Key Takeaways
- Leverage AI and automation to clean provider directories, replacing manual verification efforts to improve patient access and reduce operational costs.
In an era where healthcare transformation is measured by more than just cost and compliance, the real battleground is access—who gets it, how fast, and how well. At the intersection of artificial intelligence, automation, and equity, new solutions are rewriting the rules for healthcare organizations, providers, and, ultimately, patients. As health plans race to personalize care and cut operational waste, the role of AI in navigating the labyrinth of provider data has become mission-critical. But with buzzwords everywhere, few leaders are bridging the gap between technical promise and real-world results.
Enter Meghan Gaffney, CEO of Veda Data (now H1), who brings a rare perspective: a policy insider turned tech entrepreneur, with a pragmatic, story-driven approach to solving one of the industry’s knottiest problems. On a recent episode of the American Journal of Healthcare Strategy podcast, Gaffney shared her journey, hard-won lessons, and what it really takes to make data work for everyone. This post unpacks the conversation—so whether you’re a health system executive, payer leader, or policy-minded innovator, you’ll leave with concrete insights (and more than a few quotable moments).
Question: What inspired a seasoned policy leader to pivot into healthcare AI—and what does it reveal about today’s access crisis?
In Meghan Gaffney’s own words: “Like a lot of entrepreneurs, it was a personal experience.” After spending nearly 15 years in Washington, D.C.—“I was in the policy space in Washington DC for just under 15 years and was there during the time when the ACA was crafted”—Gaffney knew the system’s blind spots from the inside out. But it wasn’t until she tried, and failed, to navigate her own daughter’s care through well-insured, well-connected channels that the gap became real. “I could not figure out how to get an appointment with the right specialist for her and get her treated quickly for anybody that was covered in my insurance plan...If I couldn't do it literally sitting in the halls of Congress...then nobody could.”
That frustration became the genesis of Veda Data. The goal: build scalable AI tools to help people find the right provider, equitably, and fix the “invisible” infrastructure problems that policy debates often miss. Her journey is a case study in how industry outsiders, with grit and perspective, are now shaping the future of digital health.
Key Takeaways:
Personal pain points—especially for “insiders”—are powerful innovation triggers.
Fixing access means going beyond buzzwords to solve real workflow and data infrastructure challenges.
Question: How do you create cutting-edge AI for healthcare when you’re not in Silicon Valley—and years ahead of the hype cycle?
Gaffney’s path wasn’t typical. Her technical co-founder was “an astronomer...building AI tools in radio astronomy”—not a classic health IT hire. In fact, the team’s initial foray was at a hackathon in 2016, working with real provider data and forced to innovate out of necessity: “We couldn't make 10,000 phone calls to try to validate the data...so we had to build something scalable and data-driven.”
What set Veda Data apart was its commitment to:
Adapt academic machine learning to healthcare data: Borrowing methods from radio astronomy, where “there's so much data...they couldn’t process it manually...so they had to start writing machine learning scripts to clean up all the errors.”
Focus on human impact, not just technical novelty: Gaffney notes, “We build a system that can find the errors automatically clean them up and then take disparate data sets and link them together so we can get the right answer.”
Their Midwestern roots and scrappy, science-driven culture became an advantage, not a liability: “Our company was founded in Madison, Wisconsin—not in Silicon Valley...What it made us do was build things in a smart way that were scalable...it made the technology more affordable for our customers.”
Key Takeaways:
Breakthroughs happen when you cross-pollinate disciplines (astronomy + healthcare).
Starting “outside the bubble” can force more practical, cost-effective innovation.
Question: What’s the real-world impact when health plans get provider data right—and what role does AI play?
For payers like Humana, the answer is simple: getting provider directories right directly translates to better access, member satisfaction, and savings. As Gaffney shared, “For Medicare Advantage, one of the things that matters most to seniors is when they need an appointment they have to find care and so the accuracy of their directories...is a huge benefit for their seniors getting access to care and access to the right care.”
How AI-powered data accuracy drives value:
Enriched specialty and sub-specialty data:
“If you need a neurologist, you really need to know does this person treat Parkinson’s or are they treating nerve pain—two very different outcomes.”
Operational cost savings:
“They don’t have to use their team to do things like make outbound phone calls to figure out the provider’s fax number—they can use that team to be responsive to what their members are looking for.”
Enhanced member experience:
Seamless, accurate directories mean fewer dead ends for patients—especially vulnerable populations—reducing frustration and accelerating care.
