VEDA is getting patients the information they need and directing them to the right type of care.
In this episode of Bite the Orange, Meghan Gaffney, CEO and co-founder of VEDA Data, talks about how she’s working to innovate healthcare data infrastructure and make it work better for providers and patients. Thanks to outdated databases, finding providers on your health plan can be a difficult process, causing delays and driving up costs in healthcare. Meghan explains how VEDA Data has two different offerings, Velocity and Quantym, for health plans to improve speed and efficiency in their data infrastructure so their members have access to complete, built-out networks. She discusses three case studies around automation and accuracy improving data quality, user experience, compliance, and cost savings, which can be found on VEDA’s website.
Tune in to this episode to learn how Meghan’s work at VEDA is helping health plans innovate and improve their data infrastructure to provide better care!
FULL EPISODE
BTO_Meghan Gaffney: Audio automatically transcribed by Sonix
BTO_Meghan Gaffney: this mp3 audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.
Emmanuel Fombu:
Welcome to Bite the Orange. Through our conversations, we create a roadmap for the future of health with the most impactful leaders in the space. This is your host, Dr. Manny Fombu. Let's make the future of healthcare a reality together.
Emmanuel Fombu:
Good morning, good afternoon, good evening or good night, ladies and gentlemen. Thank you for taking the opportunity, taking the time to listen to another episode of Bite The Orange. And today we have a very special guest coming all the way from the Washington, D.C., Baltimore area. That's an area that I personally spent most of my life, and my mom lives in that area as well, so this is a personal connection for me, but the special guest has a growing list of accolades, including being named the 2017 Veneta Project Venture Challenge winner. She's also the winner of MedStar Health and 1776 Patient to Consumer Startup Challenge. And she's also been named Startups to Watch in 2018 and also in 2020, in 2022 myself, in 2023 and 2024 right ... by me and, but also 2018 by DC e-now and DC ... life, right hard entrepreneurs in DC tech and business community. Today I'm honored to have Meghan Gaffney on the show. Welcome, Meghan.
Meghan Gaffney:
Hi, thanks for having me.
Emmanuel Fombu:
So Meghan, you are the CEO and co-founder of a very interesting company called VEDA Data, correct?
Meghan Gaffney:
You got it.
Emmanuel Fombu:
So tell us something about yourself that would go into, what, how you got into this. So who is Meghan Gaffney?
Meghan Gaffney:
Yeah, I am a recovering political entrepreneur who got really obsessed with how to make healthcare better during the creation of the ACA. And so that has led to becoming a founder at VEDA, and now the CEO of a company that has 100 employees both in the US and in Ireland and really working to innovate healthcare and make it work better for people.
Emmanuel Fombu:
Which is very interesting. This is one of the great examples that I love, right, that we get in healthcare. And as a clinician myself, I mean, I'm a doctor background, but I say healthcare is not only for doctors. So I take people with different backgrounds. It could be politics, it could be engineering, it could be PR, whatever background it is, right? It takes all of us a collective effort to make healthcare better. So with that being said, what was the problem that you wanted to solve? What is it, that why what is the reason that got you involved in this?
Meghan Gaffney:
So back in 2017, I was thinking about these ideas around healthcare infrastructure. I come from a political background. There's always talk about infrastructure in Washington, D.C., and investing in infrastructure, roads, and bridges to make things work better. And that was true in a lot of industries, but it was something that I felt was left out of the ACA process, meaning that there were investments in new technology and innovative ways to deal with data that could bend the cost curve, but it just wasn't happening from a policy perspective. And at the same time, I was a mom of two young kids, and so you can imagine going. Yeah, thanks. They're not young anymore now. They're kids with big opinions. But, you know, making doctor's appointments, trying to find providers, particularly specialists that were taking patients on my health plan, and being able to get access to that information quickly, and as a mom, it was really hard to navigate. And so I had this kind of wild idea that kept sticking in the back of my head. Could we take the same techniques that were reinvigorating the financial services industry on the East Coast and the consumer technology industry on the West Coast and apply them to healthcare's simplest, and in a lot of ways most impactful, data problem? Just getting patients the information they need about providers that are in their insurance network quickly and direct them to the right type of care. And that was a problem that, really, I had experienced and knew well, and luckily, found a great business partner who came from the field of astrophysics, who knew AI and machine learning really well, and we started tackling that problem, and it's still the problem we're focused on solving today.
