Data is the key to AI with Keshia Maughn

The intersection of data and artificial intelligence forms the cornerstone of today's healthcare innovation, transforming the way we approach medical advancements.

In this week’s episode, I'm excited to welcome Keshia Maughn from STATinMED, a company carving out its space with a focus on specialized data solutions in biotech. We look at the nuances of data's role in AI, the unique challenges companies face with electronic medical records, and why understanding the lifecycle of a client's needs is crucial.

Keshia brings her deep expertise in navigating the complexities of a saturated market and emphasizes the importance of practicality over flash when it comes to investing in new tech.

To grasp the full spectrum of how data underpins AI in biotech, tune in to our insightful discussion. Remember, clarity in data leads to precision in AI - don't miss this episode.

Specifically, this episode highlights the following themes:

• Customization and strategic application of AI in healthcare
• Navigating challenges in data extraction and quality for AI integration
• The consultative approach to client partnerships in data analytics

Links from this episode:

• Get to know more about Steven Swan: https://www.linkedin.com/in/swangroup
• Get to know more about Keshia Maughn: https://www.linkedin.com/in/keshia-maughn-mph
• Learn more about STATinMED: https://www.linkedin.com/in/keshia-maughn-mph

Transcript

Keshia Maughn [00:00:00]:
I think right now, what I am seeing, at least from the vendor side, right, is there's so many more vendors out there than there were five years ago. And I feel sorry for the clients, honestly, because it's a lot of information they're taking in, and everybody's telling them, I have the best x, I have the best y, I have the best z. And trying to weed through all of that to then realize how do I use all these different pieces to get to what I want? I think is really where I see some of the things in terms of the trend, is that it makes it harder for people to. To make a decision when there's so many options.

Steve Swan [00:00:38]:
Welcome to Biotech Bytes. I'm your host, Steve Swan, and on our show here, we speak with it leaders within biotech about their thoughts and feelings around technologies that are currently affecting our industry. And today I am joined by Keshia Maughn of STATinMED. STATinMED is a niche data player within the biotech space. And thanks for agreeing to join us and chat with us about this, Keisha.

Keshia Maughn [00:01:02]:
Absolutely happy to be here.

Steve Swan [00:01:04]:
Thank you. I'd say we're going a little off script today, right? Because usually I'm talking to the technologists about what their thoughts are, but so many of our technology leaders have been talking about AI, and every single one of them agrees on really only one thing. And the one thing that they agree upon is that data is the gasoline that drives the AI engine. And I figured, who better to have on than somebody like you who can talk right, to maybe some of the pain points, maybe some of the trends you're seeing and those kinds of things from a data perspective in our industry, in the biotech industry. So let's kind of. Let's kind of dive right into it. And I guess maybe from dovetailing off that, what are some of the things that you're seeing in the industry when, as it comes to this big AI trend, are you getting a lot of demand, a lot of folks coming to you saying, hey, how can you help us?

Keshia Maughn [00:01:46]:
Yeah, I mean, it's a conversation. I think a lot of times for us, it's more so that people come to us with a, this is the space that I'm in. These are the types of questions that I have. And then based on that, our recommendation may be, hey, let's go with some machine learning or some AI versus them coming. I would say fewer people come to us already, like, dead set, that AI is the solution that they want to go with.

Steve Swan [00:02:11]:
Really interesting. Okay. Okay. So first things first. They come to you with data, questions, issues, comments, concerns, that kind of.

Keshia Maughn [00:02:19]:
Yes, yes. So example, question, you're going into a market that's highly saturated and you want to understand, okay, where is their opportunity? For me, I'm a small company. I don't have a bunch of money to have like 200 reps out there pounding the pavement. So I need to be able to work strategically. And I also know that my product is not likely to be first line or preferred by all the payers. So what do I do?

Steve Swan [00:02:46]:
Interesting. Pretty cool. And so tell me about some of the trends that you've been seeing in the marketplace. Maybe as the AI buzz, I don't even know what you want to call it, kind of picks up steam.

Keshia Maughn [00:02:57]:
So I think for some of the clients, well, I think a few years ago, personally, I've been dealing with machine learning and AI for four years now. This isn't my first rodeo with, it goes back to probably about 810 years of working with this type of stuff. So for me, it's not brand new. I think that some clients experienced things maybe early on, and, you know, different companies have different policies. I would say some kind of have a black box policy. So you don't know how the sausage is cooked. They just bring the sausage out on the plate and they want you to trust it, which I can understand from a client perspective why that might be a little difficult. So with that, some are very like, reluctant where they're like, I don't want AI, I don't trust it, I don't know what it is, blah, blah, blah.

