern methodologies into trial management.

The future of RBQM is increasingly centered around data as a comprehensive ecosystem rather than isolated data points. Industry stakeholders are prioritizing different drivers—cost, time, resources, or quality—in their approach to RBQM. While all four factors are important, organizations must determine their primary focus to optimize decision-making. The industry is working toward a model where these priorities can be balanced more effectively, ensuring that clinical trials remain patient-centric while also meeting regulatory and operational goals.

Ultimately, SCOPE serves as a critical venue for shaping the future of RBQM, fostering discussions that drive innovation and efficiency in clinical research.

In an interview with Applied Clinical Trials Associate Editor Don Tracy, Richard Young, chief strategy officer, Cluepoints, offers his key takeaways from this year's SCOPE Summit in Orlando. 

Latest Conference Coverage

SCOPE Summit 2025: Richard Young Discusses Key Takeaways and the Future of RBQM

CluePoints Risk Detection

ACT: What are CluePoints’ key objectives for participating in SCOPE 2025, and how do you see the conference shaping the conversation around risk-based quality management?

I think the very first thing is we're delighted to be here, because there's a need to validate the conversation. 12 years ago, a word that didn’t exist in our vocabulary—nor did AI, to be honest. Now it’s front and center. We’re here really to continue that dialogue, to share and expand, but also transition from the potential to the reality. This is what we're doing today, this is what we’re going to do tomorrow. I think that’s the exciting thing about meeting like this, it’s not a false environment, it's a genuine collaborative environment with so many different conversations, and it's nice to be at the center of it.

ACT: How has CluePoints’ approach to risk detection and mitigation evolved in response to the increasing complexity of global clinical trials?

We’re about to live through the ICH GCP E6(R3) release. In fact, we’re hosting a meeting with the FDA in just about five weeks’ time to go over it. It's going to completely change our approach. I actually think one of the biggest outcomes will be to challenge the use of that word risk. It's a word that confuses, and I wonder whether we might move into more of an informed or a data-driven era where risk isn't a negative word, it's a really positive phrase. But those guidelines are going to change everything from chain of custody to the approach we take, to just embedding a transformational, modern methodology into every decision we make.

At SCOPE this year, the key objective is to advance the conversation around risk-based quality management (RBQM), transitioning it from a theoretical concept to an actionable reality. The conference provides a collaborative platform to discuss the evolution of RBQM, particularly in light of emerging technologies like AI, which were not even part of the industry's vocabulary a decade ago. The goal is to validate and expand discussions, ensuring that RBQM is implemented effectively across clinical trials.

In an interview with Applied Clinical Trials Associate Editor Don Tracy, Richard Young, chief strategy officer, CluePoints, discusses goals of the SCOPE Summit and CluePoints' evolving approach to risk detection. 

SCOPE Summit 2025: Richard Young on CluePoints' Approach to Risk Detection in Clinical Trials

Pat Hughes, co-founder and chief commercial officer of CluePoints touches on collaboration with FDA and using artificial intelligence in clinical research.

DIA 2024: Pat Hughes of CluePoints Highlights Renewed Agreement With FDA, Utilizing AI in RBQM

ACT: What are you looking forward to working on and achieving with FDA under the renewed CRADA agreement?

We are so delighted to renew the agreement with the FDA; our cooperative research and development agreement in its original form spanned back to 2016 and at the time, we were looking at how the statistical algorithms within the CluePoints solution could be used by the FDA within their submission process, such as selecting sites for inspection based on interrogating the data through CluePoints. And it was a really great opportunity to work with statisticians within FDA, and actually we've improved some of the ways that we do things in collaboration with their statisticians. That was the whole point of the research and development agreement, that we would work together on improving things for the FDA, but subsequently, how we could improve things within CluePoints as well.

For example, we came up with a new scoring algorithm that the FDA wanted to implement that would help them in their process, and consequently, we were able to use that within our own processes, too, if we wanted to. So, it's been a great relationship. We meet regularly with FDA. They are a big advocate of what we are doing, and as much as they're a big advocate of risk-based approaches, and that is the kind of work that CluePoints does.

With the new agreement, we're now focusing on how AI and machine learning can also assist in these types of processes. So, there's no set agenda for how that cooperation will work. FDA will still be able to leverage all the things that we currently do within the CluePoints platform, but together we'll then explore how the use of AI and machine learning is going to supercharge some of those processes. So, the thing we're looking forward to most is the collaboration moves on; we're able to examine things that are groundbreaking for the industry together, and inevitably get the buy in from the agency, with respect to all the kinds of risk-based approaches that are being advocated in the industry right now. They're a great partner. They're wonderful to work with. It's a collaboration that makes sense for us and makes sense for them, so we're really looking forward to it.

