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The future of technology and AI in clinical trials with Joe Dustin
7 Plays10 months ago
In this episode of Clinical Data Talks, host Sylvain Berthelot welcomes industry veteran Joe Dustin to explore the evolving landscape of eClinical technology and the impact of AI in clinical trials. With decades of experience across eClinical vendors and the sponsor side, Joe shares his insights into the major transformations shaping the industry - from paper-based processes to electronic data capture, on-premise systems to cloud platforms, and now, the rise of AI-driven solutions.
Together, they discuss the current challenges of interoperability and fragmentation in clinical data, the potential of AI to enhance data management and trial design, and how standardization efforts like the USDM (Unified Study Definitions Model) are paving the way for faster and more efficient studies.
This conversation is packed with valuable perspectives on where clinical technology is heading.
Clinical Data Talks is a podcast brought to you by CRScube, a leader in eClinical technology solutions.
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Transcript
Introduction to Clinical Data Talks
00:00:14
Speaker
Welcome to Clinical Data Talks, a podcast brought to you by CRS-Cube.
00:00:19
Speaker
I'm your host, Sylvain Bertolo.
00:00:21
Speaker
Join me and industry experts as we discuss the latest trends impacting the world of clinical data.
00:00:32
Speaker
My guest today probably doesn't need an introduction.
00:00:36
Speaker
Like me, he's a rock star at heart.
00:00:39
Speaker
The world just doesn't know it yet.
00:00:42
Speaker
And besides music, he has a real passion for the clinical industry.
00:00:47
Speaker
He's worked at e-clinical vendors.
00:00:49
Speaker
He's been on the sponsor side as well.
00:00:52
Speaker
And he's always keen to give a hand to shape some of the greatest events in the industry.
00:00:57
Speaker
I'm very pleased to welcome Joe Dustin.
00:01:01
Speaker
Hey, Joe, how are you doing?
00:01:03
Speaker
It's quite the intro.
00:01:04
Speaker
Thanks, Sylvain.
00:01:05
Speaker
Nice to see you.
00:01:08
Speaker
And nice to see you too.
Challenges in Clinical Data Technology
00:01:10
Speaker
So today we're going to talk about the future of e-clinical technology.
00:01:16
Speaker
We're going to talk about AI as we go along.
00:01:20
Speaker
But I want it to be kind of useful in a way.
00:01:24
Speaker
I don't want us to go in crazy things that are never going to be handy or help people bring clinical trials to
00:01:34
Speaker
to the industry.
00:01:36
Speaker
So to anchor us a bit, could you share with us what you think is limiting in the technology we use for data collection and analysis?
00:01:51
Speaker
Well, just getting right into it.
00:01:52
Speaker
I think that first of all, I've been in, I've been in clinical trials for 20 years, so I've seen a number of transformations over that time.
00:01:59
Speaker
Uh, when I started in this industry, we were still doing most things on paper, um, say early two thousands, right around the.com crash at early 2002 or so.
00:02:11
Speaker
That was when I started.
00:02:14
Speaker
And, um,
00:02:16
Speaker
I've seen the transformations of paper to electronic on premise to the cloud.
00:02:21
Speaker
And now I would say we're in the third transformation, which is typically revolving around some sort of economic crash where pharma are, you know, the industry at large that is doing clinical research.
00:02:35
Speaker
tends to reshape their business and an opportunity to have to do more with less.
00:02:40
Speaker
I need to transform what I'm doing.
00:02:42
Speaker
I reorg the company, I lay people off, it happens, right?
00:02:45
Speaker
And then the technology side tends to rise during that time to help with that sort of next wave of transformation.
00:02:54
Speaker
But every time that those things have happened, there's been some sort of limiting factor that has caused the next wave to change, right?
Industry Issues: Fragmentation and Standardization
00:03:02
Speaker
If you're talking about clinical data, the core, like the gold, the core of what clinical trials needs to be successful, you need good data, you need high quality data, and you need a lot of it, right?
00:03:15
Speaker
From globally, from around the world.