Key Takeaways:
Data quality isn’t a technical detail; it’s a frontline determinant of patient access and satisfaction.
AI can “scale” what would otherwise require armies of manual phone calls and data cleanup.
Question: With provider and facility data coming from everywhere, how do you actually clean it up—and why does measurement culture matter?
Gaffney doesn’t sugarcoat the challenge: “It’s 100% a mess.” The team’s approach is deeply scientific, drawing from the hard sciences’ emphasis on measurement and iteration. “We now have over 10 PhDs from the hard sciences and they're used to measuring things over and over again...It's okay for them to get it wrong culturally.”
What sets Veda (H1) apart:
Relentless internal measurement:
“We try to get the data point right, we measure it internally, and if it doesn’t work that’s okay—we just keep going back to the drawing board.”
Specialist expertise for niche problems:
Gaffney’s example: “We have one former astrophysicist...his entire job is to make sure that we can get the latitude and longitude on hospital buildings and outpatient surgical centers correct...it matters when a patient’s plugging in the data to Google Maps.”
Focus on details others ignore:
Investing in “minor” issues (like mapping complex hospital addresses) pays dividends in access, navigation, and ultimately patient trust.
Key Takeaways:
Measurement-focused, scientific cultures adapt faster and achieve higher data quality.
Getting the details right can unlock “last mile” improvements in patient experience.
Question: How do you design AI, teams, and business processes that actually advance health equity—instead of reinforcing old biases?
Gaffney is clear-eyed about the risks: “AI is a tool like any other tool...the machines are incredibly powerful but they do what they’re told to do.” If your goal is only profit, your algorithms will reflect that. But if you build with equity and bias mitigation in mind, you can change outcomes.
Strategies for ethical, equitable AI:
Reflect the communities you serve:
“We actually built our data science team to reflect the communities of people that we serve...having all parts of our community reflected in the teams can be a really powerful way to prevent unintended or harmful outcomes.”
Recognize application and hiring bias:
“If you look at a job description...women will only apply...if they meet 100% of the qualifications. Men will apply if they meet 50%...So in order to get more diverse teams you really need to do some outbound recruitment.”
Create structural incentives:
“We give a referral bonus...if someone refers in someone and they get hired and they stay for a period of time they get a bonus for doing that.”
Key Takeaways:
AI is not neutral—diverse teams and intentional objectives produce better, fairer tools.
Outreach and thoughtful recruitment are essential for real diversity; passive pipelines won’t cut it.
Question: How can startups justify the cost of hiring top talent—and what’s the ROI for customers?
Gaffney doesn’t flinch from the numbers: “Paying people fairly is expensive. Paying to constantly recruit and hire is also expensive.” Veda Data invests heavily in people, covering “100% of our employees’ healthcare premiums for them and their family for a no deductible plan.” The payoff is employee loyalty, deeper expertise, and better results for customers—especially as healthcare problems require deep domain knowledge.
“If you had somebody that’s been thinking just about hospital addresses for two years, they can do so much more...Our customers appreciate the continuity frankly...they value that as well and are willing to invest a little bit in our team in addition to the technology.”
Key Takeaways:
Investing in people drives lower turnover, deeper knowledge, and better outcomes for both the company and its clients.
High retention and fair compensation are especially valuable in data-driven, relationship-dependent industries like healthcare.
Question: How can health tech companies prevent algorithmic bias and privacy risks as they scale up AI?
Gaffney is direct: “If you have an engineering and innovation team that is building with profit in mind and they’re not thinking about bias and they don’t have objectives set out for things like making sure the outcomes are equitable, you will get a profit-only answer.” The antidote is proactive governance:
Intentional design and safety procedures:
“If you ask these tools to build things with equity and bias in mind and you put the right safety procedures and checks in place you will get an outcome that reflects that intention.”
Team diversity as a safety check:
“They understand the impacts that those biases can have because they’ve seen them in their own lives.”
Continual measurement and openness to correction:
“It’s okay for them to get it wrong culturally...in science, you can publish a paper that says ‘Hey, we thought this was a cool signal but it was actually an airplane’ and you don’t get fired for that.”
Key Takeaways:
Build diverse, intentional teams and set explicit, equity-focused objectives for every AI deployment.
Accept that iteration and correction are essential for responsible, high-quality AI.
Question: What cultural values underpin Veda Data’s (now H1’s) success—and how can executive teams apply them?
