The Power Of AI-Driven Data Automation: Its Role in Reducing Administrative Burdens in Healthcare

Transcript 

[Greggory cave]    

And I’m going to start with my intro. So, I’m welcome to the Digital Transformers podcast, where we explore groundbreaking ideas and transformative innovations and healthcare, I’m your host Greggory Cave.   

And today we’re discussing the power of AI-driven data automation and its role in reducing administrative burdens and healthcare to guide us through this important conversation I’m thrilled to welcome Megan Gaffney the founder and CEO of Veda and Megan Am I saying that correctly Is it Veda or Veda, I never got it right. With over 15 years of experience navigating the intersection of technology and regulation, Megan is a fierce advocate for artificial intelligence and machine learning. She is also an award-winning entrepreneur and leader who drives impactful innovation.   

Megan Thank you so much for joining.   

[Meghan Gaffney]   

Yeah, thanks for having me.   

[Greggory cave]    

So, Megan, your journey from the political sector to founding Veda is inspiring and I’d love to start by hearing about what motivated you to create a company focused on humanizing data and leveraging AI to solve critical challenges in healthcare.   

[Meghan Gaffney] 

Thanks, Greggory Cave, my background was a little bit unique in my journey here to be an AI founder, but it started with my work in Washington DC, I was at a political consulting firm, and most of my clients were senior appropriators on the hill. This was during the early aughts as the ACA was being debated, crafted and put into action. It gave me a unique opportunity to sit in the room with regulators and patient advocacy organizations, leaders of health plans, and hospital system presidents.   

And it was exciting, it was a really exciting time for all of us and we talked a lot about what the future of healthcare should look like. But I saw a gap in the conversation, and it was around building the infrastructure that allowed a patient to find their right provider, meaning what doctor can help them in that moment and I was experiencing that firsthand myself as a new mom at the same time. Yeah, yeah, and it, you know, and that that pushes a mom to figure out how to fix this for your own child, how to get the right information to get your childcare was the motivation to move from policy into entrepreneurship.   

[Greggory cave]       

Wow. So, I mean, the early aughts, that was truly at the beginning, you know, there were I don’t think there were any EHRs or EMRs at the time, so it was the beginning, so you were really there at the right time. So, when we look at that and healthcare administration and healthcare administration is often bogged down by inefficiency, I’m curious to hear how VEDA’s AI-driven automation helps address these challenges and streamlines processes for organizations.      

[Meghan Gaffney]      

Sure, so I’ll talk a little bit about the problem that we solve as an AI company. As EMRs were getting put into place we started to digitize a lot of the information in the provider ecosystem but getting that information from the provider organization to the health plan was still and is still in a lot of cases a manual process. What that means for the patient at the end of the day is when they’re looking for an in-network provider, so they don’t get a surprise bill.       

It’s challenging for them to get the right information about what doctors are available that are taking appointments that can treat the condition where they need help so what we do is automate that translation and transport of data from the provider organization, the hospital or the physician group to the health plan. We serve as a kind of digital Rosetta Stone, we translate the data in between so the health plan can get it in front of their members faster to improve that member experience, but also to make sure that when there are appointments available patients can find them and that’s been a big challenge in the industry that we think is solvable with this kind of technology.      

[Greggory cave]       

Oh, it’s amazing and I mean that sounds like a lot is going on and the patient is not aware of any of this they’re just getting that seamless experience with the right information sounds like right.   

[Meghan Gaffney]      

That’s I mean that’s the idea behind all this innovation right whether it’s the EMR or data transformation that we’re doing a Veda or other kind of tech innovation it’s all about making this experience better for the patient at the end of the day, so they can get good healthcare fast. That’s affordable in a network with their insurance.       

[Greggory cave]       

Certainly, sounds like it. What kind of operational improvements and cost savings have healthcare providers experienced with solutions like betas?      

[Meghan Gaffney]      

Yeah, so it kind of spans the gamut from internal efficiency so, for example, you know folks that are using the Veda platform to help get that information from providers into that find a doc tool that you’re using to source a provider can do it 10 to 12 times faster, using our platform than they were doing that work annually before so it makes their jobs, a lot easier, and it saves some money in the process. It also puts health plans at a lower risk of getting sanctions or fines from Medicare and Medicaid. And at the end of the day and improves the member experience which helps them drive new customers right I think we all kind of know when you’re out there looking for a health plan, particularly if it’s Medicare Advantage or Medicaid and you can make an individual buying decision.       

