Beth Bauer

Beth Bauer

[00:00:00]

Don Davis PhD, MBA: Hello, and welcome to life science success podcast. for those who of you who don't know me? My name is, uh, Don Davis and I'm a consultant in life sciences. I help company manage complexity and increase performance today.

I'm joined by Beth Bower. Welcome Beth. Can you tell a listener just a little bit about yourself?

Beth Bauer: Yes. Excellent. So Don, thank you so much for inviting me today. This is my first podcast, so I'm a little nervous I have to admit. Um, probably the most important thing for everyone to understand [00:01:00] is how much I love data and analytics.

Um, I've been involved in some form of data analytics actually since, uh, my teenage years. Um, what a lot of people don't know is that I, um, originally went to school to be a psychiatrist. And discovered that, um, the tools and techniques that were available at the time that I was in college were, were not really, uh, it for me.

So I ended up, uh, playing my hand in a combination of psychology, journalism and statistics, and everyone asked me at the time. So, so what would you do with that? And I thought, I, I don't know, but these are all the things I love. and it turns out that the answer is pretty much anything you want to, right.

Because you need to be able to understand science and data is at the heart of all science. You then need to be able to communicate that. And that's where the being able to, uh, articulate, uh, both the needs and the, um, answers that you receive and, and the solution sets is [00:02:00] critically important. And then.

The change management, the psychology of all of that is really the three of them fit together so nicely that it's, uh, it's made up for a very interesting career path. And, um, also really involved in civic, a lot of civic involvement, which leads to my interests in, um, reducing disparities in health and wealth.

Don Davis PhD, MBA: Well that's, I mean, it's really interesting cuz you could almost, you could almost say even for somebody that was in, you know, psychology, that you could use all of that data and analytics, um, background of yours and like empirical research or, you know, who knows, I mean, you could, you could spend your life sort of diving into surveys and data with, uh, with human being.

Beth Bauer: Well, and in fact, in the data space, there's a lot of psychologists actually who've ended up in the data space, uh, and a lot of writers too. Right. Because being able to understand both sides in, in that communication is really a, a key, right. Um, if you're not able to [00:03:00] communicate it, um, people aren't going to understand, and they're not going to be able to move forward with it.

Yeah,

Don Davis PhD, MBA: for sure. And so right now you're the leader of a company. Can you tell us just a little bit about your company and like, what is it that you're, that you're doing, uh, currently in life sciences?

Beth Bauer: Well, if you don't mind, I'm gonna walk you a little bit through the, the journey to, to get me here. Sure.

That would, that would be great. Yeah, that'd be great. Yeah. So, um, I've been really blessed, uh, but the 30 years that I've been working in life sciences data, um, it's to a degree going full circle. Right. I began, um, actually at the Robert Wood Johnson foundation in a, in a space where there was not a full understanding of any.

Of a data national data that could actually describe the health status of the United States. And so my job was to go find all of these national health surveys, et cetera. And then we wrote a [00:04:00] book that actually articulated the, the state of, of the, of the union, if you will, with regard to health. And there was a lot that needed to be worked on.

So from there, I ended up going to, IQVIA at a time when it was still called IMS and, um, It was actually at the predoc level data in the healthcare space. And so that meant we had to actually do the statistics and, and, uh, pull the aggregations together and build the systems that actually would become the, the doctor driven data and plan data of the nineties that, that drove all the sales teams.

Then once you create that data, Now you have a massive data. What do you do with all that data? How do you actually transform that data into the insights that create targeting segmentation, market sizing, all of these things. And so being able to create the consulting services and working directly with, uh, big pharma, essentially all of the big pharma customers, uh, clients and, and companies [00:05:00] were my customers, um, solving their problems with this new data asset.

Um, At that time was actually when, um, my mom got really sick with cancer. And so, uh, I actually started my company back then, uh, pulled myself out of the workforce and, um, but realized I, I couldn't sit still. So , I ended up doing consulting services again for, for large pharma and really getting into you, the pharma side of solving all of these problems that was phenomenal for about, for about 10 years.

But then I got called back to, uh, what was still not yet. IQVIA. Um, but it became, IQVIA while I was there to actually work in the pharmacy space, being able to actually create the KPIs and market awareness for. Pharmacies to look holistically at patients and how they could improve, um, how they were serving those patients and potentially advance the [00:06:00] services that they provide with regard to overall healthcare.