Gaffney credits both Midwestern pragmatism and her time in bipartisan policy work for her results-driven, scientific mindset: “There is some Midwestern pragmatism of just trying to get things done and looking and seeing what works and being committed to doing the thing that works not necessarily the thing that you wanted to do.”
Just as importantly, she notes the role of data in uniting teams across divides: “If they could both look at information and agree like okay this study makes sense...now we can talk about we might have different approaches to solve the problem but we can all agree on what the problem is and the data is.”
A defining hiring principle at Veda Data: “The one characteristic when we hire people that we ask our people and culture team to look for is folks that are willing to change their mind with new information.” This trait supports agility and innovation—both vital in a fast-moving, data-rich landscape.
Key Takeaways:
Reward open-mindedness and adaptability, not just experience.
Make data the neutral ground for debate and decision-making.
Question: What does the future hold for AI in patient access and healthcare personalization?
Gaffney is optimistic: “I think what we’re going to see is the dream of personalization of your healthcare working uniquely for you...more and more a reality every day.” For this to happen, patients must see tangible, positive impacts—making their lives easier, safer, and healthier.
Her advice to other founders: “When we do right by patients at the end of the day, we’re building the industry acceptance for all of us.” The technology must work for people, not just as an abstract tool, but as an enabler of better outcomes and equity.
Key Takeaways:
Personalized care is no longer a pipe dream; it’s becoming an operational reality, driven by robust, responsible AI.
The key to future adoption is delivering real, visible value to patients, not just to payers or providers.
Healthcare’s next leap forward will be won not by the flashiest algorithm or the deepest pockets, but by leaders who make data work for people—relentlessly, transparently, and equitably. Meghan Gaffney and the Veda Data (now H1) team offer a model: combine policy savvy, scientific rigor, and real-world empathy. Whether you’re building AI, managing teams, or trying to fix your own provider directory, take a page from their book: hire open-minded people, measure everything, invest in equity, and never lose sight of the patient’s experience.
For your team:
Audit your provider data.
Diversify your hiring.
Make open-mindedness a core value.
Tie every tech investment back to patient impact.
It’s not just the future of AI in healthcare—it’s the future of access itself.
<p>You know, the one characteristic when we hire people that we ask our people and culture team to look for is folks that are willing to change their mind with new [Music] information. Hello everybody. This is Cole from the American Journal of Healthcare Strategy, joined by a very special guest in the industry doing really tremendous work, Megan Gaffne. Megan, can you please introduce yourself and a little bit about where you've been and what you're working on now? Sure, Cole. Thanks.</p> <p>I am uh the CEO of Veta Data Solutions. So, we are an AI and automation platform working to build tools that help folks get access to care through finding the right information about providers and facilities. My background and kind of how I came to healthcare, I was in the policy space in Washington DC for just under 15 years and was there during the time when the ACA was crafted.</p> <p>saw a lot of really interesting perspectives from hospital presidents to patient advocacy organizations coming to DC talking about what the future of health care should look like for people. But one of the things that they didn't spend a lot of time talking about was the data infrastructure that would be necessary for folks like you and me to be able to find the right provider and access care.</p> <p>So, I saw an opportunity to go into the private sector and build these types of tools to help enable folks to get access to care to do that in an equitable way and hopefully save the industry some money along the way. Thank you so much for coming on. This is exciting because you have, you know, like you said, you you've been in that policy space 15 years. I mean, that's a long time. And then now you're in this tech space. I mean, you're winning awards for what you're doing.</p> <p>You've been featured in Beckers and Inc. 5000 and uh Ernston Young. So I mean you're doing a lot of great things in the tech space. Uh but then you have this health policy background. So it's so unique. I I have to ask real quick why uh go go into the tech space and was it challenging? I think there's a few reasons why.</p> <p>I mean you think of you know women in IT is is a disproportionately small area and then what's going on right now in IT with AI and ML and a lot of new technologies and a lot of competition. So what caused you to go in that direction? Yeah, I mean like a lot of entrepreneurs, it was a personal experience. So imagine 2009, healthc care is the only thing we're talking about in Washington on a daily basis.</p> <p>And I worked with senior appropriators in the house, which meant, you know, I was in a lot of really wonky rooms where folks had access to all kinds of data and information about the health system. I also had just had a baby and my daughter had some health issues and needed to find specialty care and I could not figure out how to get an appointment with the right specialist for her and get her treated quickly for anybody that was covered in my insurance plan.