You’re asking your friends, you’re asking your family members, hey, what plan do you use what’s that experience like? And so, we can see in the data, when folks have a good experience with this kind of information when they can source data quickly, it can drive up new members and a health plan and create more revenue for that plan that’s investing in member experience.      

 [Greggory cave]       

I mean it makes a lot of sense it sounds like there’s a ton of incentive to partner with VEDA and just, you know, all the green arrows are pointing up, so wow is this awesome stuff. I am machine-learning the buzzwords we hear everywhere in healthcare. What sets VEDA apart in applying these technologies to such a complex and highly regulated field is, that’s, that’s the big part.      

[Meghan Gaffney]      

Oh man, that is like the off-the-moment conversation right like the real AI what’s good AI how should it be used? I think there are a couple of things that set us apart, we were able to go into this market with a real perspective on how to use AI ethically. For us, that means making sure that our models are created in-house and we’re not sending data to any kind of third-party vendor that could result in a breach.      

We also can test and explain our models, so it’s not a black box it’s not chat GPT where you don’t understand why it’s making the decision that the AI makes. And so that helps healthcare companies feel comfortable with innovation because they understand why the models are making the choices that they make, this all comes from how our company was founded so I talked about my background. But my founder came from academic astrophysics, and we had a kind of scientific rigor, built in from day one and so now we have a team of former academic scientists that have about 80 years of AI experience amongst them.      

And they’re committed to making sure that we’re constantly testing our systems to ensure that they work the way that we say that they do, that we can understand how they’re working and so there aren’t any surprises for our customers. And then the last thing I would say and not to go too long here but no it’s fine, it’s fine, please, this is super interesting, go ahead. We just got granted our 10th AI patent, just last week.   

And so, what’s really exciting about the patent process is that we are founded by scientists. It’s a commitment to creating openness and transparency about what we’ve discovered and how it helps and so we’ve patented this information to put it out into the scientific ecosystem so that eventually others can learn from what we’ve built, and we can make healthcare better for everybody.       

[Greggory cave]       

You know that’s awesome because that reminds me a little off topic here but Mercedes years ago, which is a very stand-up company. When they started innovating different safety things within automobiles, they never patented those things they put them out there to let other people learn from them so that everybody could be in a safe car so obviously Veda is doing the same thing, and I think that’s super awesome. So, congratulations to you guys on that.      

And also, you know, for a layperson like me and others and maybe some people that may even watch this AI seems like it just happened yesterday right so it’s a daunting thing because we’re learning a lot about it and you’re hearing so many different things so you know implementing AI and healthcare, it must, it’s got to come with its own challenges. So, what roadblocks have you and everyone in Veda encountered and how has Veda tackled them?      

[Meghan Gaffney]      

Yeah, it’s such a good question because it can be confusing. It’s this broad umbrella term that encompasses a lot of different tools and techniques and I think our biggest solution to kind of AI hesitancy has been spending time with our customers and educating them about how we use these computational tools and understanding how AI can impact their business and how we can do it measurably so that they don’t get the surprises they’re nervous about.      

You know, I think the buzz in the industry sometimes does us a little bit of a disservice in that now we have people that are like, well, I need to embrace AI. And we always try to bring our customers back to this conversation of what are you trying to solve? You might need AI to do that.      

That might be the best solution, but there may be a better tool as well. And so, I think as we all get smart about what AI can and can’t do and what different kinds of models are well served for, we all also as tech companies must invest in the kind of customer service architecture where we help our customers make good decisions. Sometimes that means using our tools, but sometimes it also means telling them that you don’t need AI to solve this problem.      

There’s a better way to do it. And so, I think that level of like investing in service and conversation with our customers is something that, you know, tech companies have a responsibility to do to make sure that people understand where this can be used and where it’s ill-suited.      

 [Greggory cave]       

And I’m sure you guys didn’t even realize you had to do that piece, right? Because, you know, creating this model and this platform was large enough, and then you come in and say now you got to educate and consult and maybe recommend and kind of steer in one way or another. And that’s a responsible space to be.      

So, I’m glad that you guys are doing that. And you guys are at the forefront of that because hopefully, everyone is following. So, speaking of that human piece and making sure that everybody is doing it the right way, or at least is aware that, you know, the most you can do.      

