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Innovation in a risk-averse industry with Adam Walker
171 Plays2 months ago
In this episode of Clinical Data Talks, Sylvain Berthelot welcomes Adam Walker, a seasoned clinical data management and biometrics consultant. Together, they explore the delicate balance between innovation and the inherently risk-averse nature of the clinical research industry.
With extensive experience working for companies of various sizes, Adam has had the opportunity to observe and lead the implementation of innovation. In this episode, he shares his observations and discusses the tension between innovation and established processes, highlighting how legacy workflows can sometimes limit progress. He and Sylvain reflect on the challenge of introducing new technologies into environments where teams are incentivized to avoid risk, and where change can feel disruptive rather than enabling.
Adam makes an important point about the importance of mindset and culture. He emphasizes that innovation is not just about technology itself, but about creating an environment where teams feel confident to challenge the status quo, rethink processes, and adopt new ways of working.
Tune in to explore how the clinical industry can strike the right balance between stability and innovation, why risk aversion can both protect and hinder progress, and what it will take to unlock meaningful transformation in clinical trials.
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Transcript
Introduction of Podcast and Host
00:00:13
Speaker
Welcome to Clinical Data Talks, a podcast brought to you by CRS-Cube. I'm your host, Sylvain Bertelot. Join me and industry experts as we discuss the latest trends impacting the world of clinical data.
Introduction of Guest: Adam Walker
00:00:28
Speaker
Talking about expert, my guest for today's recording has a lot of experience and a very impressive resume. resume He's been in the industry for over 30 years and he's had some very interesting roles along the way.
00:00:45
Speaker
I've picked a few of my favorite ones. A biometrics consultant within AstraZeneca's real world evidence team. a consolidation project for the MHRA Safety Vigilance Programme, and managing the biometrics department for MAC Clinical.
00:01:07
Speaker
He's also a fellow podcaster hosting the brilliant Pharma Prescribed podcast. I'm thrilled to share today's episode with Adam Walker. Hi Adam, how are you doing?
00:01:23
Speaker
I'm great. Thank you, Sylvain. Thank you for that lovely, warm introduction. It's a pleasure to be here on your podcast. Well, and we've already recorded a podcast together on Pharma Prescribed, so it's good to be able to return the invitation.
00:01:40
Speaker
Thank you.
Building a Data Management Team from Scratch
00:01:41
Speaker
So today we're going to talk about data management. And umm I'm quite interested in what you're going to share with us because you have so much experience and you've seen so many different aspects of biometrics, data management. So a lot to live up to really, um starting with you've had the very great experience to to be able to build your own data management team from scratch, which is relatively unusual to to be in that position. So what was it like? Are there any key highlights that you'd like to share with us in that experience?
00:02:29
Speaker
Yeah, so this was in the early 2000s. I was very fortunate to be given the opportunity to join a company called Richmond Pharmacology, who at that time were using only outsourced vendors to do all of their data management, stats, medical writing.
00:02:46
Speaker
And the opportunity arose to effectively bring that capability in-house. And it was an opportunity that was far too good to miss.
00:02:57
Speaker
It was early on in my career. We had two young children at the time, so I was doing an enormous commute from my home in Sussex to London every day. But in that four or five years, i really did learn an awful lot, not just about myself, but really about developing systems and processes from the ground up with a blank canvas. It was the kind of opportunity where At that point, I'd maybe worked in the industry seven, eight years.
00:03:24
Speaker
I thought I knew quite a lot, but it turned out I knew very little because all of my work had been done alongside other people where they'd been holding my hand. And this was the first time where ultimately I was the decision maker and it all came down to my capabilities and my decisions.
00:03:44
Speaker
Nice. And what was your your approach to to build the team and those processes?
Challenges in Data Management Systems
00:03:54
Speaker
Yeah, in essence, it was a blank canvas, as I say. So whilst I'd seen how it could be done in other companies, I wasn't really sure what I was going to be doing or how that would sit alongside the existing clinical activities that were happening. It's a clinical trial, early phase clinical trial unit.