</p> <p>And I just had this epiphany that if I couldn't do it literally sitting in the halls of Congress as the entire healthc care universe is wrapping around me with lots of access to information, great insurance coverage, then nobody could do it. And so I think we've all had those experiences. You get on your health plans website, you're looking for a dermatologist or maybe a speech therapist for your kid and you're making phone call after phone call.</p> <p>I recognized at that time for me it was a frustration but for somebody who's managing really chronic health care condition or in a behavioral health crisis or maybe they're a mom or dad working a couple jobs and trying to make these phone calls in between the hours that they have to go to work each day can really be a barrier to access that people weren't thinking about. And so as I dove in to find that solution, I was not a technologist.</p> <p>And so I did what most people do is I started talking around the water cooler and had a tap on my shoulder from a colleague of mine who said, "Hey, I have a really good friend. He's an astronomer. He's building AI tools in radio astronomy and is looking for a new gig. You guys should talk." And it was that kind of hour, two hour long phone call where we realized that some of the tools that were being used in academic science could be leveraged to help people.</p> <p>And that was really the genesis of ADAT. And since then, um, I've recognized the role of folks like me running AI companies when we're talking about things like health equity and using AI in a responsible way that voices like mine really matter. And so that's what really keeps me up every day kind of getting out of bed, charging forward at the next data challenge and making sure that we're building tools that really serve everybody in our community. So that is that is so interesting.</p> <p>So eight years ago is when you really started with the the AI, right? And that was in what 2018 I think, right? No, a little bit earlier than that. Yeah, it was 20 uh 2016 2017. So AI was not really I mean you know in the news or whatnot and then interesting partner as well, right? It wasn't like a healthcare AI like trained person. There wasn't very many of those around. No, I mean it was it was definitely before it was like the sexy buzzword that it is today.</p> <p>Um but in academic science, supervised learning and machine learning were being used all the time. And so things that seem new to this industry were actually really well established. Um I can give you a little bit of an analogy. Have you ever seen the movie Contact? You know the telescopes, right? Jodie Foster. Oh, yeah. That's called the Very Large Array. It's down in New Mexico. It's a real working telescope array.</p> <p>And my co-founder built software that's used there and at other telescopes um all across the world. There's so much data that they're capturing off of these telescopes that they couldn't process it with people anymore. It was too much data to process manually. So, they had to start writing machine learning scripts to clean up all the errors in the information.</p> <p>So for a radio telescope, if you drive by and your spark plug fires or somebody's microwaving a hot pocket in the neighborhood down the street, it blows huge holes in the radio data, but they still have to try to do science anyway. They would manually clean that up.</p> <p>And what Bob did was he invented a software program that would recognize the errors in the data, automatically clean it up, and then knit those data sets together so that people could make discoveries about galaxies in the early universe. Wow. And we do the same thing with provider data today. Less maybe exciting than thinking about galaxy information, but it's human created data, right? People are typing into EMRs, they're making phone calls. There's lots of error that needs to be cleaned up.</p> <p>And so we build a system that can find the errors, automatically clean them up, and then take disparate data sets and link them together so we can get the right answer. So it's a very similar approach, um, but it was really radical just a few years ago. And now I feel like we're finally at a time when AI is being embraced. And so that's really exciting for us as a company. Oh yeah. Because then you now you're a bit of ahead of the game, right? Eight years ahead of the game.</p> <p>So that is so interesting too though. I mean I wonder how did you identify I guess that that AI and tech was the solution to this problem. Uh we didn't have any other way to do it is the answer because we were a small company. We actually got our hands on our first healthcare data set at a hackathon in 2016. It was just Bob and I that were there and um you know a sales guy that we brought along and and it was sponsored by Humanana.</p> <p>So we went to Cincinnati, we went to their uh lab that was there and they gave us some dental data. So, it was a provider directory for their dental system and we couldn't make 10,000 phone calls to try to validate the data and we couldn't dig into a whole bunch of proprietary claims records. So, we had to build something that was scalable and data driven.</p> <p>Um, we had the opportunity that we could create some training data, but it was really through that experience we realized, okay, we can make a lot of impact fast. what could we do if we had a couple years to develop this and some investment capital and then really build out this machine learning way of doing things. But uh you know it was necessity being the mother of invention is a saying for a reason and that was exactly it.</p> <p>That was the only way we could try to tackle the problem and the results we got were really good because you a lot of the work you've done it seems like has been before you had substantial funding. Um yeah, I mean we raised very reasonable amounts of capital until our series B in 2021 that was led by Oak. Um and I think you know for better or worse it was a function of a capital environment where people were still skeptical about AI and healthcare.