This is, you know, some of the beginnings of, uh, clinics within pharmacies and medication adherence had just come with the affordable care act. So being able to identify, so what. Benefit does something like medication adherence bring to, uh, the, the patients and all of the different stakeholders in those ecosystems.

Right. So you have the patient at the center, you have the payers, you have the pharmacies, you have the providers in a medication adherence scenario. It, it, it turns out. Surprise that when patients take their medications, they actually get better. And their disease states do not, um, continue to, uh, devolve.

And so it's a win-win for really everyone. And then recognizing that the technologies that were, um, coming at this is about five years ago that would allow, allow us to begin to create, um, data lakes. Right. And then. We started to create something there [00:07:00] that I called data ponds, which didn't really stick , but in today's world, I would say that that's really the curation layer that people are talking about.

Ponds is really that concept of being able to curate the information reduced from this large amount of data, uh, it, down into the insights that matter, or an articulation of the components of insights that allow people to continue to. While ensuring that they have the right quality and fit for purpose in the data that they're working with.

So that led me to, wow, there's so much more data becoming available within healthcare. Let me go back into pharma and begin to solve some of those problems. Let's go full data strategy to be able to, um, identify all the different use cases and create the foundational systems that would allow them to, um, not only articulate what the needs are.

And prioritize those, but begin to actually, [00:08:00] um, answer those questions and work with vendors and partners, to be able to, um, manipulate, transform their data into actually solutions to the, to the problems, not just for the company, but for their customers and for each of those stakeholders in the various ecosystems.

So. What I learned through that whole experience is I've, I've kind of come 360 back into the, the Robert Wood Johnson public health concept that ties into the, the capitalist part of that too, which is we really can create win, win, win scenarios where. The patient is going to win first and we're going to be really looking at improved outcomes.

And then how can we capitalize on all of this data that's now available and these great technologies that allow us to actually, um, very quickly hold those insights. As long as you can articulate the use cases, all those insights into a space where you can have a distributed [00:09:00] system that people can use it.

So now I'm, uh, With PosiROI I'm, I'm hoping that we can begin to create a, a mechanism through which everyone wins. We can create that shared value. And so I'm primarily working with a combination of, um, both, uh, pharma, biotech customers. On their solution sets since that's where I grew up. But also with the innovators, there are so many innovators out there who are working with new sets of data, new devices, molecular diagnostics, et cetera, being able to help them to actually take this, this new technology and information that they've gathered and articulate that into the, the shared value, being able to be the bridge of how do you, how do you actually implement on this?

Don Davis PhD, MBA: And so with that, whether it's a, whether it's a, a individual group that's working on say established, [00:10:00] uh, you know, drugs and data, or, um, somebody that's brand new in the space, are there two different starting points that you normally start working with companies at PosiROI?

Beth Bauer: Well, there are many starting points, but they all actually begin with the, the basic question of what is the need of the company and its customers versus what they say they want.

Right? It's those business questions and need questions that really need, um, a deep dive, um, and a lot of transparency because, uh, here's some simple examples. Um, Sometimes people come and say, I need, uh, patient data by age and gender. Well, at the end, they don't actually need patient data, uh, by age and gender, what they need is to understand which customers and, uh, which how many, and which customers are they able to get at within those spaces.

And then how did they gain access to them? [00:11:00] Is that through the payers? Is that through providers or in today's world? Is that through some other mechanism, uh, in the digital. Space, right. Is that actually through, um, rare disease websites or is that through, um, some other social group and then understanding also the communities and ecosystems in which those customers are playing because they're influenced by the ecosystems in which they play.

So being able to understand that. You don't just ask the simple questions anymore. That's what all this data means is that we have the ability to actually, um, better articulate where we actually have that opportunity space and the transparency to show, to be able to take what the needs are versus what you can deliver and put those together in a way that customers want to engage with you and to enable the internal systems that actually will allow that to happen.

Don Davis PhD, MBA: Seems like, you're also able to shape that landing point. You know, if you, if you could envision where you wanna land, um, [00:12:00] maybe then you could backwards describe what, what data then would you start with to get to the end point that you're looking for?

Beth Bauer: That's absolutely true. And one of the major, um, issues of the day is there's so much data available and many people who are working in it have not been working in that deep of data for a very long time.

And so. even some of the innovators who are working with the data, don't realize some of the biases that are created by how they're capturing the data. So being able to clearly articulate what you can and cannot infer based off of how the data is captured. The business rules that get applied, the cohorts that are created, um, are critically important to that data as being able to be trusted.

