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The evolving role of data managers with Kelly Forester
8 Plays11 months ago
The evolving role of data managers with Kelly Forester
Sylvain Berthelot sits down with Kelly Forester, a clinical data management expert with 30 years’ experience. Kelly shares personal stories from her journey in clinical research, including how she transitioned from early roles in data entry to leading global data strategies.
They both reflect on the shifting expectations for data managers, from scripted technical execution to strategic thinking. In an increasingly digital and decentralized clinical trial landscape, they discuss the need for adaptability as ever evolving technologies reshape the industry.
The conversation also dives into career development, mentorship, and the growing importance of soft skills and cross-functional collaboration.
Tune in to discover how data managers are not just keeping pace with change but driving it.
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
00:00:13
Speaker
Welcome to Clinical Data Talks, a podcast brought to you by CRS-Cube.
00:00:19
Speaker
I'm your host, Sylvain Berthelot.
00:00:21
Speaker
Join me and industry experts as we discuss the latest trends that impact the world of clinical data.
Meet Kelly Forrester
00:00:30
Speaker
My guest today is Kelly Forrester from Vita Global Sciences.
00:00:36
Speaker
She's an industry expert, especially in data management, having been in the industry for nearly 30 years.
00:00:45
Speaker
Her experience spans many renowned CROs and she's now overseeing data management at Vita.
00:00:53
Speaker
And if this wasn't enough, she lives on a farm and takes care of many animals in her spare time.
00:01:01
Speaker
Hi Kelly, nice to have you on the podcast.
00:01:06
Speaker
How are you?
00:01:07
Speaker
Thank you for having me Sylvain.
00:01:08
Speaker
I'm good.
00:01:09
Speaker
Nice to see you.
00:01:11
Speaker
Do you prefer data management or looking after animals?
00:01:16
Speaker
You know, I think it's a great balance.
00:01:18
Speaker
I don't think I have a preference.
00:01:20
Speaker
They both energize me and keep me going.
00:01:23
Speaker
Nice, nice.
00:01:25
Speaker
So today together, we're going to talk about the role of data managers.
00:01:30
Speaker
I'm really looking forward to our discussion because you have a lot of experience in that space, and I couldn't think of anyone else to have this discussion with.
Evolution of Data Management
00:01:43
Speaker
So first of all, considering you've been in the industry for nearly 30 years, could you tell us if, in your opinion, the role of data managers has changed over time?
00:01:55
Speaker
It absolutely has.
00:01:57
Speaker
I think that this role is evolving into a smarter role and the things that they asked us to do in the past in the form of any data management position was so methodical.
00:02:10
Speaker
too many pieces and parts to put together.
00:02:12
Speaker
But when I started about 30 years, it was truly a checklist to get through your day and a lot of activity.
00:02:19
Speaker
But today we're using our brains more.
00:02:21
Speaker
We're more independent thinking and we actually offer more to any study or research program.
00:02:30
Speaker
Interesting.
00:02:31
Speaker
I didn't realize that it was more of a checklist before and now it's more involved in a way if you need to think more.
00:02:38
Speaker
That's very interesting.
00:02:40
Speaker
What would you say have been the drivers of those changes over the years?
00:02:47
Speaker
Well, I think it's easy to point to regulations, but I think it's more accurate to point to smarter protocols, clients that expect more of data management,
00:02:58
Speaker
I've always thought of data management as the glue to what keeps research going.
00:03:04
Speaker
So we're the beginning and part of the end story.
00:03:09
Speaker
And so I really think it's the smartness of the protocols, the diseases and the conditions that we're studying.
00:03:18
Speaker
And as we've evolved as humans, so has the need for medicines and over the counter products that make our lives better and easier.
00:03:27
Speaker
Yeah, and you're pointing something very pertinent about it being the beginning and part of the end.
Role of Data Managers in Trials
00:03:38
Speaker
And is there anything in between as well that data managers get involved in?
00:03:43
Speaker
Absolutely.
00:03:44
Speaker
So as the data is collected, we learn more, we know more, and protocols change throughout the life of a study.
00:03:52
Speaker
It's no longer that a protocol is stable.
00:03:55
Speaker
and ends up the same beast that it began.
00:03:58
Speaker
And so we are part of that fact-finding mission, as well as to see what the data shows us.
00:04:05
Speaker
Where is it leading us and what is it really indicating?
00:04:09
Speaker
So all protocols start with primary and secondary endpoints, and there's a rationale for the disease research or the condition.