Keshia Maughn [00:03:44]:
And I guess for us at Staten med, and one of the things I love about my role there is that we have a more of a clear box policy. So you know exactly how your models are built, you know exactly how everything's defined, and you know exactly the algorithms used to get there. There's no secret. Interesting, because there's no need for there to be a secret. You should know exactly what it is that's going into what you're doing.

Steve Swan [00:04:07]:
So you're not just doing the data and the things, you're actually can come up with a solution for crunching the data. I mean, I don't know how else to phrase it. Right?

Keshia Maughn [00:04:14]:
Yeah, I have an analytics team. I actually head up the analytics team here at Statenman.

Steve Swan [00:04:19]:
Okay.

Keshia Maughn [00:04:20]:
I have a great team of people who get the data done.

Steve Swan [00:04:24]:
Got it, got it. So your solution is, I mean, from this perspective, anywhere from data and analytics end to end?

Keshia Maughn [00:04:31]:
Yeah, absolutely.

Steve Swan [00:04:33]:
Okay. Okay, good, good to know. I wasn't fully aware of that. So now I'm ignorant, so now I'm not. So that's good.

Keshia Maughn [00:04:41]:
We definitely do end to end solutions. We work. We also like to work around what I like to call the realms of reality and practicality with our clients. So everybody has a goal and a dream and a thing they want to do. But what is actually practical? What can you get institutional buy in for? Because that's important. None of these decisions are made in a silo. You might have a point of contact, but that point of contact has higher ups, they have other colleagues. There's trade offs within that business that, hey, if we give you x amount of dollars for your project, that means somebody else isn't getting a project done.

Keshia Maughn [00:05:15]:
So we like to work to help our clients come together and figure out, okay, how can we service the most needs in a way that there might be one small part of the project or one objective that focus on a question that one stakeholder has while working to service the larger needs of an engagement but doing so, or maybe saying, hey, this, you have a great idea, but realistically speaking, the data are going to be very expensive. Especially one of the things you see a lot of people talking about now is the genomics, for example. Genomics data are expensive. There's no two ways about it. I have yet to see any type of study involving genomics data where those data are under seven figures. So if you don't have a set of figures to spend, maybe genomics might not be in the budget or the cards for you. So what can you do if you're in that space and you can't afford that data? Well, there's a lot you can do from an analytics perspective, but it might just look a little different.

Steve Swan [00:06:16]:
Interesting. Pretty cool. So now then, how does. So from a data perspective, right, you've got you folks, but you've also got the large, the IQV isn't such, right? I mean, you've got the big ones. How does, again, just trying to mentally differentiate in the marketplace, right?

Keshia Maughn [00:06:34]:
Yeah.

Steve Swan [00:06:34]:
How does what you folks do differentiate, or how are you differentiated from what they're doing?

Keshia Maughn [00:06:38]:
So I guess with us, what our huge differentiation point is is that Acuvia, I mean, they're, they're big guys, right? So they have everything. They have EMR, they got claims, they got the clinical trial operation, they got, they got everything going on over there. We're a lot more focused. Also, while we do have data assets and access to data, we also have a plethora of partnerships. So if you go to IQvia, for example, for a study, you're using iQvia's data for the claims for the EMR, for any, you know, genomics part, anything like that, you're getting IQvia full stop with us. We like to take a different model, so we're not necessarily beholden to only using what we have. We also have a bench of partners where it's like, hey, if I don't have it and I can get it for you elsewhere, let's do that. Let me get it for you elsewhere and we'll work with you to get.

Keshia Maughn [00:07:37]:
Or do you have something that you can use? Right. So if you have optum data set or something like that, like I always ask myself, what do you already have? Because we don't necessarily need to run out and get something new. We may be able to get a lot of value out of what you have.

Steve Swan [00:07:51]:
Makes a lot of sense. One of the types of conversations I've had is along the lines of, well, you know, Steve, we're smaller, so we can't afford the, the big solution. We look for the smaller solution. It sounds like you can customize your solutions a little bit better than maybe some of the others.

Keshia Maughn [00:08:06]:
Right?

Steve Swan [00:08:06]:
So that's good.