Um, and, um, to be, uh, expanded upon and utilized for, um, the different use cases. Now it may be that a, [00:13:00] a particular data set is good for multiple use cases, but it needs to have different manipulations done to it in order for it to actually be answering appropriately, or it may need to have, uh, project actions done to it or.

Some other type of statistical or, uh, AI technique to, to actually make it usable. And so being able to, um, really walk through from beginning to end all the things that need to happen to the data, holding, holding the hands, uh, through that process is really, um, the, the value point that we're bringing.

Don Davis PhD, MBA: And so what does a typical customer look like that comes to PosiROI?

Beth Bauer: So it's really twofold, right? One, uh, there there's two sets of customers that I'm largely dealing with today. And then there's a third that I aspire to . So, um, the, the, the two sets of customers, PRI primarily are. Uh, customer set. One is pharma and biotech companies [00:14:00] that are looking to leverage data in the digital transformation.

Um, there's data everywhere. There's a lot of spend on data. Um, and unfortunately, um, across the board, um, There's a lot of spend without building the additional again, curation and then enablement processes. Um, and so I really look at that as something I call adept, being able to understand the analytics and acuity, the data and decisions, the enablement, and engagement, the people and process and the technology and those components.

Are are what I'm bringing to articulate to pharma and biotech companies that allow them to understand you, you don't just buy data off the shelf and then magic happens, right? Mm-hmm you actually need to build in all of these components. And it used to be just people process technology. And now it's not just people process technology, right?

It's analytics. [00:15:00] Data and enablement that allow the people process and technology to make that happen. So that's customer set. One customer set. Two is really working with these innovators, whether they be, um, uh, largely either, um, molecular diagnostics or technology companies, or even, um, uh, companies who are aggregating, uh, different data assets, um, within.

New spaces, right? Not the traditional spaces. And everyone has put together this data. I have this cool data. Everybody said they needed this data. Um, but it's not just the data. Right. It's how do you then transform that from being a flood of data, into the knowledge and insights that are actually needed, um, to, to, to, for the product to sell itself?

Right? How can you actually, um, Create the mechanisms and the, uh, shared value articulation through KPIs, [00:16:00] leading indicators and an awareness that that's going to evolve over time. Right? Continuous innovation is the name of the game. And so if you want your. Products and services to continue to, uh, be utilized.

That means that you're going to, uh, have to recognize that your products and services are going to have to change over time. Once you've met a need, you're going to have to then evolve and continue to meet new new needs.

Don Davis PhD, MBA: Yeah, it's I I've worked with a couple of small, um, excuse me, midsize and large size pharma companies on implementing a, uh, data catalog.

Right. And so, you know, they're really thinking forward about the structure, their data, the way that they tag it and the cataloging and things like that. But whenever I think about, you know, the smaller guys mean, they just don't know yet. Um, kind of the value that this data's going to have, you know, as they, as they move forward as well.

And so having somebody like yourself would be even more valuable, I would say, you know, at, [00:17:00] at that end, um, and you know, with your history and with your knowledge with I Q V a and the organizations that you've worked with, I mean, it seems like it would be a pretty powerful combination even for the mid and large size companies, uh, as well in their journey with data and analytics.

Beth Bauer: Well, exactly. And, and I'm a very tiny company so I'm not trying to compete with some of the large consulting companies that are, you know, implementing the, the full program. What I am offering, however, is that expertise to tell you where the gotchas are is what you've just said, that you're going to create actually going to work.

What, what do you need to add to it? What do you need to take away? How do you need to change your expectations of what will occur from this? And even when we talk about, say a data catalog, um, which is near and dear to my heart, something I was just recently working on significantly. And, uh, what ended up coming from that is you, you need to not only take into account the [00:18:00] cataloging of the information.

But also the metadata that's associated with it. Right? Mm-hmm that all of the, um, regulatory information, the governance that goes along with that. And so, and, and I know governance sometimes is a bad word. Uh, it's both a good word and a, and a bad word. And, um, one of the terms that we were using is compliant by.

Right that you need to be thinking through how all this data will be accessed and utilized and for what use cases. And again, ensuring that the quality is available. Some, some articulation of the quality relative to the fit for purpose is a key component to being able to have a, a, a really strong data catalog, because Joe us saying there's data out there isn't enough.

Don Davis PhD, MBA: Yeah. And it seems like, I mean, the, there are plenty of tools to the help to enable that a little bit with, with, you know, some governance, but you're absolutely right. I mean, it seems to me like having a, [00:19:00] having a bigger or better structure and thinking ahead of where, where am I gonna need this data next?