00:04:20
Speaker
Um, but as you learn, we do better.
00:04:23
Speaker
We're able to tailor our protocols and databases and then the message of the data, I think in a much more structured way, but you don't always know the findings in the beginning.
00:04:34
Speaker
So it's quite interesting to be a part of that.
00:04:36
Speaker
So a lot in the middle, but a lot in the beginning that just gets us started off the starting block.
00:04:42
Speaker
So it's a quite exciting, I think job.
00:04:47
Speaker
I don't look at it as a job, it's a passion.
00:04:49
Speaker
And for those of us that have been in the industry for a long time, that passion has to make sense.
00:04:55
Speaker
Yes, and that's what I love about our industry, the fact that a lot of people actually are here too because they feel like they're making a difference.
00:05:04
Speaker
And that passion, you find it in so many people.
00:05:07
Speaker
I love that.
00:05:09
Speaker
So where do you think data managers really add value then in clinical trials considering that they're throughout the trial execution?
00:05:21
Speaker
Well, to elaborate on fact finding, so I think where we really shine is that we're able to help make a message, help tailor and understand what the data is showing us.
00:05:36
Speaker
If we're smart enough, we're catching the key indicators.
00:05:40
Speaker
We're looking at where the data is taking us.
00:05:43
Speaker
We're looking at the adverse effects and we're keeping the rest of the team informed.
00:05:48
Speaker
So
00:05:48
Speaker
That's where I think that the role has evolved so well is that those of us that are still here and that really have passion for it, we're able to help read that data.
00:05:59
Speaker
And we're certainly not physicians and we certainly don't bring to the table the expertise needed to interpret that data to ensure that the protocols are written correctly and so that the medicines work.
00:06:12
Speaker
But we are able to point out hard facts
00:06:16
Speaker
as well as nuances in the data that others don't see on a day-to-day basis.
00:06:23
Speaker
And I think you're alluding to teamwork, essentially.
00:06:27
Speaker
Absolutely.
00:06:29
Speaker
How data managers interact with a lot of different actors of the clinical trials within the clinical team.
Collaboration with Biostatisticians
00:06:40
Speaker
There's one role because you mentioned like we don't know how to interpret the data in a way or we don't know the stats behind that.
00:06:50
Speaker
And there's the big role of biostatistics.
00:06:53
Speaker
Do you see the right level of collaboration between data managers and biostat?
00:07:00
Speaker
I actually do.
00:07:01
Speaker
I think there's a whole lot more planning and proactive nature to that collaboration.
00:07:08
Speaker
It's a marriage, if you will.
00:07:10
Speaker
And without one, the other one doesn't really make sense.
00:07:13
Speaker
And so to plan a study, you have to have that collaboration, that teamwork at step one.
00:07:20
Speaker
And step one is finishing or adopting or even adding to a protocol, adding our expertise.
00:07:27
Speaker
If we've been in an indication for a long time, able to talk about the things that we've seen in the past or to watch out for the things that we have tripped on in the past.
00:07:37
Speaker
So that collaboration is key.
00:07:39
Speaker
It's absolutely necessary.
00:07:42
Speaker
We're not a standalone department and without biostatistics and even the help of all of the other ancillary departments, data management doesn't make sense.
00:07:51
Speaker
You can gather the data, but what do you do with it at that point?
00:07:55
Speaker
And so it is a very large team effort.
00:07:59
Speaker
Yes, I assume you need to understand as well the end goal in order to start on the right foot.
00:08:08
Speaker
So do you see still on the collaborations you work with a lot of different sponsors, do you see that it's easy to collaborate with trial teams
00:08:23
Speaker
within all the sponsors you work with?
00:08:26
Speaker
Or is it something that do you have to sell the role of data managers in a way?
00:08:31
Speaker
I haven't felt like we've had to sell the role of data management in the last 10 years.
00:08:37
Speaker
I think that the criticalness of the role, the importance of the department, the teamwork that we can bring to any study team, I think that those have been seen and that the value has really risen.
00:08:52
Speaker
as again, we've become smarter data managers.
00:08:57
Speaker
I really don't feel like there's a selling point any longer.
00:09:01
Speaker
And I feel like teams and clients especially are eager for that input and that feedback.
00:09:08
Speaker
Yeah, that's good.
00:09:10
Speaker
I heard when was it was SCDM in Boston last year, someone say, well, I think that was a big theme actually of SCDM, like data managers finally have a seat at the table.