Keshia Maughn [00:08:07]:
Yeah.

Steve Swan [00:08:08]:
Yeah. And you had mentioned to me in the past the realm of reality, which.

Keshia Maughn [00:08:11]:
I think is realm of reality. Yeah. Because, I mean, I don't ever want to sit here and come to you with a proposal or estimate or anything that's going to shock you out of your seat. And you're like, you know, we're talking for weeks and going back and forth and getting you all this feasibility and coming up with this proof of concept, and you're like, okay, this is exactly what I'm looking for. And then when I give you the price, you're like, oh, wait, I can't show this to my higher ups. They'll think I'm touched in the head or something. Like they'll start questioning my judgment if I go to them with that. And then you're freaked out because you have to meet with them the next day and you were thinking this is what you were going to show them.

Keshia Maughn [00:08:50]:
So I like to start conversations very upfront. You don't have to tell me exactly how much money you have. Cause I get that there's that aspect of things too. But maybe we start with what's your range? Right? Like, are you at the, do you have north of. You don't. Okay, well, let's see what's available below that. And typically when you start there people be like, oh, 500. I'm nowhere near that.

Keshia Maughn [00:09:11]:
Or they'll be like, okay, yeah, I got something like that.

Steve Swan [00:09:13]:
Right, well, that's good. Yeah. Cause then otherwise you always need to, like I do, right. When I go in and talk to a company that's looking for an it leader. Right. Where are you in your life cycle? Are you looking for somebody that's focused on commercial, somebody focused on R and D, somebody focused on foundational or infrastructure, whatever it is, it just depends on where somebody is. So if you don't understand where your end user or your client is, you're starting on the wrong foot, you know, so.

Keshia Maughn [00:09:36]:
Exactly.

Steve Swan [00:09:37]:
Yeah. So then, as far as some of the solutions that you've had to work on in the recent past. Right. And I'm not looking for super specifics, but have you seen the trends picking up on the AI side, on the AI side of things or not?

Keshia Maughn [00:09:53]:
Yeah, I mean, I have a few publications that we've actually, that were real world applications. I mean, I can talk about them because they're all out in the public domain where we leveraged AI in order to gain more insights into various therapeutic areas. So you. And I'm seeing other people with those types of publications as well. So it is becoming a bit more commonplace. I know a lot of it's used on the commercial side and it's a bit more internal strategic type uses. But I do also think from the evidence side that there's a lot of value, especially as we're leaning more, moving closer into value based care, value based contracting. I think that AI can play a critical role in kind of crafting those strategies around what those contracts should look like.

Steve Swan [00:10:38]:
Now, what do you think about. I had one gentleman talking to me about, I guess, to steal the term from him, he felt like we could squeeze more juice, is how he phrased it, out of our data. And I said, well, what do you mean? I think we're all doing a lot of work on our data. He said, well, I don't think we're going after a lot of the inferences we could draw from our EMR and EHR data. Right. You know, this happens and that happens, or this is going on here and that's going on there. Have you seen much from that end of the data? You know, not necessarily commercial or clinical, but more the EHR and EMR. Yeah.

Keshia Maughn [00:11:11]:
So EHR is a bit of a challenging space, right. So if you think about things like claims, for example, you can get pretty large chunks of claims data, relatively inexpensive. You can get, you know, big old symphony style data sets. You can get the optums in the market, scan style data sets, you can get data from pretty much anybody and cover large chunks of the country. I mean, you get it from the government, even like CMS data. But when it comes down to really EHR, EhR is a lot more of a fragmented universe. There's thousands of different emrs out there. So I think the biggest challenge with doing any type of work with EMR is if you have a closed IDN where patients get all their care there, you'll be, you know, relatively okay on samples.

Keshia Maughn [00:12:01]:
Like, you'll have smaller samples, but you'll have the robustness because the patients are, you know, getting everything done there. Say if it's a center of excellence, it has multiple medical units of excellence. But if you're talking about what I call more general EMR. So things that are really more along the lines of like, oh, this EMR is hyper focused on, it's just getting stuff from urgent cares or just getting stuff from specialty clinic, like oncology data sets. Oncology, EMR, great data. Very helpful for answering oncology specific questions. But if you want to know anything more about those cancer patients, good luck. If those cancer patients move to another center, good luck.

Keshia Maughn [00:12:43]:
So there's a lot of challenges, I think, with EMR because of just the fragmentation of it, because ultimately we go to whatever doctors we want to go to.