</p> <p>They didn't know is this something that health plans or hospital systems would accept as a technology solution. And you know, we were a company that didn't fit a lot of the molds that investors on either coast uh looked for. Neither Bob and I have Ivy League degrees. Um Bob has a PhD, but it's an astrophysics. So like how does that work? It's not computer science. Um our company was founded in Madison, Wisconsin, not in Silicon Valley or in New York or Boston.</p> <p>And so we looked a little different than the mold that people had seen before. What it made us do though was build things in a smart way that were scalable. It made the technology more affordable for our customers and it helped us grow a company um that was worth investing dollars in down the road because we were building it in a way that was um scrappy and Midwestern kind of in its nature. I mean that's what I I love that too. I love because that's what AJ, you know, HS is.</p> <p>We're like lean, very fiscally conservative, really trying to to maximize our efficiency. And then as soon as we get, you know, clients, it really exponentially increases our ability. And so I love that. And then Humanana is one of your your larger clients as well. I think that's been publicized. Um, what outcomes have you seen that's been positive for them? How has this benefited Humanana?</p> <p>Yeah, I mean I think they talked about it very openly with us in their press release around Medicare Advantage. One of the things that matters most to seniors is when they need an appointment, they have to find care. And so the accuracy of their directories, especially for Medicare Advantage, is a huge um benefit for their seniors getting access to care and access to the right care. And so in addition to the data just being accurate, we're able to also enrich it with things like subsp specialty.</p> <p>So if you need a neurologist, you really need to know, does this person treat Parkinson's or are they treating nerve pain, right? Two very different outcomes. Both have a neurology background. So getting that kind of nuance and depth to the data really helps seniors get access to the right kind of care. And then it also saves them money because they don't have to use their team to do things like make outbound phone calls uh to figure out, you know, the provers's fax number.</p> <p>they can use that team to be responsive to what their members are looking for, answering questions from members, engaging with providers to make sure that they're doing the right thing for the members. So, all of that kind of focuses the money where it should be, back on the patient. Um, which was a goal of Humanas and we're happy to be a part of it. Yeah. No, and I've I've worked with members and patients for many years and and that is one of the biggest issues even for myself, right?</p> <p>I mean, we were talking about behavioral health earlier. It can be very confusing to identify behavioral health providers using almost any traditional database, right? Because it's like, oh, they accept this insurance, but it's only for this type of appointment and and so it's challenging. I do wonder how have you overcome some of those issues though with the different types of data, the different ways it's entered into different systems.</p> <p>I mean, I've seen a lot of this and it's kind of a mess at its present state, right? It's coming from all these different sources. Yeah. I mean, it's 100% a mess. So you are correct about that. I think our focus on measurement has been a really defining characteristic of our business which makes sense. We now have um over 10 PhDs from the hard sciences and they're used to measuring things over and over again and it's okay for them to get it wrong culturally. Right?</p> <p>You can publish a paper that says, "Hey, we used a telescope. we were trying to measure this galaxy and it turns out what we thought was a really cool signal was actually an airplane and you don't get fired for that in science. In a lot of industry jobs you do. And so there's this cultural sense of we try to get the data point right. We measure it internally and if it doesn't work that's okay. We just keep going back to the drawing board to measure until we get to the right level of accuracy.</p> <p>And that culturally has allowed us to kind of get past places where other people in this business have stopped before and said, "Well, we've tried these three different ways, now we're done." We I'll give you one example, and it's kind of funny. We have one former astrophysicist, very highly published. His entire job is to make sure that we can get the latitude and longitude on hospital buildings and outpatient surgical centers correct. That's it.</p> <p>whole job because sometimes the addresses are really strange when you get to, you know, not picking on um teaching hospitals, but they have really strange address combinations and things like that. And it matters when a patient's plugging in the data to Google Maps to try to navigate which parking lot should I go into? Is there a handicap access to that building? So, we're willing to invest in time and expertise in problems that I think other people think are maybe not a big deal. Yeah.</p> <p>Or that they've given up on getting exactly correct. And it's because of the scientific culture that we're really proud of. Well, and that's that's a question that I have though is is that these individuals are not necessarily the cheapest uh individuals to employ, right? I mean, you and and I know that you know, knowing your stance on an equity, I'm sure that you compensate everyone fairly. Um, I've seen, you know, most ratings and reviews of your company are very positive.