00:03:18
Speaker
I think some of the biggest issues right now are
00:03:23
Speaker
It's, it's, it's things are fragmented right now.
00:03:26
Speaker
We're back in sort of a hyper fragmentation state post COVID.
00:03:30
Speaker
Um, and we have, I think major interoperability issues in general.
00:03:36
Speaker
Um,
00:03:37
Speaker
And I don't know if you or others feel the same way, but big pharma always talks about, I would wish I had one platform, but they never buy it.
00:03:47
Speaker
Smaller pharma, biotechs, prefer to have a single platform to reduce the fragmentation issues, to reduce the best of breed integration problems and interoperability, right?
00:03:58
Speaker
As what I said, is one of the major issues.
00:04:02
Speaker
And then CROs just sort of do whatever the sponsor says.
00:04:05
Speaker
And when they have their own opinion, they sometimes can be faster and more efficient, but in many cases, they're asked to be pulled in seven different directions and they have to kind of react to whatever their client asks them to do.
00:04:19
Speaker
Um, however, there are ways for them to be more efficient by standardizing on things.
00:04:22
Speaker
So interoperability issues and then standardization issues are, I think lack of standardization is, is some of the big issues.
00:04:30
Speaker
Now we have C-disc standards, right?
00:04:31
Speaker
We have even HL7 in healthcare, right?
00:04:35
Speaker
The FHIR standards are, are moving data between.
00:04:38
Speaker
pharma, like large pharma started and even large CROs started creating their own platforms to reduce the fragmentation.
00:04:52
Speaker
But now, maybe tell me if you agree or not on that, but now we've got
00:04:58
Speaker
a lot of fragmentation, but there might not be so much of a push from large pharma, large CROs for platforms anymore because they've done their own and to the point that it might be a bit difficult for them to adapt to new technology.
00:05:17
Speaker
Do you agree with that or not?
AI's Role in Software Disruption
00:05:19
Speaker
I think it's because they're doing their own.
00:05:21
Speaker
They're still buying stuff off the shelf and they are supplementing some of those off the shelf things with the custom stuff they're doing internally.
00:05:28
Speaker
But again, that also goes through cycles.
00:05:31
Speaker
So I would not say that pharma is building their own technology right now and building their own platforms.
00:05:36
Speaker
I would say with the use of AI, there are, I think the implementation of AI and various manifestations of this in pharma and in general will disrupt what we consider today as software as a service.
00:05:50
Speaker
And this is the future thing to look for, as in it'll become easier for large organizations to develop their own
00:05:59
Speaker
application layer stuff without having to pay the fees to bigger companies.
00:06:03
Speaker
I'm talking like Salesforce and other like bigger Microsoft and like other big companies, but I don't think they're going to continue to build their own platforms.
00:06:10
Speaker
They're going to build
00:06:13
Speaker
data layers, data lakes, integration layers that will allow companies to, if they have a best of breed solution, I want to know what that is and I want to pull it into my platform and pharma.
00:06:26
Speaker
Many of them say, I don't really care about your platform.
00:06:28
Speaker
I care about your capability that you do better than anyone else.
00:06:31
Speaker
That's where you get more.
00:06:33
Speaker
That's why I think more of the fragmentation has been popping up more again.
00:06:37
Speaker
Yeah.
00:06:38
Speaker
Yeah.
00:06:38
Speaker
Yeah.
00:06:38
Speaker
I agree.
00:06:40
Speaker
So more interoperability then that's one, one area of, of change.
00:06:47
Speaker
And you mentioned AI, so it's hailed as revolutionary, but it's also quite scary for a lot of people because we don't really know what's going to happen.
00:07:01
Speaker
Where do you stand on, on the topic of AI applied to clinical trials and clinical data?
00:07:10
Speaker
You know,
00:07:13
Speaker
I'm pretty bullish on it.
00:07:15
Speaker
I think many people are, well, they want to be careful.
00:07:19
Speaker
And I agree.
00:07:20
Speaker
We need oversight.
