Speaker 1: It is my pleasure to kick off our first track
Speaker 1: of the day. This is Dimitrius Zambas and Doug Chance
Speaker 1: and they are talking about implementing the next gen EDC
Speaker 1: cd MS platform advancing data analysis capabilities.
Speaker 2: So welcome, So good afternoon. So I'm going to interview Demetrius,
Speaker 2: who works for a coverany I used to work for,
Speaker 2: and we're going to talk about the UDMS. So, Demetrius,
Speaker 2: first off, what is DMS.
Speaker 3: Good afternon, Well, we made it up. It stands for
Speaker 3: Unified Data Management Solution or system, and the focus was
Speaker 3: really to do something in the sense of DEBT to
Speaker 3: capture data simulation deta consumption that wasn't entirely focused on
Speaker 3: the EDC side. At this point, about twenty five percent
Speaker 3: of our data comes from the DC. The other seventy
Speaker 3: five percent comes from third party sources, you know e pros, echoas,
Speaker 3: central Labs, imaging centers and so on. So rather than
Speaker 3: repeat the same cycle of focusing on the sexiest, most
Speaker 3: glittery EDC when we set up the team to assess tools,
Speaker 3: we put a heavier weighting on the back end capabilities
Speaker 3: to make sure that it was something that was a
Speaker 3: little more a little more points for the future, if
Speaker 3: you will. On the front end, we actually spoke to
Speaker 3: some sites and got some feedback there. On the front end,
Speaker 3: the only matter is simple, simple, simple, Let the ciras
Speaker 3: do their SDV. Let the sites have very simple, straightforward
Speaker 3: dead entry, simple straightforward ways to capture their normal ranges
Speaker 3: for their local labs. You know, the demand is is
Speaker 3: much more straightforward, I think, on the front end than it.
Speaker 4: Is in the back end.
Speaker 2: So what would you say the benefit of u DMS
Speaker 2: is over the conventional data management systems that we're.
Speaker 4: All used to.
Speaker 3: Well, again, what's it that imaginement system? I mean to
Speaker 3: me that imagement system was contral.
Speaker 2: You're not showing your age at all.
Speaker 3: No, no, no, But you have to step back and say,
Speaker 3: what is it?
Speaker 4: Why are we collecting all this stuff? Well, what's the intention?
Speaker 3: Right, It's not just for because we want it, although
Speaker 3: in some cases we have clinices that just want to
Speaker 3: collect more because they just want it. Fundamentally, it's to
Speaker 3: get to the point of having a fit for purpose
Speaker 3: data set to support a submission for an approval for
Speaker 3: a new therapy. So working kind of from that point backwards,
Speaker 3: whether it's whether it's having standards that are completely end
Speaker 3: to end, and a lot of people say end to end,
Speaker 3: but end to end meaning the landing place in STTM
Speaker 3: for every single data point we collect is the before
Speaker 3: we've collected it. That irritates some people because it takes
Speaker 3: a little bit of work during the setup. But the difference,
Speaker 3: you know, when I joined Feizer, we were at the
Speaker 3: very bottom of the of the industry benchmarks for last
Speaker 3: patient to submission, you know, the last pibolical trial, last patient,
Speaker 3: last visit to submission.
Speaker 4: We were dead last.
Speaker 2: I was afraid you were going to say dead lasting
Speaker 2: data management because I headed updated.
Speaker 4: Well, data management had been dissolved.
Speaker 3: It was fully outsourced, right, and it wasn't for the
Speaker 3: fact that it was outsourced. Sometimes you have to make
Speaker 3: sure you have the right recipe for the right oven.
Speaker 4: In this case, I think it was right or wrong oven.