And how do I best find it? Where do I, where do I sort of pay it through the bread crumbs to get back to what I, what it is that I need. um, you know, in the end seems like, uh, an important thing for anybody that's, that's having to, you know, store data long term that they might actually need access to power other things later for

Beth Bauer: well, and then thinking through from a data strategy perspective, right.

What curation is needed that actually creates additional data sets that may have bridged some of those individual assets together in a way that makes it perfect for the task at hand, so that people don't need to every time go back to raw data, certainly data scientists need the ability to do that. Um, but.

Everyone does not need to go back to raw data. Right? So if we can think through the different use cases and create the [00:20:00] curation level that is necessary, and then put that in your data catalog, you can solve some of your compliance and, and governance issues. Uh, more simply by actually creating a, a asset that is, um, more useful for the specific application, more fit for purpose.

Don Davis PhD, MBA: So ultimately true. So one of the things that you also mentioned is, is this idea of creating kind of a 360 degree healthcare customer value story. Where does that sort of start and stop? And what does one look like?

Beth Bauer: Well, this is related to the ecosystem concepts, right? That with the patient at the center, really there's all of the different touch points that the, the, uh, patient is having to deal with potentially.

So that's the provider. The IDNs and systems that the provider plays in potentially multiple provider systems that need to talk to each other, the insurers and, and payers, [00:21:00] and potentially even the employers that are, um, engaging with those payers and then the policies, whether those be states or federal policies, all of these components actually play into, uh, a patient's ability to gain awareness and access to the appropriate treatments for their diseases.

Right. So if we can begin to articulate, um, the different ways that these ecosystems talk to each other and influence each other, we can begin to better understand how we can actually begin to affect change. Because again, the. Um, and, and quite frankly, one additional component I failed to mention is the communities in which these people live and, and, and who they socialize with because the communities can actually create a whole other set of influences you can have in the precision medicine concept, you can have two patients who have a com an identical profile from a, um, a chemical [00:22:00] and molecular standpoint.

Um, however, they may have. Different plans. They live in, may live in different geographies and they may have different beliefs so that even if they had the same access and, and affluence and genetic profile based on their beliefs, they may choose different treatments. Right. So when we start to think about precision medicine, we need to find ways that we can allow each of these.

To weigh into that influence. And we're not gonna have the answer to everything tomorrow. Right. Um, but we have to get started if we're going to get there. And then we need to create mechanisms through which we can share this information so that we can actually expedite and accelerate our understanding of, of how, uh, treatments are utilized in, in the real world.

Don Davis PhD, MBA: Yeah. AB I mean, absolutely critical. I mean, having that ability to. Fully understand the different aspects that could potentially, you know, influence a product that you're trying to get to market. And, you know, really being able to work through [00:23:00] that from a patient perspective all the way back. Um, but also considering, you know, the different aspects of, of impact that might be had as well, uh, is, you know, does sign absolutely critical,

What sort of pitfalls does your company help client to avoid?

Beth Bauer: Uh, so really it's getting at that, um, understanding of fit for purpose with so much data, uh, that's available today. Um, not, not everyone realizes that the. Clearly there's marketing, that's being given to everyone. Right? I have this great new data. Look, look at all the cool things that I can find with it.

Um, but being able to understand that just because there is data doesn't mean it's the right data. And so how can you begin to actually potentially work with some of these innovators? Cause it needs to be a calm conversation, right? Between what the needs are of the marketplace and the innovators, because they [00:24:00] want to meet those needs.

right. And so how can we actually articulate the problem sets better the use cases so that the innovators can actually meet those needs. And then how can we help the, um, the innovators to actually, um, begin to think differently using their data so that they've. Ensure that those biases are addressed or that they're willing to actually share where they are best at answering certain questions and where they're lacking, where they're not going to be able to.

Right. And how we potentially need to be able to, um, connect all of this data and insights together. And, uh, again, the pitfall that you don't buy data and magic happens, there's going to need to be work underneath. And, um, it, it certainly there's going to need to be, um, Enablement of processes that didn't exist before, because anytime that you're innovating with data, that means that there's a whole slew of operational things that need to happen.

Whether that's, [00:25:00] you know, data ingestion quality, or actually on the, um, the user end, being able to change the processes that they've been utilized in the past, so that they're able to trust this new data and ensure that, um, they understand that it may not be perfect. As you as you begin, but it certainly should be better than where you started.

And then that's where test learn evolve comes in. Right. Um, because you learn from your mistakes and then make it better with every move and ask your vendors to make it better with every move, right? Yeah.