00:07:22
Speaker
I think we need
00:07:25
Speaker
Listen, the adoption rates.
00:07:28
Speaker
I was looking at some stats, you know, yesterday when I was preparing for this recording for our podcast.
00:07:34
Speaker
And as of like December of 2024, I was showing about 49% of pharmaceutical and biotech companies have implemented AI and big data in their programs.
00:07:44
Speaker
And this would reflect what?
00:07:48
Speaker
A growing acceptance of AI, but with caution, especially in pharma, because they're highly conservative.
00:07:54
Speaker
But I used to work at Bristol Myers Squibb.
00:07:57
Speaker
I ran clinical innovation there.
00:07:58
Speaker
And it feels like a totally different company now since I've left.
00:08:04
Speaker
One is during economic crash, they ripped apart clinical operations and changed everything.
00:08:08
Speaker
But number two, they have new chief data people in charge, new IT leaders, right?
00:08:13
Speaker
That are forward thinking, that are not afraid to take some risks and try new things.
00:08:20
Speaker
And so you see people that are embedded in those larger organizations that like over a weekend say, we need to roll out these large language models inside our firewall and start working on this now, because not only does it help us with drug discovery or supply chain stuff for just, you know, even in clinical operations, there are opportunities.
00:08:43
Speaker
But where it started is like what you're seeing right now in pharma, the low risk stuff is just intelligent automation.
00:08:51
Speaker
It's not really AI.
00:08:52
Speaker
It's not really machine learning.
00:08:53
Speaker
It's just intelligent automation that we were doing before with like RPA, robotic process automation bots that you had to program and now you know it's program and they're kind of more prompted, right?
00:09:04
Speaker
Yeah.
00:09:06
Speaker
but we're the real, the real place.
00:09:09
Speaker
I think we can make changes with AI and clinical data management and clinical trials.
00:09:15
Speaker
It's going to be in data management for sure.
00:09:18
Speaker
So that that's one area where AI is having a major impact, um, data cleansing, quality control, uh, data cleaning, data analysis, data management, like screw data management, like prepping the data before it can be sent to biostats like that, all that stuff.
00:09:35
Speaker
Um,
00:09:37
Speaker
is part of it.
00:09:39
Speaker
Yeah, and you mentioned standardization.
00:09:42
Speaker
With standardization, you can even benefit from AI even more.
00:09:51
Speaker
Because at the moment, we're using AI to, well, some companies use AI to read protocols and start doing some of the early data management side.
00:10:05
Speaker
But I'm looking forward to standardization as well, like the USDM, for example, which hopefully will help accelerate this setup and make study changes easier as well.
00:10:19
Speaker
So in terms of data analysis and or data collection, where do you see AI having an impact?
AI in Patient Recruitment and Trial Design
00:10:29
Speaker
In data collection?
00:10:30
Speaker
Yeah.
00:10:31
Speaker
I think
00:10:33
Speaker
I think you're going to have a number of, well, there's a few different areas, right?
00:10:41
Speaker
I think in patient recruitment, you're going to have AI to help clinical trial matching to find the right patients.
00:10:46
Speaker
So you go earlier in the stage to find the patients.
00:10:48
Speaker
Once you get them, you start collecting data, you'll be able to use AI.
00:10:54
Speaker
If I'm thinking about it from a scientific perspective to sort of make dynamic protocol adjustments, adaptive trials, right?
00:11:00
Speaker
You'd be able to have.
00:11:02
Speaker
different type of adaptive trial designs with AI.
00:11:05
Speaker
And this is where the clinical development teams will be using this to continuously review the data over and over again.
00:11:09
Speaker
I've seen solutions out there in trade shows and whatnot of people doing real time
00:11:16
Speaker
ingestion of data and showing kind of almost like the stats review of like, are we hitting our endpoints yet?
00:11:22
Speaker
Are we hitting our, as the data's coming in on a daily basis, it's just kind of changing.
00:11:27
Speaker
No one's ever had this before.