Speaker 3: It was definitely the wrong recipe and every study in
Speaker 3: the same program was done in ten different standards. Well,
Speaker 3: then it took to our statistical programming group half a
Speaker 3: year to get it all normalized so they could, you know,
Speaker 3: do a proper summary of safety and efficacy. We're now consistently,
Speaker 3: if not first but in the top quartile. And yes,
Speaker 3: it's because it's better medical writing, it's better statistical programming,
Speaker 3: better statistical programming environment. But I truly believe the single
Speaker 3: biggest factor is that every single study is leveraging the
Speaker 3: same superset of standards so that when it comes out,
Speaker 3: it's ready for analysis the next day, and it's not.
Speaker 3: It doesn't mean we don't add standards or change standards.
Speaker 3: It means we do it before the study starts.
Speaker 4: Not after.
Speaker 2: So it's been a lot of work. I mean, tell me,
Speaker 2: why is this a big game changer for.
Speaker 3: Five Well, we so we had I think forty people
Speaker 3: in an assessment team, and we did the usual We
Speaker 3: send people out to conferences to s write down every
Speaker 3: possible player and then selected a subset of about a
Speaker 3: dozen to do a general you know, request for information response,
Speaker 3: and then from there selected a smaller subset to do
Speaker 3: a full RFP. And in that process we made sure
Speaker 3: it's usually more of a of a d M LED
Speaker 3: activity picking an e DC. But because of what this was,
Speaker 3: it was much broader with ECO representation from clinical, from
Speaker 3: study management, from monitoring and so on, and everybody got
Speaker 3: everybody got to identify criteria that were important to that
Speaker 3: part of the organization, and then we waited them based
Speaker 3: on what we thought was most most critical to the
Speaker 3: company from a platform perspective, like like the like the
Speaker 3: ability to handle back end data loads and things like that.
Speaker 4: So I've done this.
Speaker 3: I had the benefit of doing of working on the
Speaker 3: first industry implementation test transfer of an e DC with
Speaker 3: my expos Dave de Torre, who's somewhere back over there,
Speaker 3: face forwards inform into sharing power right, and then I
Speaker 3: had the benefit of kind of redoing that at Mark
Speaker 3: and then when I was at Nevardist I left before
Speaker 3: I ex implement did, but we had started implementation of
Speaker 3: medidata rave there. So having done this a few times
Speaker 3: kind of you know, seeing the good, the bad, and
Speaker 3: the ugly. When we formed this team, I think really
Speaker 3: focused on it being more cross functional, really focusing and
Speaker 3: all the different all the different components of the puzzle,
Speaker 3: not just the DC piece. You know, it's very easy
Speaker 3: to get to get distracted by the shiny, glittery lights
Speaker 3: in a new DC when you go to someone and say, hey,
Speaker 3: I wanna I want to standards super set and I
Speaker 3: want to make sure that my my c disc is
Speaker 3: all adequately defined.
Speaker 4: Before I start to study. You know, your executives fall
Speaker 4: asleep before you're done talking.
Speaker 3: They want to hear that it's somehow gonna be you know,
Speaker 3: solved world hunger for clinical trials. But in this particular case,
Speaker 3: we got enough with enough momentum behind it cross functionally
Speaker 3: to really really not focus on the glittery parts, but
Speaker 3: the fundamental platform parts. And no, no data what do
Speaker 3: we call you know, no batch loads? That that is
Speaker 3: live from collection to consumption. It streams, it's live, and
Speaker 3: no one thinks that's important until you run a COVID
Speaker 3: vaccine study with forty six thousand patients and you're waiting
Speaker 3: for ninety cases and you wake up at five in
Speaker 3: the morning, You get your coffee and you go over
Speaker 3: to your desk and you turn it on to see
Speaker 3: what the data status is from the night before before
Speaker 3: you fall asleep.
Speaker 4: It's batch loads, no muss.
Speaker 2: So you put a lot of work into this. I
Speaker 2: should have asked this question earlier, But why what made you?
Speaker 2: What led you to this place where you developed it?