Don Davis PhD, MBA: Yeah. I mean, I, the, the one sort of example that I see going on right now, um, is that, um, you're seeing an awful lot of next generation sequencing, uh, diagnostics, you know, get rolled out.

As a matter of fact, there was, you know, just a, another announcement that I saw today on Reuters that, you know, again, you kind of see these tests growing and growing. Um, however, [00:26:00] The only areas that they seem to be catered to is people that can self pay. So they're not working with, you know, Payers, uh, as of yet.

And, um, they're also, you know, kind of going down this route further and further where it's, you know, we can bring you more and more data. Um, but again, I, I just kind of wonder for what purpose, you know, at the end of the day, what is the, unless you have a target, you know, mal or a target drug or, or some, some actual reason that.

Providing you, the data actually will help with, um, I, I don't really understand the purpose and that's why, you know, I keep sort of looking at these and going, okay, so there's another test that's rolled out, but I don't see a, you know, another series of, you know, sort of personalized medicines right behind it that are targeting, uh, the use of that test, which is, you just seems strange to me.

Well,

Beth Bauer: This is near and dear to my heart, because I just [00:27:00] had a situation where some of those little interesting, unheard of diagnostics actually help me with a situation of my own that I won't get too deep into. But, um, More information and insights is critical, critically important. So there is so much we don't know about, you know, our DNA and, and the moleculars, et cetera.

And even some of the basic blood tests that we currently have today that we don't fully utilize. So all of this. Information and data is not fully leveraged. What that also means is we are spitting out huge amounts of, of flooding of data. When really what people need to understand is that insights and those insights against declaration layers, right?

Those insights are aren't actually going to be the, the golden ticket either. Right, because all of it is a combination of risks, uh, for the decision making [00:28:00] and rewards. Can you still hear me? I pause for just a second. Yes. Yes. I can hear you fine. Mm-hmm um, and so risk versus rewards. And so I think about some of the new technologies that are currently available that are leveraging.

Kind of the social media aspects that allow us to be able to understand who buys into some of these tests. What, what does this suggest could be the, uh, cause or, um, the direction of treatment for a particular patient based on this combination of gen of genetics and tests. But that means we also need to be able to get access to many more people.

Right? So there are situations where academic medical centers for example, are, are really leading the way in some of that testing, because they're aware of the, the influence that some of these, um, tests can bring, but because the, the, um, Group of white coats who make the decision about what should be treatment guidelines haven't necessarily come to a, a full [00:29:00] alignment on what the treatment protocols should be.

Some of this information sits around for decades, um, without being fully leveraged. And if we can begin to actually evolve those apps to a way that, um, we can create trust potentially through real world evidence and, and that we can see that certain treatment plans. Potentially are more effective by just looking at what's happened in the real world.

And then using that to expedite and accelerate the decision making about those, those protocols that that's really the Nirvana of where we wanna get to. And ideally it would be a state where. Patients can also be able to see what are the different testing that should be available to them, and then make the decisions as well with their providers and their payers about what their next steps could be tying in a combination of their beliefs, their genetics, their access, and their financials.

Right. And, um, the ecosystems that they play in. [00:30:00]

Don Davis PhD, MBA: So important overall. And I mean, I I've used that example of decades, you know, of lag, um, you know, before that, uh, it seems, it seems oftentimes that we're in this position where, you know, some new technology will come out, you'll hear about it, but then we're so far away from it actually hitting, you know, the, kind of the main hair pathway for any PA any patient that's out there.

Beth Bauer: Well, the whole concept of the interoperability of information exchange within healthcare, the HIE, right. And the technologies that are coming available hopefully will help us to force a combination of the regulatory. Right. Because it's that we've got regulatory hurdles that, um, keep some insights from actually making it into main.

Right. It's that if we tell people about all these different things that could be, they're going to get confused. And so they don't get shared, we need to be able to, to have full transparency on, on that end. And then [00:31:00] the second piece is a lot of insights have been created, uh, essentially in a, in a vacuum of, um, IP that those who are creating these insights actually consider that to be proprietary instead of in an open source way.

2030 years down the road. And this was one of the conversations I just had at HES two days ago is that there's a whole bunch of people, myself included, who are hoping that within our lifetime, we will get to see the point where all of this data comes together in an interoperable way. And we actually all have access to these healthcare insights, but we've got a lot of metadata we're to do before we get there.

That's for sure.