Steve Swan [00:12:52]:
Right.

Keshia Maughn [00:12:52]:
I don't know how many EMR systems I'm in. I'm all over the place. So I think with EMR, it's a lot harder to have the complete picture of the patient.

Steve Swan [00:13:01]:
Interesting. Okay. Okay, I see it. Yeah, I was just thinking of it, you know, big picture without the actual, you know, drilling down into it like you just did, you know, and from a big picture, makes. Makes sense. But from what, when you bring it up, the reality of the situation. Yeah, it's tough. It's real tough.

Keshia Maughn [00:13:19]:
Very hard. It is very tough. And I personally look at it from the perspective of when I look at EMR, what is it that you need? Pick one to three endpoints. But you can't have everything, because the other problem is, is that you have to also balance that with reality. Right. If you want to have patients who have BMI, okay, that's one that you're going to get, because everybody gets BMI taken. But say, for instance, in the cardiovascular space, LPA, lipoprotein a, that's a lab test that's not very commonly done. So the second you're going to require a test that's not as common.

Keshia Maughn [00:14:01]:
Plus, a test that's very common. Plus, you know, another test and another test, you're starting to whittle down your sample size. So you have to first, before you even start identifying. Oh, these are the endpoints I want first. Start with how commonly are these done? Things like biopsy results, people. I get that question. Oh, biopsy results. I was like, okay, well, how often are people you're working to say something like liver or something like that? Well, how often are people getting livers, biopsy.

Steve Swan [00:14:27]:
Right, right.

Keshia Maughn [00:14:28]:
Not, it's a very invasive thing. Nobody wants to be probed around in their liver for no good reason. So you gotta have a very good reason to get me to agree to that.

Steve Swan [00:14:40]:
Yeah, put it that way.

Keshia Maughn [00:14:42]:
If it's not very common, then finding the data are gonna be hard. And if you then want to add other criteria, you're making it impossible.

Steve Swan [00:14:50]:
Right, right. So then when you go in and I'm just thinking about, you know, I'm on the other end. Right. And I need whatever data I need. And so when you go in and you counsel these folks on what data? Realistically, I was going to say from a reality perspective, that's, you know, that you can get. You're like you just said, from the realm of reality, you're really level setting them as to what the reality of the situation is and what they're really looking for, and then what you can get them and what you can provide them. I mean, you know.

Keshia Maughn [00:15:21]:
Right.

Steve Swan [00:15:22]:
You have to.

Keshia Maughn [00:15:22]:
Because I'm not limited, so because I'm not at a bigger company where thou must only use our data. Our data is the best, you know, that type of mentality. We have dozens and dozens of partnerships and can access all types of data. So we're not limited in that way. So for us, it's like, well, what is it that you need? The limitation is really, you know, how much. How much can you spend?

Steve Swan [00:15:47]:
Mm hmm.

Keshia Maughn [00:15:48]:
It's kind of like house shopping. If, you know, you only can spend 500k, don't go looking at those million dollar houses.

Steve Swan [00:15:54]:
Right, right, right, sure. And so in a bruised ego.

Keshia Maughn [00:16:00]:
Exactly.

Steve Swan [00:16:01]:
So at the end of the day, then what you would say that's unique about your organization versus the others is you really can put together more of a customized solution. It just depends on where the budget ends.

Keshia Maughn [00:16:13]:
Right. And also just even sometimes helping people to realize that you'll come to me. Okay, what type of study you want to do? And I'm like, okay, those are really great goals. But before you get to that study. Let's talk about where your product is right now. It just launched last year. EMR is probably not the most practical study for you to go with because uptake is still building. EMR might be a few years down the road.

Keshia Maughn [00:16:37]:
So maybe let's start with something a bit more simpler that you can get something out, maybe get a poster or a product presentation out of it, a manuscript out of it, where you can start characterizing your early adopters or something of that magnitude. So it's not just, you know, sometimes you have to shift the question that you're going to answer today and at least make some progress and then work down the line to be able to answer that bigger goal question.

Steve Swan [00:17:04]:
Right. What I'm doing as you're talking is I'm equating it to because I don't know your world, right. I know my world, right. So I've got to think about how I approach people. And that is true. You know, sometimes you really got to ask the right question because sometimes on the other side, you know, sometimes they don't know exactly what they need or what's in the marketplace or, you know, what some of the solutions are. So, yeah, you gotta, you gotta sculpt the question, right? And you gotta ask the question, right. And sometimes you gotta ask those difficult questions, you know, are you really here? Do you really need that, you know, based on where you are and what you're doing now, what would you say are, well, I guess more that customization.