</p> <p>So, paying everyone fairly, paying scientists fairly is challenging. How do you justify and how do your customers justify that kind of ROI on that investment? Yeah, I mean, I'll say two things. One is paying people fairly is expensive. Paying to constantly recruit and hire is also expensive. So the typical uh time an engineer stays at a startup is 18 months. I'm especially proud.</p> <p>One of the things that we've done since the very genesis of the company is we pay 100% of our employees healthcare premiums for them and their family for a no deductible plan which is an incredible investment but it means that our employees aren't worrying about health care bills at work. And so what we've seen is an incredible loyalty to the company because we value them and folks stay and they're able to build more because they have incredible institutional knowledge about the problem.</p> <p>You know, if you had somebody that's been thinking just about hospital addresses for two years, they can do so much more. And so we really think about it as an offset of other costs. And our customers appreciate the continuity, frankly, if they've been working with us. And a lot of our customers kind of have bespoke problems. Why does this doctor want to take my competitor's patients over mine, right?</p> <p>When they're booking appointments, we can help them with those individual problems and they really get to know the data scientists that they're working with and they know their businesses well. And so I think they're they value that as well and are willing to invest a little bit in our team in addition to the technology. Yeah, those are two really powerful things. I'd like to talk with you offline at some point too about covering the health premiums.</p> <p>I mean, from an economic standpoint, it's really interesting, right? Uh healthy workforces can work and then of course produce more things and so that's good. And yeah, so that's an interesting approach and and I'm really happy to hear that it's working. You are very committed to to health equity and equity in general. You talk about it a lot.</p> <p>One of the concerns that we see though is that AI and machine learning and data and technology can you know have privacy exposures to especially to minority groups. AI can be uh trained in a way that discriminates. I mean how have you overcome some of these problems? Yeah. I mean AI is a tool like any other tool and the machines are incredibly powerful but they do what they're told to do.</p> <p>And so what I mean by that is if you have an engineering and innovation team that is building with profit in mind and they're not thinking about bias and they don't have objectives set out for things like making sure the outcomes are equitable, you will get a profit only answer, right? Because that's what you've asked the machine to do.</p> <p>If you ask these tools to build things with equity and bias in mind and you put the right safety procedures and checks in place, you will get an outcome that reflects that intention. I think one of the most impactful things we did was we actually built our data science team to reflect the communities of people that we serve.</p> <p>That means we have women and people of color and unre under underrepresented groups on the team building the algorithmic approaches, interacting with the AI, building our safety procedures and our bias testing and they understand the impacts that those biases can have because they've seen them in their own lives.</p> <p>And so I think that really uh democratizing the people who are building these tools and having all parts of our community reflected in the teams can be a really powerful way to prevent uned or you know harmful outcomes. One of the things and I was actually just speaking with someone else about this and so I'll ask you as well.</p> <p>A lot of hiring managers like the idea of having more diversity on their teams, but when it comes to the actual hiring process, it's like, "Oh man, I got like 5,000 applicants." Yeah. I just want to use the AI screener and have the AI screeners go through, right? And so, it's kind of like they're in this position where they know they need to have diverse teams. They see the performance of diverse teams in their company and how it excels, but the hiring process is tough. Yep.</p> <p>Did you have experience with this yourself? I mean, I'm sure you did as a founder. I mean two things. One is it ask the question is who is building those AI screening tools right? Go and looked on LinkedIn see who's building them and if they don't look like the teams of people you want to hire it's an indicator that maybe that's not the right tool for you.</p> <p>I think the other thing is just you know I'm a woman I have an understanding and I've seen in the literature Harvard Business Review has study after study that in if you look at a job description and women will only apply and a lot of this tracks for people of color as well if they meet a 100% of the qualifications. Men will apply if they meet 50% or more of the qualifications. So there's a selection bias of people who will apply for your job.</p> <p>And so in order to get more diverse teams, you really need to do some outbound recruitment. But once you get those first few key employees, they will bring talented friends. They will make referrals and bring talented people to the company. But it is some leg work and just understanding that folks respond to job applications differently.</p> <p>And so if you really want that talent, you might be looking at the same level of talent, but one person will choose to apply and one will not based on their background. And so um some of the things that we did were really doing some of that outbound recruitment, incentivizing our employees to have referrals. Um we give a referral bonus. So if someone refers in someone and they get hired and they stay for a period of time, they get a bonus for doing that.