00:09:25
Speaker
So do you agree with that?
00:09:27
Speaker
Absolutely.
00:09:28
Speaker
Absolutely.
00:09:29
Speaker
And I think that the seat again before 30 years ago when I started, that seat was very defined.
00:09:36
Speaker
We knew what our role was.
00:09:38
Speaker
which lends itself to the checklist as long as you could get through your day and get the things done that needed to be done you were doing well and now we have a seat at the table and the conversation is open so we do add expertise we do add experience and i think clients are excited to get that input it's not always incorporated because again we are not physicians we are not the scientists developing the drugs and so we rely on them for that expertise
00:10:07
Speaker
but the conversations are much broader.
00:10:12
Speaker
Yeah.
00:10:12
Speaker
Well, that's good.
00:10:15
Speaker
So, I mean, technology, I love technology as you would expect.
Technology's Impact on Data Management
00:10:22
Speaker
And I'm a bit worried about what you're going to answer to this question.
00:10:28
Speaker
Do you think technology fulfills what data managers need from it at the moment?
00:10:37
Speaker
You know, I think the story is still evolving.
00:10:41
Speaker
I think that there are some tools that are incredibly necessary for us to do our jobs that also help in the ease of doing our jobs.
00:10:50
Speaker
I know your passion being AI.
00:10:53
Speaker
There's a lot of unknown territory out there.
00:10:57
Speaker
So many people are talking about the tools of AI and yet there are no
00:11:04
Speaker
thought leaders, I think, that are stepping forward and taking the risk just yet, or that's what I have seen.
00:11:11
Speaker
I do think that there are some tools that we have come to lean on, but those tools evolve as the needs of the studies and the patients evolve, so do the tools.
00:11:24
Speaker
And so that's really exciting to me just because we're never going to get stagnant.
00:11:31
Speaker
We don't have to think we know our jobs inside and out.
00:11:34
Speaker
And that's what keeps, I think, the field exciting for me is that it's constantly evolving.
00:11:40
Speaker
And it is exciting to be a part of that.
00:11:41
Speaker
It's exciting to see a drug hit the market and to know that you had even a small piece of that.
00:11:48
Speaker
And the tools help make that possible.
00:11:50
Speaker
I think clients want to go faster and cheaper and better all the time.
00:11:57
Speaker
And that's where tools really do help.
00:12:00
Speaker
They are cost cutters.
00:12:01
Speaker
They can do things in a much more methodical way that takes us a lot longer.
00:12:07
Speaker
And so in essence, it can make our jobs easier.
00:12:11
Speaker
I've enjoyed learning about the tools, but they're still evolving.
00:12:14
Speaker
They're still coming.
00:12:15
Speaker
And that is exciting.
00:12:18
Speaker
Yeah, yeah, I agree to some extent in a way, actually, because I fully agree with you that tools are evolving.
00:12:26
Speaker
It's great because, I mean, our industry keeps evolving from regulation requirements, from trying to be more efficient, reducing timelines and so on.
00:12:39
Speaker
At the same time, we still use tools that don't seem to have evolved that much since they started.
00:12:48
Speaker
So for example, EDC does a specific task and the task in itself is not necessarily evolving that much.
00:12:59
Speaker
And I'm wondering if the tools
00:13:03
Speaker
are evolving enough actually to carry on or to support not only data managers, but sites, for example, in the data capture.
00:13:13
Speaker
What do you think about that?
00:13:15
Speaker
I think you're right in that the EDC platform's function has been caught.
00:13:22
Speaker
And I wouldn't say that it's stagnated because I think that different tools, different companies,
00:13:29
Speaker
are adding different collaborations to their EDC systems.
00:13:32
Speaker
It's a build upon, if you will.
00:13:35
Speaker
And so again, it's nice to see those companies that are investing in those tools that are investing, I think, in that way of discovery and nobody's reinventing the wheel.
00:13:51
Speaker
I think that there are some systems, as you speak of EDC, that are easier to use than others.
00:13:57
Speaker
There are some that have limitations and it's about learning those tools and what a client's preference might be.
00:14:04
Speaker
I can see your point that it may not have evolved enough.
00:14:09
Speaker
Let's just say over the last three decades, but I can think about products that we were using 30 years ago that would never cut it in the market today.
00:14:18
Speaker
There are EDC systems that are dead, that are no longer being used.