00:04:11
Speaker
and so it was really about adapting processes to the existing systems that they had but then fitting them around what was already there so it was the the approach i took was really just the best practice that i could see that would apply very well to those particular systems but having sat alongside many of those people in the clinical trial unit was very different in principle to the the real headline view that you get when you've been working for a big global multinational CRO where effectively all those processes are very regimented, very restricted and not very flexible.
00:04:53
Speaker
What I needed to do was build in flexibility because there was change happening all the time.
00:05:01
Speaker
It's interesting that you you bring the difference between large companies and like smaller, ah more nimble, I imagine, in ah companies in the industry.
00:05:15
Speaker
So can you tell us a bit more about that that comparison, actually? Because you've you've worked at AstraZeneca as well, which is so big ah Are there any things that you pick from the large ones and the small ones as good things or things maybe that are not necessarily that good?
Innovation in Small vs. Large Companies
00:05:38
Speaker
Yeah, so effectively when you when you're in a small situation, turnover is quick, not just people, processes, turnover is fast. um The scope of the work is extremely broad.
00:05:53
Speaker
And no one size fits all in any of those situations, certainly not in an early phase, small small clinical trial setting. In comparison to those larger CROs where you have very fixed processes, much less autonomy over developing process, you know it may well be that the in the very simple sense that if there is a deviation to SOP or a process,
00:06:22
Speaker
you'll, you'll put that in, into various documents in the trial master file, but really there isn't an awful lot of adaptation that happens in big, in big CROs. You know, you're very fixed around those and those operational risks are taken at a much higher level and pushed down through the, through the organization. So one, on the one hand, it was, there was flexibility and on the other, it was really,
00:06:47
Speaker
In the larger CROs, you kind of do as you're told and everyone follows the same processes because you have to be audit ready at all times, no different to early phase. But the sheer scale of it determined really the process differences and how that works, small versus larger settings.
00:07:09
Speaker
Yeah, that's very interesting. And i mean, I've seen it myself that in a way, the larger the organization, the more resistance to change there is. And it's not necessarily, I wouldn't say that it's necessarily a mindset, but it's probably more the fact that, as you said, those processes are there and it's such a strong structure that deviating from those processes or making them evolve is is very difficult in a way. But at the same time, i see those organizations really taking the lead in innovation.
00:07:50
Speaker
And I don't know if you agree with that, but I don't necessarily see small biotechs ah being the leaders in innovation. It's more the larger CROs or former companies.
00:08:03
Speaker
Do you agree with that? And and how like do you have any ideas of how that's possible, considering that inherent resistance to change?
AI Enhancements in Data Management
00:08:13
Speaker
Yeah, I mean, I would challenge that back, actually, because I think innovation tends to happen in smaller settings, but by very by the very nature of larger CROs and pharma companies, they have to be at the forefront of change. They're kind of hedging their bets against technology capability implementing those changes on an ongoing basis because if they don't they're left behind and also there is an expectation from regulators and from competition that they have to be seen to be doing these things really as quickly and as agile as they possibly can but in my experience the the setting of being in in a smaller early phase environment really
00:09:01
Speaker
in itself allowed me to drive those um innovative changes because there was a direct implication of not doing those things on ah on a daily basis.
00:09:12
Speaker
So for example, if we didn't adapt to electronic data capture tool or a system process to what was happening in the unit that same day, we might lose data.
00:09:22
Speaker
We might not collect those blood draws or the pharmacokinetic data if there weren't a particular field in place or it wasn't long enough. You know, there were occasions when perhaps we really, really fast track that UAT, the user acceptance testing process, and maybe missed a couple of units on on a particular number.
00:09:40
Speaker
And then we wouldn't be in a position to collect all the data that was really pivotal to the decision making in those clinical trials. So the adaptation had to be there and was very much adapted on the fly in the early phase clinical trial setting.
00:09:56
Speaker
That was my experience. Yeah, it's interesting because whether it's a small unit like your experience or a large company, there seems to be an external expectation of innovating. For your experience in the smaller setting, this expectation was, well, we need to be able to run the study or we need to be able to analyze the data. If we don't do that, essentially, we're not going to be able to do it.