Emmanuel Fombu:
And that's another great reason why people from different backgrounds actually meet. So it takes a politician and someone with astrophysics background to come together to solve healthcare's problems, right? So that's the beauty of it. And so with that being said, so for the average person that has no idea about our industry and it's pretty chaotic, right? And I know you have your target clients are payers and so, why do payers care about you? What does it matter to them?
Meghan Gaffney:
Yeah, so you know, payers care about their members as consumers. So if you think about Medicare Advantage plans, their members have lots of choices now of where they can go to get coverage. And one of the first things that a member looks for is they go on the health plan's website and they're looking to see, are the doctors that I trust and the facilities that I have faith in, in that work, accepting patients on this health plan? Do the phone numbers work? Can I get access to the care that I need? And what we've found is that in the industry, about half of the data is wrong and it's driving member satisfaction, it's driving just enrollment, and there's been a lot of research that's come out both from the Journal of the American Medical Association and from Yale Public Policy Institute, showing that it is affecting members' health because they're delaying preventative care and appointments that could treat things earlier because they can't get access to the provider information, and so that's driving up costs. So health plans are really looking at this from both perspectives. We want to control costs, obviously, but we also need to see members of health plans as consumers that have choices, and serving them well through data is important for retention of those customers.
Emmanuel Fombu:
Which is a, I like the fact that you mentioned that part, right? And because everything you mentioned impacts compliance ... ratings and claims fallout, right, leading to poor member and provider experience across the board. But you have, so you target Medicare, and Medicaid, and commercial health plans, so those are some of the plans that you go after. And so, and you have two key products that I looked at, I think you have Velocity, right, and is it Quantym? Which I like to ... is quantum ... why, so quantum, so a lot of physics in terms of velocity and quantum, so quantum physics.
Meghan Gaffney:
We have to give, we have to let my astrophysicist co-founder get a word in sometimes, so he gets a say in the product naming.
Emmanuel Fombu:
Talk about diversity of opinion, that really matters.
Meghan Gaffney:
That's right.
Emmanuel Fombu:
It's like, it's so great. So tell us about the two key products that you offer, right? So what is Velocity and what is Quantym?
Meghan Gaffney:
Yeah, so Velocity is our product that's focused on speed, not surprising. And what we're trying to accomplish for health plans is to help automate the movement of data from the provider all the way through to the member. So if you think about your physician, you probably experienced this at some point. The group that you work for is sending information to health plan, Hey, you know, Doctor Manny just joined, here's his information, get it in front of the members in the directory so they can make an appointment. Today that process is typically done by people. So the health plan is getting a spreadsheet or an entry into a system or a platform, but they have to manually then take that data and enter it into their claims management systems so that that provider could get paid eventually and manually enter it into the system that feeds their Find a Doc tool on the website. It can take weeks for that information to get in front of members, and it's really error-prone because it's a lot of stare-and-compare someone looking at one screen and typing it into the next screen. What Velocity does is it takes all of that manual process and automates it from end to end. So we can ensure that within 24 hours of the health system, sending the information about the new doctors that join and the new access points that are available, that information can get in front of the member. So it's really a real-time information sharing. And the brilliance of the product is that it doesn't require the doctor or the health plan to change their tech stack at all. It lets them automate in place and use the technology that they're using today, so we can get really quick results and get people the information they need to make that appointment with the doctor.
Emmanuel Fombu:
And just expand that piece to make it basic understanding. This is like joining a company and it takes like that long for you to actually be posted that you actually work in a company, right? Which is like, basically common sense things that you're thinking of functions in the regular world, right? And so that probably makes sense. I don't understand why anyone should not be doing this.
Meghan Gaffney:
That's right. I mean, I can open a new bank account on my phone and make a transaction in like an hour and a half, right? It's so fast in other industries, and so what we're trying to do is create these similar automations that help healthcare catch up and meet the expectations of the modern consumer.
Emmanuel Fombu:
I'll just expand on that piece. Sometimes, for example, you could join a health plan and you go on, let's remember you're trying to find a clinician and you might say, Hey, there's no clinicians around where you are, but they might actually be a new clinician that showed up, right? And you are spending some time traveling out of place when just a delay in that process, just what Velocity does.