00:14:23
Speaker
They did not serve the end user at the site well.
00:14:27
Speaker
And it was hard to get the data in such a format that we could do adequate cleaning.
00:14:33
Speaker
And so I think that, again, there are some tools that have evolved that have supported the needs.
00:14:39
Speaker
But that's part of the excitement is watching the different tools rolling out.
00:14:43
Speaker
So the basic function is still there.
00:14:47
Speaker
But I do think that there are some companies that have actually invested in ancillary tools or improving the nature of their platform.
00:14:55
Speaker
And that's exciting to see.
00:14:59
Speaker
Yes, and hopefully we're one of them.
00:15:03
Speaker
Well, without being product placement, I would say yes, you are.
00:15:07
Speaker
I've been very impressed with CRS Cube system.
00:15:12
Speaker
Thank you.
00:15:12
Speaker
I appreciate that.
00:15:13
Speaker
That's what we do for somebody that's been in the industry for 30 years, to impress with an EDC system.
00:15:19
Speaker
So that's been a lot of fun to learn about your technology.
00:15:23
Speaker
Well, I really appreciate that.
00:15:25
Speaker
Thank you.
00:15:26
Speaker
You mentioned AI.
Exploring AI in Data Management
00:15:28
Speaker
I have to ask, do you have any, not expectations necessarily, but wish of what you'd like AI to do for data managers?
00:15:43
Speaker
That's a really good question.
00:15:44
Speaker
And I think that as I attend some of the industry conferences and actually see some of the tools that are out there, I think that there are some ideas about AI that are really forward thinking.
00:15:59
Speaker
You mentioned SCDM and I sat in on a conversation about a car whose wheel would get all of your vitals.
00:16:08
Speaker
And if something should happen to you in your your your driving,
00:16:13
Speaker
you know, a message would be sent somehow directly to your doctor.
00:16:16
Speaker
And the, the scenario was very elaborate all the way to the way of treatment and getting help on the side of the road.
00:16:24
Speaker
But then at the very end of the conversation, the gentleman asked how many of you would show of hands would actually buy a vehicle like this and nobody raised their hand.
00:16:35
Speaker
So,
00:16:37
Speaker
What I would like to see is as the industry is changing and things are being developed and even discussed, um, where are those risk takers?
00:16:46
Speaker
I've already talked about, there's not a lot of companies out there just grabbing a hold of all of this new technology.
00:16:53
Speaker
I think we have a more deliberate action.
00:16:57
Speaker
Wait and see, let's see if it really does fit the need and let's see if there's a market for it.
00:17:02
Speaker
So,
00:17:04
Speaker
As fast as we can develop the tools, it doesn't mean they'll be used and it doesn't mean they're always helpful.
00:17:09
Speaker
So I would like to be a part of that, which is why I'm still in data management after all of these years.
00:17:17
Speaker
Yeah.
00:17:17
Speaker
And to be fair, I think I fully agree with you that it's a bit of a wait and see situation.
00:17:28
Speaker
And I have heard companies really looking into AI, especially around data, because we're requiring more data and it's getting more difficult to be able to analyze all of these data in a way, or to know where to look, especially with wearables and so on.
00:17:55
Speaker
However, I don't see necessarily those companies doing it with an end goal in mind.
00:18:06
Speaker
And I feel like sometimes, because if you have a lot of data, it's very easy to do data mining.
00:18:13
Speaker
But if you don't know what you're going to mine the data for, then it's pointless.
00:18:21
Speaker
So I'm wondering if we really have
00:18:25
Speaker
enough of an end goal to achieve with AI for the big AI stuff to be really impactful.
00:18:40
Speaker
On the other hand, I see AI being used on very localized, practical aspects of clinical trials
00:18:52
Speaker
And I feel like currently, at least, it has more of an impact because it can bring faster efficiencies or data quality.
00:19:04
Speaker
So, yeah, I'm really interested.
00:19:06
Speaker
Like, as you said, that's something that I love.
00:19:11
Speaker
Not necessarily because I want us to use AI.
00:19:14
Speaker
I think AI needs to have a means.
00:19:19
Speaker
But at the same time, I think it can be transformational for the industry.
00:19:25
Speaker
It can be.
00:19:25
Speaker
And just because we can develop it doesn't mean we'll use it.
00:19:28
Speaker
So I like your thought that an end goal has to be in mind.
00:19:32
Speaker
And I think that's what AI is right now.