Steve Swan [00:17:42]:
Right. So, you know, is probably one of the things that it sounds like you would, you would talk about, you know, your company has an advantage over the others. Anything else that you could bring up that you would think would make your company? Why would I want to be there? Why would I want to work there? Obviously, you're fully remote, right? And like you said, you guys can, yeah, you guys can provide those custom solutions, but is there anything else that you could think about or that I would think about? As I'm looking at statin med, we're.

Keshia Maughn [00:18:11]:
Not a big company, we're a smaller company. So there's a lot of opportunity in terms of growth. We're also disease area and data agnostic, so you get opportunities to do a lot of things. So, sure. Yeah, we have data in house that we use, but we have other types of data as well. So you can have the opportunity to work with things that are interesting, like genomics and EMR and work across indications. So you might like oncology, but have an interest in cardiovascular disease. Oh, we get a cardiovascular project.

Keshia Maughn [00:18:41]:
Boom, you get an opportunity there. So I think those are some of the.

Steve Swan [00:18:44]:
So across all therapeutic areas then, too, right? No specialty. Okay. Okay.

Keshia Maughn [00:18:49]:
Yeah, we have people who are specialists in certain ones, sure. But.

Steve Swan [00:18:54]:
Yeah, yeah, but you can work across any of them without an issue.

Keshia Maughn [00:18:57]:
They can work across any, yeah.

Steve Swan [00:18:59]:
Very nice. All right, cool. What other kinds of trends, you know, just kind of big picture, do you see in the marketplace right now?

Keshia Maughn [00:19:06]:
I think right now, what I am seeing is a lot of, at least from the vendor side, right. Is there's so many more vendors out there than there were five years ago. And I feel sorry for the clients, honestly, because it's a lot of information they're taking in, and everybody's telling them, I have the best x, I have the best y, I have the best z. And trying to weed through all of that to then realize, well, how do I use all these different pieces to get to what I want? I think is really where I see some of the things in terms of the trend is that it makes it harder for people to make a decision when there's so many options. So I'll give you an example. I've had a clients that I've talked to where they're like, oh, we're interested in getting data for some type of rare cancer, right? And I'm like, yep, okay, gotcha. They tell me what genomic markers they want, and I'm like, you understand that this is. They're like, oh, well, we talked to some of the genomics labs and blah, blah, blah.

Keshia Maughn [00:20:07]:
And I'm like, yep. And they gave us a really high price. I said, yeah, those data are very expensive, you know, and they're like, oh, well, we have this other type of EMR that might contain some of it. I'm not going to name any names. And I'm like, yeah, but that's also going to be very expensive. So it's like, I think they're also just getting overwhelmed and inundated with a lot of very high priced options. And also, then they get stuck. Like, I don't want to make the wrong decision.

Keshia Maughn [00:20:34]:
I don't want to. Am I asking the right questions to make sure that this is the right data? And I think I'm seeing a lot of that, is that they don't know what questions to ask. They don't know, is this person just selling me something that looks shiny? But then the second I get it, the data is not going to be up to what we need, ultimately.

Steve Swan [00:20:53]:
So, but through their homework of talking to folks like you and others, you would hope that they get to the point where they are asking the right questions. Right. Or know what questions to ask. Do you? I guess you've got to sculpt the conversation a little bit, right. To make sure that the right questions are answered, you know?

Keshia Maughn [00:21:11]:
Right. And that's true. But I think the challenge is that there, not many companies take the approach we take. A lot of the other companies out there are just trying to sell them that shiny x, y, or Z, and whether or not that thing actually does what they need it to do, we're much more consultative. Oh, we take, so, like, my, I'm not a salesperson, but I'm on sales calls.

Steve Swan [00:21:36]:
Right.

Keshia Maughn [00:21:37]:
I'm a subject matter expert, so I'm there to help make sure there's an understanding so we don't have the situation that happens in a lot of companies where you have, you know, the fast talking sales guy.

Steve Swan [00:21:48]:
Right.

Keshia Maughn [00:21:48]:
Talking out of your budget, and then when the project closes, you're like, this is not what I wanted. This is not what I signed up for. This is, you know, that's a tough.

