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<v Speaker 1>It is my pleasure to kick off our first track

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<v Speaker 1>of the day. This is Dimitrius Zambas and Doug Chance

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<v Speaker 1>and they are talking about implementing the next gen EDC

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<v Speaker 1>cd MS platform advancing data analysis capabilities.

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<v Speaker 2>So welcome, So good afternoon. So I'm going to interview Demetrius,

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<v Speaker 2>who works for a coverany I used to work for,

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<v Speaker 2>and we're going to talk about the UDMS. So, Demetrius,

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<v Speaker 2>first off, what is DMS.

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<v Speaker 3>Good afternon, Well, we made it up. It stands for

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<v Speaker 3>Unified Data Management Solution or system, and the focus was

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<v Speaker 3>really to do something in the sense of DEBT to

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<v Speaker 3>capture data simulation deta consumption that wasn't entirely focused on

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<v Speaker 3>the EDC side. At this point, about twenty five percent

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<v Speaker 3>of our data comes from the DC. The other seventy

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<v Speaker 3>five percent comes from third party sources, you know e pros, echoas,

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<v Speaker 3>central Labs, imaging centers and so on. So rather than

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<v Speaker 3>repeat the same cycle of focusing on the sexiest, most

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<v Speaker 3>glittery EDC when we set up the team to assess tools,

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<v Speaker 3>we put a heavier weighting on the back end capabilities

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<v Speaker 3>to make sure that it was something that was a

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<v Speaker 3>little more a little more points for the future, if

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<v Speaker 3>you will. On the front end, we actually spoke to

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<v Speaker 3>some sites and got some feedback there. On the front end,

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<v Speaker 3>the only matter is simple, simple, simple, Let the ciras

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<v Speaker 3>do their SDV. Let the sites have very simple, straightforward

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<v Speaker 3>dead entry, simple straightforward ways to capture their normal ranges

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<v Speaker 3>for their local labs. You know, the demand is is

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<v Speaker 3>much more straightforward, I think, on the front end than it.

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<v Speaker 4>Is in the back end.

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<v Speaker 2>So what would you say the benefit of u DMS

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<v Speaker 2>is over the conventional data management systems that we're.

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<v Speaker 4>All used to.

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<v Speaker 3>Well, again, what's it that imaginement system? I mean to

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<v Speaker 3>me that imagement system was contral.

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<v Speaker 2>You're not showing your age at all.

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<v Speaker 3>No, no, no, But you have to step back and say,

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<v Speaker 3>what is it?

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<v Speaker 4>Why are we collecting all this stuff? Well, what's the intention?

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<v Speaker 3>Right, It's not just for because we want it, although

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<v Speaker 3>in some cases we have clinices that just want to

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<v Speaker 3>collect more because they just want it. Fundamentally, it's to

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<v Speaker 3>get to the point of having a fit for purpose

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<v Speaker 3>data set to support a submission for an approval for

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<v Speaker 3>a new therapy. So working kind of from that point backwards,

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<v Speaker 3>whether it's whether it's having standards that are completely end

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<v Speaker 3>to end, and a lot of people say end to end,

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<v Speaker 3>but end to end meaning the landing place in STTM

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<v Speaker 3>for every single data point we collect is the before

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<v Speaker 3>we've collected it. That irritates some people because it takes

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<v Speaker 3>a little bit of work during the setup. But the difference,

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<v Speaker 3>you know, when I joined Feizer, we were at the

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<v Speaker 3>very bottom of the of the industry benchmarks for last

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<v Speaker 3>patient to submission, you know, the last pibolical trial, last patient,

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<v Speaker 3>last visit to submission.

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<v Speaker 4>We were dead last.

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<v Speaker 2>I was afraid you were going to say dead lasting

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<v Speaker 2>data management because I headed updated.

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<v Speaker 4>Well, data management had been dissolved.

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<v Speaker 3>It was fully outsourced, right, and it wasn't for the

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<v Speaker 3>fact that it was outsourced. Sometimes you have to make

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<v Speaker 3>sure you have the right recipe for the right oven.

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<v Speaker 4>In this case, I think it was right or wrong oven.