00:19:34
Speaker
It's an open frontier without a lot of end goal.
00:19:38
Speaker
So just because we can develop it and we can use it, what are we using it for?
00:19:44
Speaker
And at what cost?
00:19:45
Speaker
Yeah.
Importance of Teamwork and Mentoring
00:19:48
Speaker
So going back to the role of data managers and linking with the amount of data that we capture now, which is much broader than what we did probably 20, 25 years ago.
00:20:06
Speaker
How do you manage that as a data manager?
00:20:09
Speaker
Well, such is the nature of research, right?
00:20:13
Speaker
There's the must haves and then there's the exploratory part of collecting data.
00:20:19
Speaker
And I think that it's, it goes back to teamwork.
00:20:23
Speaker
It goes back to the conversations that you have during the beginning of the study.
00:20:27
Speaker
And as the study evolves, what is it that's critical?
00:20:31
Speaker
What is it that is not a Christmas wish list, if you will?
00:20:34
Speaker
What is it that we must be looking at?
00:20:37
Speaker
And then the rest of the data,
00:20:39
Speaker
How can we turn that into a story?
00:20:41
Speaker
How can it be part of the current story?
00:20:43
Speaker
And is it needed?
00:20:45
Speaker
And I think that is the smart thinking in designing protocols, but in designing databases that reflect protocols.
00:20:53
Speaker
Unless it's an exploratory study, you have a very specific end goal in mind.
00:20:58
Speaker
A lot of data may be captured, but not a lot of data may be needed.
00:21:03
Speaker
There may be some pieces of that data that
00:21:06
Speaker
is tucked away for future user conversation, should it be needed?
00:21:11
Speaker
And that's what we see, I think, when the amount of data is so large, not in the numbers and population, but the amount of data being collected on one subject.
00:21:22
Speaker
Is it needed?
00:21:24
Speaker
And I think that that's where clients are really getting involved for the faster, the more cost effective approach to developing databases.
00:21:33
Speaker
And then if time and money allow, yep, sure, it's great to collect the rest or the additional data, but what will be done with it?
00:21:41
Speaker
So it's about the conversation.
00:21:44
Speaker
It's about setting the goals in the beginning.
00:21:46
Speaker
And I think that those have to be adhered to.
00:21:49
Speaker
Yeah, yeah, a very good point.
00:21:52
Speaker
And we're going back to the collaboration question.
00:21:55
Speaker
Yes.
00:21:57
Speaker
Like, we're not going in circles, but it all ties in very nicely.
00:22:02
Speaker
I have one last question for you.
00:22:04
Speaker
Sure.
00:22:05
Speaker
What's the best piece of advice you've received that you consistently apply in your work?
00:22:13
Speaker
That's an easy one.
00:22:16
Speaker
It's not enough to do a good job anymore.
00:22:20
Speaker
I think that what we don't know and what is still evolving in the industry, it's important for those of us that are in this for the passion to pass on that baton to the ones below us, the ones coming up in the industry.
00:22:35
Speaker
I won't be doing this forever as long as I possibly can, but I think it's important to always invest in those that support us.
00:22:45
Speaker
I'm only as good at my job as the team below me or as the team that I help to lead.
00:22:51
Speaker
So I think the best piece of advice is not pay it forward, but turn around and look who's behind you and to pass that baton for the things that we don't know, because they'll be the ones that will help define AI.
00:23:05
Speaker
And hopefully clinical research continues to grow in intelligence.
00:23:12
Speaker
Yeah, that's very nice.
00:23:14
Speaker
And again, I completely agree with you.
00:23:17
Speaker
We need to pave the path for the next generation or even not a generation below, but then the next people who will take over and who we are likely to depend on for new treatments actually as well.
00:23:34
Speaker
Absolutely.
00:23:37
Speaker
Wow.
Closing Remarks
00:23:39
Speaker
Wonderful, Kelly.
00:23:40
Speaker
Thank you so much for your time.
00:23:41
Speaker
It's been amazing chatting to you.
00:23:45
Speaker
I'm sure I'll see you soon at an industry event.
00:23:50
Speaker
Yes, you will.
00:23:51
Speaker
And thank you for including me in this.
00:23:52
Speaker
Congratulations to you.
00:23:54
Speaker
Oh, thank you.
00:23:55
Speaker
My pleasure.
00:23:56
Speaker
And thanks all for listening or watching us.
00:24:00
Speaker
You can visit our website to find more clinical data talks episodes.