00:11:28
Speaker
It was more of a, you know, we do an interim lock, we do a review, we see where we are, and then we decide, do we need more sites?
00:11:33
Speaker
Do we need more patients?
00:11:34
Speaker
We go there.
00:11:36
Speaker
This is not mainstream yet, but it's because of a lot of this AI work being able to process the data in real time, but also laying the pipes and the ingestion pipes to standardize the data and bring it in in a substandard format is making this possible.
00:11:51
Speaker
It's all in the name of speed and quality, right?
00:11:54
Speaker
So, so, all right, adaptive trial designs are one thing.
00:11:57
Speaker
Um, I think you're going to see in an, an addition and enhancement of the use of wearables and digital biomarkers turning into digital endpoints, um, will be enhanced.
00:12:07
Speaker
Uh, when I say enhanced, I mean, you're going to see more of them getting approved by FDA and global regulators that will then, cause right now there's tons of work being done in, in the digital data collection world with wearables and, and
00:12:20
Speaker
and other things.
00:12:21
Speaker
Some of them are multimodal, right?
00:12:23
Speaker
It's multiple devices on your body, plus an e-probe device, plus other things, biomarkers from genetics and lab tests combined with these things.
00:12:33
Speaker
And AI brings all that stuff together and gives you some answers faster.
00:12:37
Speaker
But what you're seeing is a lot of what others in the industry would consider pilot studies, these smaller studies, really is just doing the work to test out is that biomarker valid or not.
00:12:47
Speaker
Yeah.
00:12:48
Speaker
Yeah.
00:12:49
Speaker
And then once that's valid, they go back to the beginning again and use it in a real study.
00:12:53
Speaker
Like as in, you know, I have nocturnal scratching algorithms now, now that I've tested it and made it valid and FDA is checked off and say, yeah, that's, we would accept that.
00:13:02
Speaker
Now they go back and do legit trials and atopic dermatitis with that as the end point.
00:13:07
Speaker
Right.
00:13:08
Speaker
And you're going to see more of those type of things happening and AI will speed that process.
00:13:14
Speaker
A lot of it is also understanding the regulatory approval process and having people along the way to manage the services that go along getting that done.
00:13:24
Speaker
And that's interesting, actually, because that's what limited the adoption of wearables to start with, because that was such an investment.
00:13:33
Speaker
No one wanted to be the first one to do it because they didn't know if it was going to be approved.
00:13:38
Speaker
So you could throw money at it and then it not having any return on investment.
00:13:46
Speaker
And also the investment you're putting yourself as a farmer, well, anyone else will be able to benefit from it because once you've proved that you can do it, then everyone else can follow and they don't have to run the studies anymore.
00:14:00
Speaker
So if you can accelerate those with AI, that's going to be very good because then you increase the amount of data you can use.
00:14:10
Speaker
You'll see some pharma companies starting to license these algorithms to other pharma companies.
00:14:15
Speaker
If it builds to that level, it's starting now, but it's small, but I think it's getting there.
00:14:20
Speaker
In addition, I think you'll see...
00:14:24
Speaker
I don't know, you'll see it moving along faster because those who are starting now, they don't know if it's going to work.
00:14:31
Speaker
And it's just a lot, think about data management, it's a lot of data to collect just to collect it and I don't know what we're going to do with it yet.
00:14:36
Speaker
So it has to be very purpose, fit for purpose used for why we're collecting the data.
00:14:41
Speaker
And that's why a lot of folks just haven't been doing it.
00:14:44
Speaker
If we did, we would have a lot more data to play with and run algorithms on, but people have been very, they're not just putting wearables on patients just because.
00:14:53
Speaker
So that's why it's sort of taken longer.
00:14:54
Speaker
We don't have a lot of the data.
00:14:55
Speaker
It takes a lot of patience to do that.
00:14:57
Speaker
Yeah.
00:14:59
Speaker
Yeah.
The Functionality of AI in Clinical Data
00:15:00
Speaker
So when I think about AI, I think about it as a tool.