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<v Speaker 3>It was definitely the wrong recipe and every study in

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<v Speaker 3>the same program was done in ten different standards. Well,

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<v Speaker 3>then it took to our statistical programming group half a

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<v Speaker 3>year to get it all normalized so they could, you know,

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<v Speaker 3>do a proper summary of safety and efficacy. We're now consistently,

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<v Speaker 3>if not first but in the top quartile. And yes,

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<v Speaker 3>it's because it's better medical writing, it's better statistical programming,

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<v Speaker 3>better statistical programming environment. But I truly believe the single

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<v Speaker 3>biggest factor is that every single study is leveraging the

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<v Speaker 3>same superset of standards so that when it comes out,

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<v Speaker 3>it's ready for analysis the next day, and it's not.

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<v Speaker 3>It doesn't mean we don't add standards or change standards.

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<v Speaker 3>It means we do it before the study starts.

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<v Speaker 4>Not after.

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<v Speaker 2>So it's been a lot of work. I mean, tell me,

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<v Speaker 2>why is this a big game changer for.

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<v Speaker 3>Five Well, we so we had I think forty people

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<v Speaker 3>in an assessment team, and we did the usual We

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<v Speaker 3>send people out to conferences to s write down every

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<v Speaker 3>possible player and then selected a subset of about a

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<v Speaker 3>dozen to do a general you know, request for information response,

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<v Speaker 3>and then from there selected a smaller subset to do

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<v Speaker 3>a full RFP. And in that process we made sure

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<v Speaker 3>it's usually more of a of a d M LED

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<v Speaker 3>activity picking an e DC. But because of what this was,

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<v Speaker 3>it was much broader with ECO representation from clinical, from

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<v Speaker 3>study management, from monitoring and so on, and everybody got

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<v Speaker 3>everybody got to identify criteria that were important to that

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<v Speaker 3>part of the organization, and then we waited them based

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<v Speaker 3>on what we thought was most most critical to the

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<v Speaker 3>company from a platform perspective, like like the like the

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<v Speaker 3>ability to handle back end data loads and things like that.

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<v Speaker 4>So I've done this.

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<v Speaker 3>I had the benefit of doing of working on the

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<v Speaker 3>first industry implementation test transfer of an e DC with

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<v Speaker 3>my expos Dave de Torre, who's somewhere back over there,

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<v Speaker 3>face forwards inform into sharing power right, and then I

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<v Speaker 3>had the benefit of kind of redoing that at Mark

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<v Speaker 3>and then when I was at Nevardist I left before

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<v Speaker 3>I ex implement did, but we had started implementation of

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<v Speaker 3>medidata rave there. So having done this a few times

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<v Speaker 3>kind of you know, seeing the good, the bad, and

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<v Speaker 3>the ugly. When we formed this team, I think really

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<v Speaker 3>focused on it being more cross functional, really focusing and

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<v Speaker 3>all the different all the different components of the puzzle,

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<v Speaker 3>not just the DC piece. You know, it's very easy

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<v Speaker 3>to get to get distracted by the shiny, glittery lights

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<v Speaker 3>in a new DC when you go to someone and say, hey,

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<v Speaker 3>I wanna I want to standards super set and I

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<v Speaker 3>want to make sure that my my c disc is

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<v Speaker 3>all adequately defined.

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<v Speaker 4>Before I start to study. You know, your executives fall

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<v Speaker 4>asleep before you're done talking.

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<v Speaker 3>They want to hear that it's somehow gonna be you know,

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<v Speaker 3>solved world hunger for clinical trials. But in this particular case,

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<v Speaker 3>we got enough with enough momentum behind it cross functionally

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<v Speaker 3>to really really not focus on the glittery parts, but

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<v Speaker 3>the fundamental platform parts. And no, no data what do

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<v Speaker 3>we call you know, no batch loads? That that is

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<v Speaker 3>live from collection to consumption. It streams, it's live, and

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<v Speaker 3>no one thinks that's important until you run a COVID

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<v Speaker 3>vaccine study with forty six thousand patients and you're waiting

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<v Speaker 3>for ninety cases and you wake up at five in

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<v Speaker 3>the morning, You get your coffee and you go over

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<v Speaker 3>to your desk and you turn it on to see

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<v Speaker 3>what the data status is from the night before before

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<v Speaker 3>you fall asleep.

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<v Speaker 4>It's batch loads, no muss.

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<v Speaker 2>So you put a lot of work into this. I

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<v Speaker 2>should have asked this question earlier, But why what made you?

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<v Speaker 2>What led you to this place where you developed it?