Meghan Gaffney:
That's exactly right, it gets that information out there quickly. And if a clinician moves or they get a new office, that information is updated right away because the worst-case scenario is you make an appointment, you drive to the wrong location. We had a customer at one point that told us a horror story of someone who went to a urgent care that had been torn down. So they went to go get care and they went to an empty parking lot because the data was so bad and delayed, in the website that they hadn't updated that information, and so those are the kind of problems we're solving.
Emmanuel Fombu:
Something, apply, which is great, that's a good example which made me think about something. Is it something? The same thing happens if someone retires or a clinician dies? Because I look at data sets before and I've seen patients being referred to a doctor that died five years ago, ten years ago.
Meghan Gaffney:
Yeah, it happens, and it happens in referrals, I'm glad you brought that up too. I mean, we're starting to work with ACOs and expanding out our services for things like referrals, particularly things like behavioral health referrals. You want to make sure if you're referring someone out as a primary care provider, that the information is correct for the universe of behavioral health providers. You don't want to send them to someone who's not taking patients or delay that care because there are really bad outcomes that can happen when that process is delayed or potentially not completed at all because the data isn't there to help get a proper referral made. So those are the kinds of things where we think in the past data has been a barrier to people getting what they need. But now it can really be an opportunity to make healthcare work better.
Emmanuel Fombu:
That's a no-brainer. I think anyone listening to this should definitely Bite the Orange in this and support Meghan and the team on this, right? So I think that's a straightforward thing because it makes sense. And so now let's talk about Quantym. So we need Velocity and the speed, I think the name is well-earned. So let's see the Quantym earn its name.
Meghan Gaffney:
Yeah, so I can give you, from a physicist perspective, Bob, if he's listening, is probably cringing at me trying to explain quantum mechanics. But really it was a change in thinking fundamentally about how the world works. There was one way that was pretty simple and easy to understand that were traditional mechanics in physics and then you had this crazy quantum mechanics stuff where you have atoms and particles that are vibrating and you have to use probabilistic methods to understand where they are, scared people, it was uncomfortable, but it made things like GPS possible. So in the realm of healthcare data, we really think what we're doing is shifting from a method of data cleansing that's focused on process to a method of data cleansing and augmentation that's focused on outcomes. And I'll give you an example of what that means. So we're trying to assess whether a provider is taking patients at a location on any given day in time. There isn't a good, simple source of information, including a call to that provider's office, unfortunately, that can get you the right answer. But we've been able to show that using supervised learning models that have been developed over the course of the past few years, that is a complicated way to get to the answer, but we can get the right answer in a reliable way that's mathematically and statistically sound for our customers. And so that's what our Quantym project, our product does. It provides reliable answers about the quality of provider data in really large data files every day. So we can cleanse and augment and edit the entire provider network file for a national health plan in 24 hours. So again, getting back to the speed that we have in Velocity, you still get that with Quantym. We can process anything up to 80 million rows of data for a multitude of data fields, give back a statistically accurate view of that provider data, make corrections, get it to compliance levels of accuracy all within the same 24-hour period. So it's a really impactful product that allows claims to be processed accurately, it allows health plans to see gaps in their network for the first time that they didn't realize that they had. Members, patients have known there are gaps, but health plans had a hard time seeing it because there were so much erroneous data within their network file, they just didn't know. And so now we can make that available so that health plans can be compliant and members have access to networks that are built out and complete.
Emmanuel Fombu:
That's incredible. I think it's, between Velocity and Quantym, I see speed and efficiency to the health system, right? Just the back office thinks that, this is not the sexy things to think about in healthcare, right? But it makes it very efficient and improves that member experience and provider experience overall. With that being said, I'm very interested in like three case studies that you actually published in the website, that anyone can go on your website, and we share information at the bottom of the podcast, anyone could check it out, but you have three main case studies that I'm very interested in by multistate payer, the multistate payer achieved a zero roster backlog, which is a product, right? And there's a large BlueCross BlueShield plan that achieved data accuracy of 99%, which a product as well. And then you have, you partnered with the mid-Atlantic BCBS, which is BlueCross BlueShield to save millions in administrative costs. So what you're saying is not just, you know, this futuristic thinking piece, you've actually partnered and actually delivered on this. So explain, talk more about some of these case studies that you could share with this, and also tell us about what your ideal customer or client is.