Don Davis PhD, MBA: Won't that be nice. I mean, I it'll be a, a, you know, a great time as well to have, um, you have more open access to some of it, but at the same time, I agree with you. There's a lot of, a lot of, especially IP protection that that is going on. So we're at the point in the interview where I typically ask every guest the same three questions, [00:32:00] Beth, what inspires you?

Beth Bauer: It, like, I just saw at HIMSS, this group of people, these innovators, they're coming out of the woodwork to be able to solve the healthcare problems and reduce the disparities in, in health and wealth is just, um, absolutely. Uh, it, it is an inspiration. Right. And. What's super exciting about most of these innovators is they are looking to share.

They all recognize that we need to do this. Um, I call it the Amish lift that we're, we're gonna have to hold hands and then in order to move the house. Right. And, um, and that's why a lot of people are discussing that probably it's the smaller, um, businesses that are potentially going to be able to make that happen because they don't have the same resources that larger organizations have.

And so they have to trust. They have to trust their partners and they have to, uh, trust that, you know, what they're bringing is actually adding, uh, value and, and [00:33:00] therefore their, um, the people they're holding their hands with, aren't gonna let them go.

Don Davis PhD, MBA: I mean, I like, I really like your example, the Amish lift.

I'm gonna have to steal that. Uh, I'll lift it from you. my, my little pun back . So, um, what concerns you.

Beth Bauer: Um, what concerns me is that with, with, um, with all this data availability, and if we actually get to the points that we're talking about, that means that there's a lot more transparency, which is great.

It's not the transparency that concerns me. What concerns me is that there may be some very powerful folks who, um, don't like the transparency and may, um, cause this to actually take a lot longer than it needs to.

Don Davis PhD, MBA: Yeah, well, it was. I think it was the example. The part that scared me one time was I, I remember there was a replay of a, a discussion.

I believe that happened in Congress where they were [00:34:00] describing the internet as a series of pipes. And so I was like, wait a minute. It's not plumbing. It's really not. It's, it's, it's, uh, electrons being moved around, but, uh, that's the only way that they could seem to explain it to our government officials.

And then that's even more scary because then that means that they're, you know, just further behind and, and, um, I mean, if they came up to the times, maybe they still have the same concerns, but they're always, it seems like a lot of people that have, um, concerns that are. Really ones where you, if you ask most people and provided them all of the information, would they make the same decision to conceal their information?

I mean, I I've said this before where, you know, if, if certain devices in my house are listening in on my conversations, I feel bad for them. I mean, I don't know what can honestly be gleaned from that. Um, it would be a pretty boring day in most cases. Um, but yeah, I mean, I, I, I just don't, [00:35:00] I, I don't really see the same.

Need for, you know, swinging the pendulum all the way, so you can have no access to no data, but I also see kind of protecting information to make sure that, you know, people that are using it, don't have, um, the ability to go out and, you know, do bad things either.

Beth Bauer: Well, and I think one of the things you just said there is really key.

Um, I, I can't remember the gentleman's name, but there's a, a very famous data strategist out of Israel who all data, not just healthcare data who, um, articulated that, you know, the key is that. Not all the data can be in one place and that's true. Mm-hmm right. And that's why the internet example is perfect because the internet isn't all in one place it's everywhere.

Right. And that's, they, they built it in a way that, um, that that is the case. And so the internet doesn't go down, right. Because it is everywhere. It's always existing and that's Essent what we need to do with health data too. And, uh, ensure that the information [00:36:00] that can be shared. Get shared and the information that can't be shared, doesn't get shared.

And that we as individuals eventually be able to have control over who we get to share it with, because maybe we don't even want all of our doctors to see our data.

right, right. right.

Don Davis PhD, MBA: My last question for you is what excites you?

Beth Bauer: Oh, I'm super excited by, um, what young people are gonna be able to bring to this. Um, because they have grown up in this world of data. They have grown up being exposed on just about everything about themselves. And so they have a lot less concerns. Uh, with regard to the transparency components, they also recognize that we need to have a lot more health and wealth equity, and that they're willing to put in the hard work to be able to make that happen.

So super excited, wanna make sure that all of our young people are able to, um, use data to its full potential, uh, and, and working with young groups to actually [00:37:00] sure that that happens. So thank you.

Don Davis PhD, MBA: Yeah, thanks so much. And, and so Beth Bower, thank you so much for being a guest on the life science success podcast.

I greatly appreciated our conversation and getting to spend time with you.

Beth Bauer: I, as I do as well, I can't thank you enough. It's been a pleasure, Don.