00:10:27
Speaker
oh Whereas in the larger setting, it might be more a an industry expectation or a regulatory expectation. do you agree with that?
00:10:39
Speaker
Yeah, absolutely. I would say, you know, in a small CRO, you own the work. In a large CRO, you own the process. And the process is not yours to define, it's given to you.
00:10:52
Speaker
So here's your tools, here's your menu, make a cake. Whereas it's very, very different in in the small CRO setting. Yeah, yeah, yeah.
00:11:04
Speaker
If you were given the opportunity and like, it's like your dream setting here so you can go as wild as you can. If you were given that opportunity to, let's say reshape a data management department in a medium to large company, what would you do?
00:11:30
Speaker
That's a really broad question. And in order to answer it, I think I have to bring all of my experiences to bear around that. And really what I've seen, and what I've experienced over those many years of working in this industry is that it is very similar to trying to build an airplane mid-flight.
00:11:54
Speaker
You're trying to change the engines. You're trying to flip the wings. The undercarriage needs to be changed mid-flow, but actually in an ideal world, we want to build something that's fully integrated. And I'm talking now about a system that's got EDC, clinical trial management system, e-pro, safeties, imaging, the whole thing, but from the ground up.
00:12:17
Speaker
If I had my time again and someone gave me a blank canvas, I would start from scratch with a blank piece of paper and build it from the ground up rather than trying to adapt and build and fix on the go.
00:12:30
Speaker
Because in effect, that's really what we're doing in clinical trials today. Because of the nature of the amazing transformations that have happened digitally and electronically over the last five, 10 years, those things have had to adapt on the fly.
00:12:47
Speaker
And effectively, we're building mid-flight. The ideal, which we never have, is blank canvas to run alongside And actually, as I'm saying it, what I'm thinking is really, you know, you run your standard processes in one way, and then in real time, you're running them complementarily alongside and then switching when everything has been validated and tested and works.
00:13:14
Speaker
that's That's an approach. but Whether or not any companies have the budget, the time, the resource of people to do that is another matter. you know That's where companies, innovative companies that are developing new systems perhaps have that time to do it. But you very rarely have time to do that when you're working every single day and you're trying to adapt processes as you go.
Change Management in AI Adoption
00:13:38
Speaker
Yes, and it's not only processes, but then you need to train your staff. yeah Change brings risks, and we're a very risk adverse industry.
00:13:50
Speaker
ah and mean, at the other end, you also have your regulations, you need to follow the regulations. So you can't, you don't, I guess you never have a ah blank canvas because it's so structured around us.
00:14:04
Speaker
And but Do you think it would be possible for a large company to like kind of reshape its data management?
00:14:18
Speaker
um Or do you think we're in a situation where most companies now have established such strong processes around their existing technology that it's it's easier to add things to it rather than trying to change the core of the technology plus processes?
00:14:45
Speaker
Again, it's its said it's a very tricky question to answer. Where I've seen it work has been in big pharma. And big pharma have deep pockets. And what they've done, there's one particular example I'm thinking about without mentioning any names,
00:15:01
Speaker
where this particular big pharma company built a data science function and were effectively mining their own real world data based upon many, many years of their own clinical trial data, health outcomes research, all of that by data scientists doing deep dives into their big data sets.
00:15:22
Speaker
The one place where that fell down though, however, was that these data scientists did not necessarily understand where the data came from and how it connected through from humans to clinical trials, to the whole life cycle of those data points.
00:15:39
Speaker
So there were brilliant data scientists able to mine existing data that was already there, but it couldn't be applied to the ongoing activities within data management stats, programming, medical writing of those complementary systems that were already in place.
00:15:59
Speaker
So I have seen companies try and do that. And as I mentioned earlier, you know, they're hedging their bets. They're effectively betting on the next big thing being that which is going to provide them with another income or revenue stream based upon the work that had already been done over the last 20, 30 years within their organization.
00:16:21
Speaker
Yeah.