Meghan Gaffney:
Yeah, thank you for bringing those things up. I mean, being a company that is grounded in science and the scientific method, we measure everything and these are some of the reasons why we can get these great results partnering with customers, is because we're really focused on measuring the outcome of what we do. So in the roster process, the customer that we had worked with had the proverbial stack of papers on their desk. They were processing these rosters manually. What it meant was there was a delay in the providers being shown to members. What they also found was that behavioral health data wasn't getting a look at all because they were focused on the Medicare-required providers, so folks like primary care providers, cardiologists, etc, and so they never got to the behavioral health data. We were able to create a process that fully automated the roster coming from their provider groups. And it did two things for them, it fundamentally changed the quality of the data that their members were seeing, and it allowed the people who were doing the manual work on physician data to move and shift, to focus on ensuring that they were getting the right information about their behavioral health providers, which had a huge impact on their members. And so that was something we were able to facilitate in partnership with them. On the accuracy side, there's really two big drivers here. In the second two case studies, I'm going to blend them together a little bit and talk about how these two things align, so why data quality means cost savings. We did a large validated study by a third party who looked at the quality increases and what data was able to provide, and particularly on credentialing fields, things like licensure, the status of the state license, the specialty that the doctor or other healthcare practitioners, so we don't just work with doctors, nurses, advanced practice nurses, PTs, OTs, etc. We were able to get very high rates of accuracy. Like you said in that study, it was 99% credentialing accuracy. And what that means downstream is that you get fewer claims that fall out to manual workflows, you don't get returned mail, which is a surprisingly large cost, it's a six-figure cost for this health plan in mail that they were getting returned. Sometimes checks to small providers, which you can imagine is really frustrating. And you also get fewer calls into the call center, because when members can self-serve us the information they need on the website, they don't have to make calls into your call center to ask those questions. And so good data provides great experience, it provides compliance, but also downstream cost savings.
Emmanuel Fombu:
Which is quite incredible. So right now, so your main customers, so your main, you sell mostly to health plans, correct?
Meghan Gaffney:
Yeah, we focus on health plans first because that was really where members were going to get this information most of the time, if they hadn't given up and gone straight to Google or calling their friends and neighbors to try to figure out where to get care, that's where they were going first. And health plans have a compliance interest in getting this right. We focused first on Medicare and Medicaid, but we found the provider experience, and for provider operation, was providing challenges for commercial plans too. Even if they didn't have compliance requirements, their members weren't happy, but the providers were also unhappy because they were getting paid too slow and they were having challenges with their customers, patients finding their information to make an appointment. That's not a great experience if your partnering with a health plan, no one can make an appointment at your office. So now we've broadened out, we serve the entire health plan ecosystem and are starting to provide offerings to others like ACOs, particularly value-based care organizations within a health system because the data is so crucial to getting that right. You can't do value-based care if you don't have the data fundamentals at a really strong place and have that trust built between the health plan and the provider organization. So that's where we're going, so we're hoping more and more people will take the bite out of the orange.
Emmanuel Fombu:
Oh, yes, definitely. I think you're going in the right direction. And I think this is better, a lot of clinicians will bite the orange just listening to what you just said, right? Because like in clinical practice, I'll tell you, a lot of my colleagues, what they bring up is by dealing with Medicare, Medicaid is how long it takes for them to get paid, right? It's a massive challenge, and add that to physician burnout rates today. I think what you're doing makes it more efficient, that makes everyone more comfortable. I think that translates to a better care for patients overall, what you say, right? So with that being said, what has been the biggest challenge that you've faced so far? And it sounds great than what you've done, right? I think getting, even get one, yes, so tell us about it. Our challenge is to get one contract deal with the ..., it's not easy.
Meghan Gaffney:
It's not easy, I would say the thing that is trickiest and actually, it's kind of perfectly encapsulated by this bite-the-orange analogy that you've given everyone who's listening is, building the trust that's required to get a healthcare organization to move from a manual process to an automated process. I often describe it to our implementation teams as you're asking somebody to get in the back seat of a driverless car. Even if the data says that it's safer to be in the back seat of a driverless car than with a human driver, it is a scary choice and a scary moment of transition. And so we learned the hard way through the early years that we weren't focusing enough on that change management and education and trust building, and we fell down at times because of that. We lost deals because we didn't focus on providing enough reporting, enough transparency, enough human support in this process to make it worth it for our customers. And so we've really doubled down on that in the past two years, bringing on a new vice president of delivery who is with our customers from pre-contracting through the life cycle. They do implementation, but also client experience work to make sure that there are human eyes on the data and we're able to explain to our customers what's happening in the automated system in a way that builds trust over time. And so I think that's a lesson that a lot of automation entrepreneurs will learn is that the change is difficult for people and you have to meet them where they are and help them understand the value and keep a human relationship in that process, because it is asking a lot to put this really critical data in the hands of machines, so we need to have people engaged in the process and the service of those customers along the way.