Speaker 3: Well, we had a list of things we really wanted,
Speaker 3: kind of everything from a nice to have to we
Speaker 3: think it's an absolute must have. So for instance, the
Speaker 3: live the live data streaming. Not everyone agreed, but some
Speaker 3: of us thought it was a must have, especially those
Speaker 3: that went through some recent critical experiences, less hops, less
Speaker 3: instances of the data again, things that when you when
Speaker 3: you talk to a senior executive about prioritizing something about
Speaker 3: a tool, it doesn't sound important. At a prior organization
Speaker 3: I worked with that started with the letter M. Someone
Speaker 3: decided to take data down the path to SDTM and
Speaker 3: too adam in parallel paths.
Speaker 4: It was one of the most critical submissions in the
Speaker 4: company's history.
Speaker 3: The FDA came back and gave us a month to
Speaker 3: prove to them that the SDTM and the ATOM were equivalent.
Speaker 3: It was the most horrific, stressful month of my life.
Speaker 3: I would never want it. So now we have these
Speaker 3: kind of caveats, there's no parallel data flows, no this,
Speaker 3: no that, just to keep ourselves out of that kind
Speaker 3: of trouble.
Speaker 4: Uh. And it does. It doesn't mean we don't we
Speaker 4: can't be innovative about it.
Speaker 3: You just can't go will and Lilly taking data down
Speaker 3: one hundred different paths, so wanting to consume it, meaning
Speaker 3: you know, drive all the reporting from the layer that's
Speaker 3: streaming live from the front end. That was an absolute
Speaker 3: prerequisite requirement.
Speaker 2: So Sabina is such a bad experience. Never again, is
Speaker 2: what drove you? Drove you in that direction. So I'm
Speaker 2: guessing that not everything went perfect. So tell us about
Speaker 2: some challenges, you know, what lessons did you learn and
Speaker 2: implementing a major overhaul?
Speaker 3: Yeah, well, I mean, not everything goes perfect when you're
Speaker 3: implementing something completely off the shelf, as it is because of.
Speaker 4: The amount of integration.
Speaker 3: Yeah, and when you're a company, the size of hours,
Speaker 3: the amount of integration is out of this world, even
Speaker 3: down to sending eesa messages to argus they have to
Speaker 3: be a certain you know, the level of specific specificity
Speaker 3: and the amount of specs that the safety team has
Speaker 3: of how they want that data and how they want
Speaker 3: the updates to that data. You wouldn't believe you think
Speaker 3: it's one requirement, It's like one hundred requirements, right. So
Speaker 3: definitely the integration where we thought some things would be
Speaker 3: more straightforward, we're not.
Speaker 4: The the actual getting agreement and alignment.
Speaker 3: We had a great partner in this, but the partner
Speaker 3: you know, was part of the RFPD except all those specifications.
Speaker 3: But a SPECT that's written by a data manager or
Speaker 3: study manager or CRA written by read by an IT
Speaker 3: person doesn't always mean the same thing. So we had
Speaker 3: a lot of back and forth to get on the
Speaker 3: same page of what things meant.
Speaker 4: And then given the scale that.
Speaker 3: We are, the number of acquisitions we've had, we've literally
Speaker 3: had we have studies and everything.
Speaker 4: We have studies in data labs. Actually I think the
Speaker 4: last one just shut down.
Speaker 3: But until recently, we had a study in data apps, Oracle,
Speaker 3: clinical metadata, rave Viva, from acquisition, you know, everything, everything.
Speaker 3: So this particular implementation we are migrating. It's going to
Speaker 3: be the it's this biggest migration in the one hundred
Speaker 3: and twenty studies from Legacy Pfizer and I think thirty
Speaker 3: studies from Legacy, about one hundred and fifty studies are
Speaker 3: going to get migrated. And we don't mean build a
Speaker 3: new study and migrate the content. Lily did that last
Speaker 3: year with about twenty thirty studies. We're actually creating the
Speaker 3: data models in the new system with a migration tool
Speaker 3: and then pulling the data over and getting all the
Speaker 3: study teams on board. One hundred and thirty of them
Speaker 3: with the best week and months for their study to
Speaker 3: basically pause.