ACT: How has ICH E6 (R3) changed the risk-monitoring landscape? What can industry look forward to?

It's never one thing that changes the industry, right? And the risk-based approaches have been advocated as part of guidance documents since 2011. FDA, in fact, were the first of the regulators to come out with a risk-based approach that was part of how they were recommending biopharma sponsors go about managing studies. So, it's not new, but with every iteration of this sort of guidance and ICH E6 (R3) is no different. There's a continued emphasis on what are the expectations of companies within the industry. And it's not often that a regulation is designed to improve efficiencies. Nobody looks at regulations and thinks, “Oh we can do things better, faster, cheaper, because we've got a new regulation.” Usually there's some hurdle to overcome, and it feels like more of a burden in a lot of cases. But in this case, very clearly, FDA and other regulators have realized that there is a much more efficient way of running clinical trials. You only have to look at other industry sectors like manufacturing industries. You don't have a production of widgets, and check every single widget that comes off the production line. You do random samples, you look at batches; you take a risk based approach. And certainly in our industry, we've been trying to look at every variable across the way, the data points that are being collected, and that's just not viable; if we're trying to do things more efficiently, that's just not viable anymore. So the regulations in R3 double down on that recommendation, that you take a risk-based approach and you concentrate on what matters most. So those are the kinds of things that we're looking forward to with the new regulations.

Aside from the regulations, there’s just so much more momentum in the industry. It takes a long time for these oil tankers to turn around, and we've worked in the industry for decades where things have been the same. It's very difficult to turn that large vehicle around when you're trying to become more nimble and agile. We've reached the point where pharmaceutical companies know that they need to do something different. This paradigm shift is now in motion, and there's definite momentum there, so the regulations help, but I think the industry in general recognizes that they need to do something different. Whenever we've spoken to pharmaceuticals companies since we first launched in 2012, everybody has always said, “This is fantastic, we need to do this.” But the next thing that comes out of their mouth is: “But how do we do it?” Because they know that they need to change to harness the power of these kinds of technologies, and it's that that the industry has a difficulty with, because change is momentous, because of the number of people involved, the silos that have existed in the past, and hopefully we're breaking down a lot of those now, but it's just a big change, and we're starting to see the industry moving completely towards this risk-based approach and not just dabbling in it, which is what I feel like the industry's been doing for, certainly the first 5 or 6 years, since the regulations were first announced. So, to answer your question, R3 is just a real reinforcement of the things that people are doing anyway, but it's helped, because now there's an expectation that there's a regulatory requirement to do them too.

ACT: Where do you see the greatest potential for the use of AI in RBQM?

Not just in RBQM, but throughout any of the processes that exist. Certainly, the CluePoints approach is to look at use cases where it's clear that the manual intervention that's going on today can be vastly improved using these types of principles. So, what we're looking to do is to replace most of the heavy lifting that goes on in trying to find the needle in a haystack of what exists by a supercharged process to allow the machine learning, the deep learning algorithms, as we would call them, to do that for you; you need to concentrate on what matters most. If there's these types of approaches that can do the heavy lifting for you, then what we're doing is allowing you then to use your critical thinking to deal with whatever comes up. So in the case of RBQM, we've used a very advanced statistical approach, and we will continue to do that because it's proven and it's credible and it's working, but we're laying on AI and machine learning to help improve how the critical thinking can be applied. For example, when we run an analysis on a data set, we produce signals, these are potential issues. We're not saying they are definite issues, but we are saying you should definitely look at these things because they're outliers, they're discrepancies, they're anomalies that you should be dealing with, even if it's something that you can say, yes, no; when you recognize it's an anomaly, but we can explain it, and here's the explanation. When you produce those signals, there might be in some cases, and based on experience, this happens quite a lot. There might be signals that are indicative of the same problem, but they're all individual signals. What the machine learning algorithms will do is group those single signals into one signal that is the potential issue, because they were all reflective of the same potential problem. And so that's one element where that's a real time saver, because you don't need to click into every signal and say, “Well, that was the same as the last one. All right, same again.” So, there's a nice collecting and summarizing of where those potential issues might be.