00:15:04
Speaker
It's interesting because we're very focused on it and AI being almost a goal.
00:15:12
Speaker
But for me, AI is like an engine.
00:15:18
Speaker
When you buy a car, you care about the engine, but you care about what this engine is going to help you do, not about it as a goal.
00:15:31
Speaker
So from your point of view, do you think about innovation just from AI or do you think we'll see other types of innovation that will be as good as AI?
00:15:48
Speaker
It's a good question.
00:15:48
Speaker
I think AI is a, it's one of those moments where it's a major step change in our industry and how we think about doing things today.
00:15:59
Speaker
Do we do tasks, how we do work, how we collect data, how we analyze things.
00:16:05
Speaker
I don't think there's been a
00:16:07
Speaker
as significant of a change since maybe the iPhone and the smartphone in general and how we have these things in our pocket now.
00:16:14
Speaker
And it just changes fundamentally how we, how we connect with people, how we live our life, how we buy things, how we, all that.
00:16:21
Speaker
Right.
00:16:23
Speaker
There have been a number of buzzwords in our industry, specifically through the years that have come and gone.
00:16:29
Speaker
You know, it was, it was,
00:16:31
Speaker
EDC, it was space monitoring, it was mobile health, it was decentralized trials, it was AI, like all these things are, they were big deals and then they kind of, you know, go through waves.
00:16:43
Speaker
What I would say is each of those things had like the sexy, shiny sort of allure to it.
00:16:51
Speaker
But when it became less interesting, it just kind of became mainstream.
00:16:55
Speaker
I think people are focusing on AI right now because it has the highest impact to speed and cost.
00:17:02
Speaker
And everybody's hurting right now with pipelines in rough shape, trying to accelerate things and trying to reduce their costs and do more with less.
00:17:10
Speaker
And I think that's just got everyone's attention.
00:17:12
Speaker
That's why.
00:17:13
Speaker
But the reality of promise of AI is the generative sense is the cautious part, right?
00:17:19
Speaker
It's like we don't want to be generating things that are going to lead us down to false positives, right?
00:17:25
Speaker
I see
00:17:28
Speaker
AI itself is a misnomer in some cases, right?
00:17:31
Speaker
True machine learning in many ways has been going on for many years.
00:17:35
Speaker
It's just becoming easier, more democratized to use.
00:17:38
Speaker
But in drug discovery, for example, I'm seeing an innovation
00:17:44
Speaker
Maybe it's not as new to others, but to me, at least it was a bit of a revelation.
00:17:48
Speaker
I'm seeing a lot of these, there's biotechs, but there's tech bios.
00:17:51
Speaker
These tech bios that just, their core of their company is this technology that they have.
00:17:55
Speaker
Some of it is AI and drug discovery, identifying targets earlier, and they just decide to develop their own drugs.
00:18:02
Speaker
And why?
00:18:03
Speaker
Because big pharma isn't probably going to be the one to fundamentally change the way they develop drugs.
00:18:09
Speaker
It's gotta be some new company that has no hundred year history.
00:18:13
Speaker
of doing this, right?
00:18:14
Speaker
So they're going to develop drugs in new ways because that's just how they started, right?
00:18:18
Speaker
At the core, it's just different.
00:18:19
Speaker
I think you're going to see a number of those popping up and they're just going to get things done significantly faster and cheaper than the top 10 pharma of the world.
00:18:29
Speaker
And that's going to turn heads.
00:18:31
Speaker
Now,
00:18:32
Speaker
Is AI the thing that did that?
00:18:34
Speaker
I think it's part of it.
00:18:35
Speaker
Maybe it helped identify a target faster and help them do protein folding and all kinds of wacky things in the lab faster.
00:18:42
Speaker
But fundamentally, the innovation is the fact that they just started from a different place and they decided that using technology at our core as opposed to science is the core, but technology is now also the core.