</p> <p>And so that incentivizes people um to bring talent to us kind of outside of the big pipelines digitally that are available online now. And so we get a more diverse pool in that way. Wow.</p> <p>That is actually really So one of the things I've taken away from the conversation is that you as a person and now I'm sure of course you're co-founder and then throughout the company you kind of take a very scientific approach to things because a lot of people with the hiring process right like I said you just kind of type out the you know application or the description and you put it out there but you're actually thinking about the people who are going to be seeing the description how it's impacting them and then of course all of your data as PhD data scientists that are kind of assigned to work on it.</p> <p>Do you why do you think that you are that way? Is this something a personal experience that you've had or upbringing? What what has geared you to this kind of pragmatic approach? Oh man. I mean, not to be cute about it, I do think like I grew up in Cincinnati, Ohio. Like there is some Midwestern pragmatism of just trying to get things done and looking and seeing what works and being committed to doing the thing that works, not necessarily the thing that you wanted to do.</p> <p>Um was a part of my kind of cultural upbringing. It was that hard work will get you to the end um endgame that you want and you can't kind of be committed to a certain way of getting to the outcome. But I think the other thing is, you know, in a background in policy, I worked with a lot of members that were really bipartisan and in an age when, you know, now it's hard to imagine, but when I came to DC in 2005, a lot of the members that I worked with pretty much lived there.</p> <p>And so Republicans and Democrats kids played on the same T-ball team, so they talked a lot and they respected each other more. But one of the ways that really brought people together was data, right? if they could both look at information and agree like, okay, this study makes sense. We both believe that Harvard Business Review is a good entity to look at for labor market data.</p> <p>Now, we can talk about we might have different approaches to solve the problem, but we can all agree on kind of what the problem is and the data is. And I I think that's a very valuable way to problem solve in a company because it's not personal the different perspectives people are bringing to the table. We're all looking at the information and you know the one characteristic when we hire people that we ask our people and information. So we don't get kind of too attached to a perspective.</p> <p>We're kind letting the numbers show us the next place to go. Yeah, that is so that's important of executive teams and and teams everywhere. I mean, that's really a I think a really common roadblock we see is entrenched ideas over many years. And there's a lot of reasons. I'm not uh saying it's bad to have entrenched ideas, but in in this innovative age where things are moving quickly, sometimes we need to be be agile. And and that kind of follows up to the the last question.</p> <p>and I really appreciate your time today. Um, where do you think the future is going in the healthc care space in terms of data where with AI we're eventually going to run out of data and uh to train you know AI in the the greater you know uh data environment but specific to healthcare what what do you think is going to happen next? Yeah, I am an eternal optimist. So I will give you my optimistic view of the future of AI and healthcare.</p> <p>Uh because I think more people are going to run towards being a part of the solution to this challenge. I think what we're going to see is the dream of personalization, of your health care working uniquely for you and you being able to find the providers that are uniquely suited for you and the treatments that are suited for you is going to become more and more a reality every day.</p> <p>And I think when patients see the positive impact on their own health and wellness, they will start to embrace technology. But it has to work for them in order for that kind of future forward view and AI acceptance to really come to fruition. And it that's my advice to all other kind of founders in this space is when we do write by patients at the end of the day, we're building the industry acceptance for all of us.</p> <p>And so if we continue to deliver value to them and respect their privacy and deliver things that are equitable and really make their own health care better, we're just on a journey that's going to be a really exciting one.</p> <p>I I really like that approach to things because we've you know the fee for service model is no as we know it's no longer really working and it's eventually going to go away and so the the challenge is figuring out how can we make patients enjoy preventive care how can we make them value preventive care and the current system makes preventive care challenging right and your system what I love about it is it it makes it very seamless having that access to to data on the patients and it also helps the staff of industry insurance companies like Humana help their patients and members.</p> <p>So, that's a really great approach. Um, we're going to have to include that in the writeup for this because it's something that I've I've been trying to say for a while, right? Is we need to take like an Apple or an Amazon approach. Yeah. To our members experience. Really appreciate you coming on, Megan. I hope we can have you back on again. Anytime. I appreciate the conversation. Thanks so much.</p>
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