And then the other thing that the deep learning, machine learning algorithms are starting to do is then potentially explain to you what might be going on and what we have seen before is the potential resolution. We're not a big fan of taking the human out of the loop. We always want there to be some expert using their critical thinking to decide what happens next, but there's no doubt that these types of algorithms—machine learning—could start to advise what might be the potential solution to the problem, because we've got a vast body of data now that we can draw from in terms of the training that would point them in the right direction. So those are a couple of examples of what's available today, but we're also looking at the things like intelligent query detection in EDC, which is an associated swim lane, in as much as it's still a risk-based approach, not quite RBQM, as you and I might know it today, but it's looking at that approach to say there's a lot of manual queries that fall out of the EDC system that need to be dealt with. We can run the algorithms on the manual queries and start to deal with those by taking those actions that are recommended, or saying, “Well, actually, we know that a lot of the manual queries don't result in any action. Perhaps the machine learning can just eradicate those at source, so we don't even get to the point where we review them.”

There's a number of intelligent aspects of machine learning that can be done in all areas of the clinical trial process, and we're trying to select the ones that just reduce the heavy lifting and roadmap items that might be taking a look at the protocol, so running the protocol through large language models, machine learning, and determining what risk might lie in the protocol and almost coming up with an automated risk assessment just by reviewing the protocol with algorithms. So, there's all sorts of exciting use cases, but what we're not fans of is just hypothesizing about what this might look like in the future. We're actually taking those particular use cases where we know there's a manually intensive, labor-intensive process right now, and taking away the labor intensive part of it and leaving you with the elements that you really want to focus on. And that's where we see the future of AI/machine learning for RBQM and other kinds of data interrogation aspects of clinical trials.

In an interview with ACT editor Andy Studna at DIA 2024, Hughes, co-founder and chief commercial officer of CluePoints touches on collaboration with FDA and using artificial intelligence in clinical research.

Ken Getz of Tufts CSDD and Steve Young of CluePoints forecast the future of RBQM adoption.

The Future of RBQM Adoption

ACT: What do you think the RBQM space will look like in five years? Will we see any drastic changes in adoption either way?

 As an academic researcher, we fully expect to see adoption continue to grow, we're starting to actually track more of a maturity model where you can actually see where companies are along the adoption process. And we expect a much larger number of companies to embrace and use in a standardized way, the RBQM (risk-based quality management) components in their trials moving forward. So in five years’ time, it would be great to be looking at, nearly all clinical trials, or at least a very, very high percentage that are applying the RBQM components.

 Yeah. I couldn't agree more. As I said earlier in the conversation, I think we have hit an inflection point, I think the momentum is there. And those who are maybe a little bit behind, laggards, we might call them, are going to be coming on board I think over the next few years, for sure. It's inevitable. This is now, to me, it's becoming more widely recognized that this is the new way of doing clinical research. This is not some experiment that the industry is doing. Right? This is this is this is the way we're moving forward. Just an interesting note, talking about the survey results and the 57% adoption as the overall metric we came up with, I expect that when we reach “full” adoption, it still wouldn't reflect as 100%. And the reason I say that is because there are some components that we measure of RBQM that actually might not be applicable to certain studies, like for instance, certain aspects of statistical data monitoring on small sized studies, or very quick duration studies or things like that. So the bar is probably not 100%, it's probably something like 90 to 95%.

 Yeah, it's a great point. And probably our methodology will need to change, Andy, as we actually start measuring additional components that are introduced. This survey, for example, asked if companies were starting to use generative AI (artificial intelligence), or any kind of AI and machine learning enabled approach, and it was very low percentages recorded. But that's an area where there's a lot of expected growth. Another area that was really low that surprised us was the use of patient input into protocol design. And that was also an area where a lot of companies projected much higher usage over the next three to five years. So I think you're going to see some components where you'll see really high and rising levels of adoption and others that may become less critical over time.

 Yeah, I completely agree. And one thing that's moving very rapidly, I think many of us are aware of increasingly is AI and machine learning, and its impact on processes across the board, in our space and in clinical development. And while that's a technology advancement, it's not specifically about risk-based quality management, there's definitely implications because what that technology is starting to show more and more dramatically, is the ability to automate traditional processes a lot more. So all in all, I mean, we're starting to rethink the whole paradigm in terms of the way we're looking and in a sense, you can call that part of a risk-based, more targeted, more efficient approach to clinical research. So I don't see them as completely separate topics.

 for giving us an opportunity to continue to talk about this important area of research and this obviously important practice that a growing number of companies are embracing.

 Likewise, thank you very much for the opportunity, Andrew. It's always exciting to talk about this topic and of course, I want to thank Ken and Tufts for including us in that important study. It was really fun to collaborate on that. 

In the fifth and final part of this video interview series, Ken Getz, executive director and research professor, Tufts Center for the Study of Drug Development, Tufts University School of Medicine and Steve Young, chief science officer, CluePoints look ahead to the future of risk-based quality management and what adoption will look like.

Risk-Based Monitoring

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