00:18:56
Speaker
that fundamentally was the innovation that would get them forward I think we're going to see a lot more of that in the last few years yeah companies that make headlines there um yeah but it's interesting because like so
00:19:12
Speaker
Reading between the lines a bit, I think you're talking about accelerating clinical trials, accelerating clinical research, like the fundamentals of clinical research early on, making it easier to analyze data on an ongoing basis.
00:19:30
Speaker
So kind of a supercharged data monitoring, which allows you to react faster.
00:19:37
Speaker
But we started talking about the problem of interoperability.
Interoperability Challenges and Solutions
00:19:44
Speaker
And do you think AI is going to help with that?
00:19:48
Speaker
Because I think it's still a challenge and it's still going to be a challenge, no matter how much technology we throw at it, if we don't really focus on that problem.
00:20:01
Speaker
I don't think AI is a silver bullet to fix interoperability.
00:20:04
Speaker
What I do think is you're going to have different players coming into the mix that will potentially solve the interoperability problem because they solve other problems for themselves, which naturally then closes the loop on the reason why interoperability doesn't work.
00:20:19
Speaker
If you're able to get data from sites directly now and not have them transcribing things and not have them, you know, this is
00:20:25
Speaker
the starting right now with the whole EHR to EDC idea, but I think it's going to go beyond that.
00:20:30
Speaker
I think sites are going to start having their own clinical data infrastructure, clinical data infrastructure, which is fundamentally different than what sponsors are used to today.
00:20:38
Speaker
That is a huge transfer of
00:20:40
Speaker
control, right?
00:20:44
Speaker
That will start to help solve the interoperability issue because if you can connect to sites, you're not going to do custom integrations to every site.
00:20:53
Speaker
It needs to be a standardized way to plug in.
00:20:55
Speaker
And if there's like a seal of approval or a stamp that does not exist today,
00:21:00
Speaker
that approves them, I think that is a pathway for, yes, we have standards, but now they're almost enforced because the only way this works for your business is if you can get your own shop in line and the technology works for you, but you get more trials when you can connect.
00:21:16
Speaker
And if that connection is easy, low cost, standardized and almost like a push button security authorization, I think we start to things start to change a little bit more.
00:21:27
Speaker
It becomes this network effect, like the dual sides that are benefiting from the standardization.
00:21:32
Speaker
It's not just it's always been just pharma that really benefits.
00:21:37
Speaker
As soon as you get more benefits to other stakeholders,
00:21:40
Speaker
the markets i think will change in a way i think ai is really going to have the right impact when we stop talking about it when we don't actually realize that what we're doing is using ai and and we we can focus on on more less repetitive tasks and tasks that really add more value
00:22:04
Speaker
of what i've been saying for years when digital health just becomes health we know we've succeeded yes yeah that's a very good point right i've got one last question for you joe um
00:22:18
Speaker
because you sharing your knowledge is not enough.
Career Advice from Glenn De Vries
00:22:22
Speaker
I want to get even more out of you today for our audience.
00:22:28
Speaker
What's the best piece of advice you've received in the past that you consistently apply in your work?
00:22:39
Speaker
It was Glenn De Vries, who was the president of Medina.
00:22:42
Speaker
And we were
00:22:45
Speaker
we were just noodling on ideas and mocking up demos of what ended up becoming Medidata's E-Pro solution to patient cloud.
00:22:53
Speaker
And we were just trying to figure out how to get this first thing set up.
00:22:56
Speaker
And I was like, I'm trying to think outside the box here, Glenn, I don't know how to start.
00:23:00
Speaker
And he's just like, no, no, no, there is no box.
00:23:03
Speaker
Start there.
00:23:05
Speaker
And so it's just a different way of thinking.
00:23:07
Speaker
There's no like catchy catchphrase besides, you know, that don't put yourself in the constraint of a box to get out of in the first place.
00:23:16
Speaker
It was just kind of think beyond where we were and then we would
00:23:22
Speaker
we would do some really cool work at that time.
00:23:25
Speaker
So I consistently think about, I guess his catchphrase was always do epic shit.
00:23:33
Speaker
That's what we always want to do.