Emmanuel Fombu:
And that's great, so just as we wrap up, I think it's also great, I think you mentioned earlier you have about 100 employees now with a very short span from when you started to where you are, and you are in how many countries.
Meghan Gaffney:
Two. We are in the US, and we just opened an office in Dublin, Ireland, so, yeah.
Emmanuel Fombu:
That's right, Dublin, Ireland, so that's fantastic. So you're growing and so everything starts with a seed, then you expand to the markets. So where do you see yourself going in next year or next years? Because I would love to have you on the show, of course, and check your progress and see how you're doing. So where do you see yourself in the next year?
Meghan Gaffney:
Yeah, in the next year, I mean, we've got some really exciting things on the horizon. We are going to continue to expand our product offerings to offer more for a broader array of customers, more data points, more automation. And I believe data will continue to be a thought leader in the automation space, and putting the member or the patient at the center of the automation experience. It's not enough for us as entrepreneurs to think about back-office savings, we need to think about how the tools that we're building impact our communities. And so I'm confident that we'll continue to lead that conversation as we grow both here and now, expanding over into Europe as well, and always happy to come and get your point of view on how things are shaping up and really would be happy to check back in a year from now and talk about how we're both doing and growing, your audience and our company.
Emmanuel Fombu:
I think a year is too long, which I can, even sooner than that.
Meghan Gaffney:
Yeah, maybe I'll see you at HLTH. I don't know, I don't know if you're headed there, but we definitely are.
Emmanuel Fombu:
I'll be on HLTH and I'll be in all the other conferences. And, you know, the lines is always open, you know? And so if you listen to the show, please, and you listen to what Meghan said, you believe in what Meghan does, hashtag #Bitetheorange, hashtag #VEDAData. We have Meghan's contact information right below the podcast. Please share your knowledge with her, connect with her on LinkedIn or wherever you want to reach her. Thanks for joining us today, Meghan.
Meghan Gaffney:
Thanks, Manny. I appreciate it.
Emmanuel Fombu:
Thank you for listening to Bite the Orange. If you want to change healthcare with us, please contact us at info@emmanuelfombu.com, or you can visit us at EmmanuelFombu.com or BiteTheOrange.com. If you like this episode and want more information about us, you can also visit us at EmmanuelFombu.com.
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About Meghan Gaffney:
Meghan Gaffney is responsible for making the Veda experience exceptional for our team members, customers, and partners. She provides resources and strategic leadership to our employees and serves as a liaison to customers and partners. With over 15 years of experience working with federal and state elected officials and consulting on technology opportunities, she has experience helping people—from elected leaders to impact organizations—achieve their goals. She is a passionate advocate for Artificial Intelligence and Machine Learning and believes it will create unprecedented opportunities for the United States and the world. Meghan has a growing list of accolades, including being named the 2017 Vinetta Project Venture Challenge winner, winner of the MedStar Health and 1776 #Patient2Consumer startup challenge, and has been named Startups to Watch in 2018 by DCInno and DCA Live: Red Hot Entrepreneurs in DC tech and business community. She is a contributing author for Entrepreneur Leadership Network.
Things You’ll Learn:
Healthcare data infrastructure was mostly left out of the Affordable Care Act process, meaning that there were investments in new technology and innovative ways to deal with data that could bend the cost curve, but weren’t covered from a policy perspective.
In the industry, about half of the data is wrong, which is driving member satisfaction and enrollment down as this causes delays, hinders access, and drives costs up.
Velocity is VEDA’s product focused on speed, helping automate the movement of data from end to end.
Quantum is VEDA’s product focused on quality, processing up to 80 million rows of data for a multitude of data fields, giving back a statistically accurate view of it, making corrections, and getting it to compliance levels of accuracy.
Good data provides a great experience and provides compliance, but also downstream cost savings.
For a healthcare organization to move from a manual process to an automated one requires a great level of trust.