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<v Speaker 3>Well, we had a list of things we really wanted,

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<v Speaker 3>kind of everything from a nice to have to we

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<v Speaker 3>think it's an absolute must have. So for instance, the

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<v Speaker 3>live the live data streaming. Not everyone agreed, but some

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<v Speaker 3>of us thought it was a must have, especially those

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<v Speaker 3>that went through some recent critical experiences, less hops, less

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<v Speaker 3>instances of the data again, things that when you when

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<v Speaker 3>you talk to a senior executive about prioritizing something about

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<v Speaker 3>a tool, it doesn't sound important. At a prior organization

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<v Speaker 3>I worked with that started with the letter M. Someone

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<v Speaker 3>decided to take data down the path to SDTM and

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<v Speaker 3>too adam in parallel paths.

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<v Speaker 4>It was one of the most critical submissions in the

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<v Speaker 4>company's history.

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<v Speaker 3>The FDA came back and gave us a month to

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<v Speaker 3>prove to them that the SDTM and the ATOM were equivalent.

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<v Speaker 3>It was the most horrific, stressful month of my life.

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<v Speaker 3>I would never want it. So now we have these

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<v Speaker 3>kind of caveats, there's no parallel data flows, no this,

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<v Speaker 3>no that, just to keep ourselves out of that kind

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<v Speaker 3>of trouble.

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<v Speaker 4>Uh. And it does. It doesn't mean we don't we

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<v Speaker 4>can't be innovative about it.

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<v Speaker 3>You just can't go will and Lilly taking data down

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<v Speaker 3>one hundred different paths, so wanting to consume it, meaning

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<v Speaker 3>you know, drive all the reporting from the layer that's

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<v Speaker 3>streaming live from the front end. That was an absolute

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<v Speaker 3>prerequisite requirement.

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<v Speaker 2>So Sabina is such a bad experience. Never again, is

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<v Speaker 2>what drove you? Drove you in that direction. So I'm

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<v Speaker 2>guessing that not everything went perfect. So tell us about

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<v Speaker 2>some challenges, you know, what lessons did you learn and

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<v Speaker 2>implementing a major overhaul?

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<v Speaker 3>Yeah, well, I mean, not everything goes perfect when you're

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<v Speaker 3>implementing something completely off the shelf, as it is because of.

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<v Speaker 4>The amount of integration.

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<v Speaker 3>Yeah, and when you're a company, the size of hours,

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<v Speaker 3>the amount of integration is out of this world, even

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<v Speaker 3>down to sending eesa messages to argus they have to

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<v Speaker 3>be a certain you know, the level of specific specificity

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<v Speaker 3>and the amount of specs that the safety team has

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<v Speaker 3>of how they want that data and how they want

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<v Speaker 3>the updates to that data. You wouldn't believe you think

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<v Speaker 3>it's one requirement, It's like one hundred requirements, right. So

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<v Speaker 3>definitely the integration where we thought some things would be

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<v Speaker 3>more straightforward, we're not.

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<v Speaker 4>The the actual getting agreement and alignment.

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<v Speaker 3>We had a great partner in this, but the partner

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<v Speaker 3>you know, was part of the RFPD except all those specifications.

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<v Speaker 3>But a SPECT that's written by a data manager or

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<v Speaker 3>study manager or CRA written by read by an IT

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<v Speaker 3>person doesn't always mean the same thing. So we had

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<v Speaker 3>a lot of back and forth to get on the

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<v Speaker 3>same page of what things meant.

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<v Speaker 4>And then given the scale that.

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<v Speaker 3>We are, the number of acquisitions we've had, we've literally

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<v Speaker 3>had we have studies and everything.

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<v Speaker 4>We have studies in data labs. Actually I think the

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<v Speaker 4>last one just shut down.

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<v Speaker 3>But until recently, we had a study in data apps, Oracle,

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<v Speaker 3>clinical metadata, rave Viva, from acquisition, you know, everything, everything.

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<v Speaker 3>So this particular implementation we are migrating. It's going to

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<v Speaker 3>be the it's this biggest migration in the one hundred

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<v Speaker 3>and twenty studies from Legacy Pfizer and I think thirty

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<v Speaker 3>studies from Legacy, about one hundred and fifty studies are

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<v Speaker 3>going to get migrated. And we don't mean build a

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<v Speaker 3>new study and migrate the content. Lily did that last

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<v Speaker 3>year with about twenty thirty studies. We're actually creating the

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<v Speaker 3>data models in the new system with a migration tool

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<v Speaker 3>and then pulling the data over and getting all the

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<v Speaker 3>study teams on board. One hundred and thirty of them

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<v Speaker 3>with the best week and months for their study to

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<v Speaker 3>basically pause.