Steve Swan [00:21:57]:
Conversation because I've had, you know, of the IT leaders I talked to, you know, and the folks that have the data or own the data or whatever, own the technology, you know, with everything that's going on right now in the world, like, we're, like, we've talked about AI quite a bit here. You know, I ask all these leaders, you know, you, the budget's finite. Everybody's. But it's not unlimited. Right? So when I ask them, you know, have we put aside an innovation budget and does that shrink the rest of the budget? Nobody's really committing, but you gotta guess that it is, you know, because budgets is big, and whatever you're gonna spend it on, you gotta spend it. You gotta squeeze everything in there, you know? So you used to spend this much on this stuff, but you have to now buy this much stuff with this same budget. So you gotta kind of move it around, you know, and figure out what's going to make sense and what's not. And that's where, you know, like I said, that innovation that AI, and then on top of that, where you guys come in, you know? So from a spend perspective, so that can't be easy for anybody.

Steve Swan [00:22:56]:
And that's got to be something that, as you said, you've got to help them work their way through or the salesperson or somebody. Right. Has to help them work their way.

Keshia Maughn [00:23:04]:
Yeah, yeah. It's, it's a lot. And then it's also sometimes, especially when people move around from company to company, they might be new in a company and having to deal with, like, just trying to navigate the politics and understand, you know, what everybody's like. If they have to get buy in from a bunch of people they've never met in person, they barely know, you know, and some of my time, I'm like, all right, let me help you. Let me help you succeed. Like, that's the way I look. Like, let me help you succeed. Let me help you frame this up, like, let's work together type of way so that you can feel confident in everything as you're communicating this to your colleagues.

Keshia Maughn [00:23:47]:
Because especially if it's your first project or your first set of projects within that company, you don't want to be the person who contracted for something that's a dud. Right. As your first project in that company, that's not a good thing.

Steve Swan [00:24:01]:
Let me help you. Help you, right.

Keshia Maughn [00:24:03]:
Yeah.

Steve Swan [00:24:04]:
Well, so anything more that you think we should cover as far as trends, things that you folks do or things going on in the market space?

Keshia Maughn [00:24:13]:
I would say one of the things I'm seeing more and more we touched on the budget aspect is really just the budgets. Right. So everybody's going through different things within the organizations where they're trying to figure out, you know, how they're going to prioritize and how they can leverage what they have in house and those types of things. And I think, you know, we all have to do that type of accounting, both personally and, you know, from a business perspective is trying to maximize the value of the resources available to us. And even though there might be something nice and shiny out there, the reality is, is that there might not be shiny budget.

Steve Swan [00:24:51]:
Right. Right. Again, I'm going to go back to it. I know I said this at the onset. I've had several conversations. You know, I'm doing this once every other week, right. And launching these on my podcast. And it's really around the biotech leaders in technology, and they're all talking about AI.

Steve Swan [00:25:08]:
However, every single one of them has said, if your data is not ready, if you don't have the right data, you know, data is the fuel for that engine. And if it's not where you need it to be, you're dead in the water. You're putting, you know, you're not putting gasoline in the engine, put water in the engine, it's just not going to work. And as long as a company like yours can come along and at least like you said help guide them and help get them there, you know, because a lot of companies don't have their data in shape and some of them even admit it. You know, we're working on it, but it's going to take a while and we got to get it there. Or we don't have the right kind of data or it's not whatever structured right. However, you know, it's just not going to work. But you know, that's where an organization like yours comes in working with because there's, I don't know, 6500 different biotechs out there.

Steve Swan [00:25:58]:
So they all have data needs at one, one level or another.

Keshia Maughn [00:26:03]:
So, yeah, that's, yeah, and then sometimes they have licenses for stuff. And one of my favorite answers to the question I ask often is like, okay, do you have any data in house currently that you're using or that we could potentially leverage to help, you know, for efficiencies purposes? They're like, oh yeah, I think somebody has some IQV data. I'm like, okay, which one? They're like, what do you mean? I'm like, you know, IQV has like 20 different data assets that they out license. So which one do you have? And, you know, just even that question sometimes to them, they're like, oh, I didn't realize that. And I'm like, yeah, well, your colleague in the commercial side may have like, you know, that NRX TRX data. That's an IQV thing, but it may not necessarily be helpful for the questions you have from an Hior perspective.