Staying Informed on Innovations
00:16:22
Speaker
Do you think AI can help with, mean, existing technology, which in fact, data management processes, but do you think it's possible to add AI to improve existing processes?
00:16:43
Speaker
I'd like to think it is possible. I've certainly seen examples recently where organizations have implemented and are trialing large language models, AI tools in and around those platforms.
00:16:59
Speaker
I think there has to be a mindset shift within large organizations where that can be harbored and embraced in change. And I think you made the you made the point around change. change management seems to be the thing that is probably the next big thing in in in all of our companies, because what that really means to to those who haven't experienced change management is that you have external people coming in describing what you're doing before, describing your role in the new world and effectively selling it to the people that sat in the seats doing the doing.
00:17:36
Speaker
In other words, this is your job today. This is your job tomorrow. And somewhere in between, we're going to take you along a journey and you're going to be more productive and it's going to be better for you. And life will be simpler.
00:17:48
Speaker
The data will be better and the garden will be rosy. Change management is pivotal to all of those implementations around AI that you mentioned, but also I think with respect to the regulations, all the regulators, whether it be MHRA, FDA, EMA, and others, will all say that there are no rules around AI. You just have to describe what you're doing, how you're doing it, and give any external auditor or sponsor confidence that what went in
00:18:24
Speaker
has followed due process and you've documented exactly what what you've intended to do. And you can follow that data point all the way through, following the breadcrumbs and the quality steps in place.
00:18:36
Speaker
So it's not necessarily about having process that says we use AI. It's what does your human in the loop do? What does that look like? What does that feel like? And show me, you know, demonstrate it from the nuts and bolts of a blood draw into an EDC system lab data coming in, analysis, all the way through to final reporting. It's making sure that that data point follows all the way through.
00:19:05
Speaker
That's not changed. The speed of it may change and the adoption of it may change and the cost of it initially will change and likely be more expensive. But I think the whole driving force behind this is that it should ultimately reduce the time to market for those drugs, for drug safety information through to the decision makers, the principal investigators and the pharma companies that are paying for these clinical trials.
00:19:38
Speaker
Yeah, yeah, I agree. And what's interesting, I think, with AI is also that it amplifies the change management process, because when you implement AI, I mean, what we've seen so far of AI, I don't think it's a good representation of what's the art of possible because it could completely change processes, completely change roles. So you can't really focus just on one piece of the the equation. You can't just focus on technology because you need to think about technology plus processes plus resources. And that's the tricky part.
00:20:25
Speaker
And to your point earlier about ah doing changes in flight, well, those changes become so massive that it it's probably even more difficult to make those changes in flight. So i'm I'm really interested to see what's going to happen within next two to three years, because I agree with you that the the speed of change is much faster with AI than it has ever been before.
00:20:58
Speaker
ah From your point of view, so you you you're a consultant, so you you work with many different organizations.
Passion for Clinical Research Industry
00:21:08
Speaker
Is this an expectation that your clients have of you to be knowledgeable about the latest innovation? and obviously, nowadays, that means a lot of it around AI.
00:21:24
Speaker
Is this something that they request from you?
00:21:30
Speaker
It's an interesting point because I think any anyone who works in our industry needs to stay aware of what the next big thing is. And for us right now, of course, it's AI.
00:21:42
Speaker
I think there is an expectation, certainly, that we are informed. As a consultant, I would expect to be informed. And that comes through various different guises. As you're aware, host a podcast.
00:21:53
Speaker
host a podcast And on that podcast, I speak to lots of people and one of the hottest topics right now is the use of artificial intelligence in and around clinical research.
00:22:05
Speaker
So I'm learning a lot from the people that I'm interviewing as much as other podcasts that I follow. And in reality, if you look at LinkedIn, just LinkedIn jobs right now, the amount of jobs that have AI in the title or some reference to machine learning, implementation of systems, it's all much of the same sorts of principles, but with a very big ai blanket around it so the way i sell myself and the way i sell my services is i have to keep a debt a debt of the regulations i need to know what's going on that means regularly attending conferences regularly listening to what people in the industry are saying whatever side of the fence they sit on whether there's cro farmer it doesn't matter but in reality you've got to be informed and i think as a consultant
00:22:58
Speaker
I wouldn't be doing my job and I certainly wouldn't be selling my services if I wasn't informed. I'm also fascinated by change in this industry. The reason I've stuck with it as long as I have was because it's all I ever wanted to do.