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<v Speaker 4>For us to do this. We're almost We're almost there.

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<v Speaker 3>I think the first studies, the first migrated studies, are

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<v Speaker 3>intesting right now. The system went live, the first study

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<v Speaker 3>went live. I think we have our first patient in

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<v Speaker 3>the study. No, not yet, they keep It's what almost

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<v Speaker 3>what the study team felt it was important to accelerated

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<v Speaker 3>from mid September to meet August for the build was

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<v Speaker 3>still waiting for the first patient. At least you're not crying, no, no, no, yeah, yeah, yeah.

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<v Speaker 4>But it's one thing to go live. It's another thing.

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<v Speaker 4>One thing to build a study and then to present

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<v Speaker 4>test data.

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<v Speaker 3>It's another thing to have a site actually starting putting

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<v Speaker 3>patients in it.

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<v Speaker 2>So, I mean it's kind of a wrap up. Why

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<v Speaker 2>are you excited about this?

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<v Speaker 4>What?

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<v Speaker 2>Why should we be excited for you?

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<v Speaker 4>Well, it's new. I mean you're not. You're not.

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<v Speaker 3>You can't go and say I'm leading this space or

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<v Speaker 3>my team is the most advanced in this space. If

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<v Speaker 3>you're just getting another another same off the sheelf tool

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<v Speaker 3>as everybody else. Uh here was an opper and and

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<v Speaker 3>the big boys. There was a time when they were smaller,

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<v Speaker 3>and you could you could influence that.

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<v Speaker 4>Just even with face.

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<v Speaker 3>Forward before they were acquired by a bigger company. But

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<v Speaker 3>I think here we had the opportunity to really sit

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<v Speaker 3>down and say, what's what do the sites want, what

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<v Speaker 3>do the cis want, what do the clinicians want, what

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<v Speaker 3>did that managers want? And found a partner that was

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<v Speaker 3>crazy enough to jump in with us to do it.

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<v Speaker 3>So I'm truly excited that we're going to have the

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<v Speaker 3>most advanced unified EDC data management environment in the industry.

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<v Speaker 3>And I'm sure we'll have bugs as we start to

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<v Speaker 3>execute the first studies, there's no doubt, but everything from

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<v Speaker 3>how it's set up, to how the third party data

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<v Speaker 3>is assimilated, to how that it flows, to reporting and

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<v Speaker 3>to safety is it's optimized across all them instead of

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<v Speaker 3>you know, kind of pick one or two instead of

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<v Speaker 3>all of them.

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<v Speaker 2>So in the last minute and twenty some odd seconds,

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<v Speaker 2>what's next, What's what's the next version or the next

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<v Speaker 2>enhancement or the next innovation.

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<v Speaker 3>Well, we have some we already have planned for. So

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<v Speaker 3>this is version two of red kept Cloud that's going

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<v Speaker 3>in now. Version one was already available to the public

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<v Speaker 3>before we've already got specs for version three, which my

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<v Speaker 3>team wants to you know, screaming that we haven't even

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<v Speaker 3>finished version two, but there's critical things we want to

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<v Speaker 3>have there in version three. And then based another work

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<v Speaker 3>we're doing, whether it's the setup or the signal detection

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<v Speaker 3>leveraging a gentic AI that I think we're talking about

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<v Speaker 3>the tomorrow in another session is very exciting, especially when

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<v Speaker 3>it's live and you can kind of prevent issues from proliferating,

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<v Speaker 3>you know, running running a signal detection for incential monitoring

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<v Speaker 3>and once a quarter which many companies do. Do you

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<v Speaker 3>know in a vaccine study once a quarter roment is

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<v Speaker 3>finished you or even if you even if it's not finished,

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<v Speaker 3>if you have thousands of patients through and you realize

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<v Speaker 3>they're interproperly reporting AEES or whatever the case is. So

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<v Speaker 3>I think think starting with this being live, the third

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<v Speaker 3>part of data being loaded in a romatic way and

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<v Speaker 3>having single detection running on it live, it's got to

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<v Speaker 3>be the it's it's got to be how we have

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<v Speaker 3>to think to be strius.

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<v Speaker 2>It was great talking with you this afternoon. Thank you

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<v Speaker 2>very much.

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<v Speaker 4>Yeah, thank you