Steve Swan [00:26:52]:
So would that be, you just made me think of this and my question started going to, as you started talking, you know, what advice would you have to give to a company right before they call you? What should they think about? What should they be doing? But I think maybe you almost just answer that, you know, understand where you stand with your data right now, and maybe that's more difficult. Yeah, maybe that's more difficult than I think it is. You know, I'm sure that's hard to.

Keshia Maughn [00:27:14]:
Understand what you have, the specific licensing details of it, because just saying I have IQvia data. When IQvia has 25 different data products that they're selling, it's not very helpful because I don't know which one you're, it's like, okay, which one are you talking about the EMR? Are you talking about some of their more aggregated reports that, you know, commercials teams might use? Is it the patient level data?

Steve Swan [00:27:39]:
Like, understand what you have and probably why you have it too? I mean, why do you, why do you have that specific data set?

Keshia Maughn [00:27:45]:
Why do you have it, and does it contain what you need? So, for example, if you license the data, this actually happened a few years ago. I was working with a client that had a data set from a company, won't name the company, but the data set they had, they had for the disease area, they needed of interest, but they now needed a control population, so non diseased patients for us to do the comparative study. So we were all like, okay, yeah, we can do this, blah, blah, blah. And I said, okay, wait, one quick question. Do you have those control patients? And they're like, what do you mean? I said, so you told us you have the data set that has those diagnosed patients, but in order for us to have the control patients, do you have those control patients in your license? And they're like, let me find out. Lo and behold, they didn't have them. And then when they went back to the vendor to ask for them, they were said, oh, yeah, well, you didn't pay for that, so you'd have to pay more money. It derailed the whole thing, derailed the whole discussion, and we ended up, we were like, you know what? Don't.

Keshia Maughn [00:28:46]:
We can help you find something that will fit in. You're like, I wasn't planning to buy data because I had budgeted for already having it.

Steve Swan [00:28:55]:
Wow.

Keshia Maughn [00:28:56]:
So we were able to work with them to find a solution that kept them within their budget range. But those types of things happen where people just, they're like, oh, well, we, yeah, you have it, but it's only if you didn't have the comparative groups you needed.

Steve Swan [00:29:11]:
So understanding what you have, why you have it, and then as best you can, what you need and why you need it, but those are the things that you're going to help them solve, or your team. Yeah. So, wow. Okay. All right. All right, cool. Good. Well, thank you very much for your time here.

Steve Swan [00:29:27]:
You know, is there anything else you wanna cover?

Keshia Maughn [00:29:28]:
Exactly? No, I think we covered a lot of things.

Steve Swan [00:29:32]:
Okay, before we go, I don't know if you've watched any of my other podcasts. I have that the very end, there's always one question I ask everybody. If you made it all the way through to one, you know what I'm about to ask you.

Keshia Maughn [00:29:44]:
Go for it.

Steve Swan [00:29:45]:
I always ask folks what has been their music? I like music. I'm a music guy. You know, live music concerts. What was your favorite live music you'd ever seen?

Keshia Maughn [00:29:55]:
I have been to one concert in my life. Cause I don't like loud. I get that a lot. This was back when I was a teenager. It was Eminem, Snoop Dogg smoke tour.

Steve Swan [00:30:09]:
Wow. Now I understand why you don't like loud. That scared you off.

Keshia Maughn [00:30:15]:
Yeah, that was it. I can't do it.

Steve Swan [00:30:17]:
I gotta tell you right now. I see Snoop Dogg, like, everywhere. Like, he's like some. Yeah, he's like an ambassador. He's like a king now. People are like. He's like the guy. And I'm like, I guess people have a short memory.

Steve Swan [00:30:27]:
I don't really know, but it is what it is, you know? So that's cool, you know? But, yeah, no, he's. He's rebranded himself a little bit. Right? So it's all good.

Keshia Maughn [00:30:36]:
Yeah. Oh, yeah. A lot of those rappers from my upbringing have rebranded themselves. I mean, look at ice tea. He's been on what is that, like, law and order for, like, 20 years?

Steve Swan [00:30:46]:
Isn't that crazy, right? It is. You know? Oh, gosh, what's her name? I see her on all the shows too. Queen Latifah, right?

Keshia Maughn [00:30:53]:
Mm hmm.

Steve Swan [00:30:54]:
I see her on all this stuff. I'm like, okay, cool. Yeah, so you're right. They all have, you know, polished themselves up a little bit. So. Anyway, all right, well, thank you very much. I appreciate your time.