00:23:11
Speaker
I studied in order to get into clinical research and be around medical information. It's the thing that I love more than anything else, apart from my family, God bless them. But, but it's the thing that I find so fascinating. You know, I'm learning all the time and i would,
00:23:27
Speaker
hate to be in a position where everything became routine. I like change. i like interest and I'm infused by the incredible things that I'm seeing today. And that's not changed for the last 30 years. There's always been the next big thing.
00:23:42
Speaker
This is just the next big thing. Yeah. Yeah, but at the same time, i feel like... what it so I agree with you, actually. It's just the next big thing. And we've seen the next big thing multiple times in in the industry.
00:23:59
Speaker
But here, I feel like we're in a bit of a wait and see situation because think from a technology point of view, from what I can see at our end, we can, ah or we very soon will be able to do things very differently.
00:24:20
Speaker
whether the industry will be ready for it or not. That's the question that I don't have the answer to. And i'm a when I look back at innovation in the past in the clinical trials industry, I'm a bit worried that we'll be promising so much and we'll only be able to do very little of it because of those big changes that need to happen on top of technology.
00:24:53
Speaker
What do you think about that? Well, I think risk is at the heart of this, isn't it? The risk averse, the conservative will stick with the status quo.
00:25:05
Speaker
And actually, in reality, that's a large proportion of the people that we work alongside every single day. I think the innovators and those that are pushing the needle potentially that's where the success is going to happen.
00:25:18
Speaker
And as you might gather, I'm interested in being on that side of the fence because I want to be challenged. I want to try the new thing.
00:25:29
Speaker
But also, no one ever tried something and failed for want of trying. And failure is data acquisition. One of my most um overriding principles is around that.
00:25:42
Speaker
So to fail is not is not to do the wrong thing. It's to try. And trying, if you don't try, what's the point? Yeah, I agree.
00:25:55
Speaker
I have one last question for you. What's the best piece of advice you've received that you consistently apply in your work?
Systems Over Goals: A Quote from James Clear
00:26:06
Speaker
Well, there is a an author that I've been following for a number of years now, and he's called James Clear, and he wrote the book called Atomic Habits. You might be familiar with it. And in there, There is a quote that I think so really, really beautifully brings all of my principles in one swift sentence. And I'm going to read it to you because I don't want to get it wrong.
00:26:28
Speaker
And James Clear says, you do not rise to the level of your goals. You fall to the level of your systems. Habits are the compound interest of self-improvement.
00:26:42
Speaker
Nice. Do you think that applies to, uh, to, to us and our industry? Uh, I really do think it does. Yeah. I think consistently showing up being the person, being that consistent person that shows up wherever you are, whatever you're doing and repetitive behaviors, whilst they may sound very dull and boring, actually There is compounding effects around that. And I take that on a personal and a professional level. So that's why I show up, do the things I do, put out the content that I do and try and be this person that you see in front of you today, because it doesn't come through no effort. It comes through showing up, turning up every single day and just doing the right thing.
00:27:30
Speaker
Mm-hmm. Yeah, I agree. And it's interesting to see, and I'm sure you can look back at your career as well, over the years, how small habits or small changes that you make to your habits over time actually lead to big changes. and i really like that. really Thank you. Thank you. and And thank you for listening to my musings for...
00:27:58
Speaker
No, thank you very much to you. It's been an absolute pleasure. and i look forward to seeing you a future conference. It's always interesting. Absolutely. And thank you so much, Sylvain, for having me on your podcast. It's been a delight.
Conclusion and Invitation to Listen More
00:28:13
Speaker
Well, thank you all as well for watching us today. You can find more episodes of the podcast on the CRS Cube website. Thank