00:23:35
Speaker
And that's how I, I guess that's how I sort of live my life in general.
00:23:40
Speaker
Not just at work, but everywhere.
00:23:42
Speaker
And I think life is too short to be boring and ordinary.
00:23:46
Speaker
I think you want to do really cool stuff, really interesting stuff.
00:23:49
Speaker
At this point, even with whether it's clients I'm working with or new jobs I might take, I just want to work with cool people I want to hang out with.
00:23:57
Speaker
And that really can make an impact in the world and keep things interesting and not just do anything that's like a me too.
00:24:05
Speaker
And so that's my obsession with being
00:24:08
Speaker
different and trying to change things.
00:24:12
Speaker
I've always embraced change.
00:24:13
Speaker
Well, thanks a lot, Joe.
00:24:14
Speaker
That's been very nice talking to you.
Guitar Talk and Jam Session Ideas
00:24:18
Speaker
What is the last guitar that you bought?
00:24:22
Speaker
Oh, the last guitar that I bought is a Gretsch.
00:24:29
Speaker
A bright blue Gretsch.
00:24:34
Speaker
Hollow body.
00:24:35
Speaker
Yeah, yeah.
00:24:36
Speaker
Hollow body with the, you know, the Gretsch.
00:24:40
Speaker
I can't remember what you call it.
00:24:41
Speaker
The whammy bar.
00:24:43
Speaker
Yes.
00:24:43
Speaker
Yeah, yeah.
00:24:44
Speaker
I absolutely love it.
00:24:45
Speaker
It's got like a very nice full sound.
00:24:49
Speaker
Yeah, I love that.
00:24:50
Speaker
Yeah, the last one I got, not a grudge, but similar, is my first semi-Hollabody.
00:24:57
Speaker
I was in Nashville and I went to Carter's Vintage Guitars, as one does when you're in Nashville.
00:25:02
Speaker
It's like the hardware store of Nashville.
00:25:05
Speaker
You're around all these warehouses, they're all just recording studios and random people walk in and sweatpants and a hat and they're all just crazy rock stars you wouldn't even know.
00:25:15
Speaker
I got this guitar called the D'Angelico and it was a,
00:25:22
Speaker
It's beautiful, but it's like, it looks like a Gibson ES-355, but it doesn't have the hole in it.
00:25:26
Speaker
It doesn't have the F hole.
00:25:28
Speaker
So it's just a semi hollow body, but it's solid on the front.
00:25:32
Speaker
And it's got that bright sound.
00:25:33
Speaker
It's got beautiful, like old hardware.
00:25:37
Speaker
And it's like this blue is off this like lighter blue.
00:25:40
Speaker
It's really cool.
00:25:41
Speaker
So I'm a fan.
00:25:42
Speaker
It rocks, but it also kind of gets that like kind of jazzy, jazzy.
00:25:48
Speaker
Nice.
00:25:49
Speaker
One day we'll have to play together.
00:25:52
Speaker
I feel like I've been trying to organize at some industry conference, some random like all-star jam session.
00:25:58
Speaker
Yeah.
00:25:58
Speaker
And, uh, it's hard to organize because if you just have equipment on stage, you can just plug in that.
00:26:03
Speaker
We just got to figure that out.
00:26:04
Speaker
It'll just happen.
00:26:05
Speaker
Yeah.
00:26:05
Speaker
Someone has to sponsor that one.
00:26:08
Speaker
So let's do it.
00:26:09
Speaker
Yeah.
00:26:10
Speaker
Yeah.
00:26:11
Speaker
Well, yeah.
00:26:12
Speaker
Thanks a lot for your time and I, uh, we'll see you soon.
00:26:17
Speaker
I'm sure it's a, it's an industry event.
00:26:20
Speaker
Thanks for having me.
00:26:21
Speaker
Take care.
00:26:22
Speaker
You too.
00:26:23
Speaker
And thanks everyone for listening to us.
00:26:26
Speaker
And we'll see you soon on another episode of Clinical Data Talks.