Speaker 4: For us to do this. We're almost We're almost there.
Speaker 3: I think the first studies, the first migrated studies, are
Speaker 3: intesting right now. The system went live, the first study
Speaker 3: went live. I think we have our first patient in
Speaker 3: the study. No, not yet, they keep It's what almost
Speaker 3: what the study team felt it was important to accelerated
Speaker 3: from mid September to meet August for the build was
Speaker 3: still waiting for the first patient. At least you're not crying, no, no, no, yeah, yeah, yeah.
Speaker 4: But it's one thing to go live. It's another thing.
Speaker 4: One thing to build a study and then to present
Speaker 4: test data.
Speaker 3: It's another thing to have a site actually starting putting
Speaker 3: patients in it.
Speaker 2: So, I mean it's kind of a wrap up. Why
Speaker 2: are you excited about this?
Speaker 4: What?
Speaker 2: Why should we be excited for you?
Speaker 4: Well, it's new. I mean you're not. You're not.
Speaker 3: You can't go and say I'm leading this space or
Speaker 3: my team is the most advanced in this space. If
Speaker 3: you're just getting another another same off the sheelf tool
Speaker 3: as everybody else. Uh here was an opper and and
Speaker 3: the big boys. There was a time when they were smaller,
Speaker 3: and you could you could influence that.
Speaker 4: Just even with face.
Speaker 3: Forward before they were acquired by a bigger company. But
Speaker 3: I think here we had the opportunity to really sit
Speaker 3: down and say, what's what do the sites want, what
Speaker 3: do the cis want, what do the clinicians want, what
Speaker 3: did that managers want? And found a partner that was
Speaker 3: crazy enough to jump in with us to do it.
Speaker 3: So I'm truly excited that we're going to have the
Speaker 3: most advanced unified EDC data management environment in the industry.
Speaker 3: And I'm sure we'll have bugs as we start to
Speaker 3: execute the first studies, there's no doubt, but everything from
Speaker 3: how it's set up, to how the third party data
Speaker 3: is assimilated, to how that it flows, to reporting and
Speaker 3: to safety is it's optimized across all them instead of
Speaker 3: you know, kind of pick one or two instead of
Speaker 3: all of them.
Speaker 2: So in the last minute and twenty some odd seconds,
Speaker 2: what's next, What's what's the next version or the next
Speaker 2: enhancement or the next innovation.
Speaker 3: Well, we have some we already have planned for. So
Speaker 3: this is version two of red kept Cloud that's going
Speaker 3: in now. Version one was already available to the public
Speaker 3: before we've already got specs for version three, which my
Speaker 3: team wants to you know, screaming that we haven't even
Speaker 3: finished version two, but there's critical things we want to
Speaker 3: have there in version three. And then based another work
Speaker 3: we're doing, whether it's the setup or the signal detection
Speaker 3: leveraging a gentic AI that I think we're talking about
Speaker 3: the tomorrow in another session is very exciting, especially when
Speaker 3: it's live and you can kind of prevent issues from proliferating,
Speaker 3: you know, running running a signal detection for incential monitoring
Speaker 3: and once a quarter which many companies do. Do you
Speaker 3: know in a vaccine study once a quarter roment is
Speaker 3: finished you or even if you even if it's not finished,
Speaker 3: if you have thousands of patients through and you realize
Speaker 3: they're interproperly reporting AEES or whatever the case is. So
Speaker 3: I think think starting with this being live, the third
Speaker 3: part of data being loaded in a romatic way and
Speaker 3: having single detection running on it live, it's got to
Speaker 3: be the it's it's got to be how we have
Speaker 3: to think to be strius.
Speaker 2: It was great talking with you this afternoon. Thank you
Speaker 2: very much.
Speaker 4: Yeah, thank you