VEDA as a company emphasizes humanizing data, right? And I’d love to know how that philosophy translates into real-world outcomes. I mean, we spoke about it a little bit, but, you know, whether or not those outcomes are improving provider-patient relationships or enhancing the decision-making processes.      

[Meghan Gaffney]      

Yeah, humanizing data has always been what we’ve been trying to do, right? That’s been our intention. Our tagline is that we build technology that helps people help people.      

And what that means for us is we’re always putting ourselves in the patient’s shoes. So, success for us means that the patient can access care in a better way than they did if VEDA wasn’t involved in that interaction. And so, I can give you a quick example of what that means for us.      

So, we think about behavioral health care a lot. It is a big challenge in a lot of our communities. And we have customers that are very heavily concentrated in the Medicaid space.      

So, you have folks that are in real need. And often when they go to look for a provider, they’re met with information about whether this is a licensed clinical social worker or this is a licensed professional counselor, but no data on what they treat. And so, if you’re struggling with addiction, you need to find an addiction counselor.      

If you’re struggling with an eating disorder, you need someone who specializes in treating eating disorders. But on the market today, there haven’t been companies to enhance the information to make sure that level of detail is there. And so, we focus on getting as granular as we can.      

Because if you’re facing an addiction crisis, you don’t have 40 phone calls to make through a directory to get that right. You want to be able to get them to an addiction counselor on the first call so that they can get help quickly. And so, you know, when I’m meeting with our data scientists and asking about, hey, let’s dig into our coverage on these subspecialties and behavioral health.      

I’m making sure to have a conversation with them about this is why it matters. And this is how it’s going to impact families like ours. And it really drives a desire to do better and to drive innovation forward so we can get the data in the hands of people in the way that’s most meaningful to their care.       

[Greggory cave]       

That’s amazing. I mean, that gave me chills listening to that. Just a quick aside, why do you think that is that you know, that granular information, as you said, hasn’t been, it just seems so obvious that if I have a certain issue, I should be able to get to that provider as quickly as possible.

Why do you think that’s never kind of been out there before? And I’m so glad Veda’s out there making sure that that’s being done.    

[Meghan Gaffney]      

Thanks. I mean, I don’t think it’s malicious. I think there, you know, we still haven’t gotten phone numbers and addresses right for where physicians work in a lot of health plans and provider organizations.   

They’re tracking it in a hierarchy and they’re trying to tackle things one at a time. So, you do need to get those foundational steps right first. Across industry, about half of the data that’s put in front of patients to access care is wrong.   

And so, I think there’s been a lot of focus and energy, but some folks just don’t get to that next level of detail on the provider. I think the other thing is that research is starting to come out that quantifies what these delays in care mean. There was a great study that the Kaiser Family Foundation did in the fourth quarter of last year that looked at delays in care due to these data issues.       

So, it doesn’t have anything to do with co-pays or the ability to afford care. These are for people with health insurance who are trying to seek care. And it resulted in $24 billion being spent on emergency room visits that could have been seen in an outpatient setting because they couldn’t find the right data.     

And so, I think now there’s some more awareness in the community about what that means. What that also means, and what that study showed going a step further, is that 57 percent of those folks had negative health care outcomes because they couldn’t find care quickly and they didn’t have the data to do that. And so, as the healthcare industry starts to understand how bad data is impacting medical costs, I think you’ll see more and more focus on solving these problems because they can see the real impact on care at the end of the day.    

[Greggory cave]       

Wow. Thanks for that insight. It makes sense, and it all ties together as far as patient outcomes and the quality of care is concerned.     

So, thanks for that. It really gives me a lot of perspective there. So, let’s talk about the impact.    

Can you share an example of how VEDA specifically, how your solutions have made significant differences for a healthcare organization or a significant difference in general?      

[Meghan Gaffney]      

I love talking about that. I’ll give you an example. So, CMS comes in and they audit health plans fairly frequently, but on a kind of random basis.      

You never quite know when one’s coming. We worked with a large Blue Cross organization that got a 43 percent accuracy score back from CMS, which is a kind of red alarm fire when you get those sorts of results. They were looking at how many people they would have to hire to go in and try to manually fix this problem.      

And they were looking at adding 20, 25, 30 headcounts onshore to try to manually correct these errors. And they weren’t even sure if they could accomplish it because the data problem was so massive. I got a phone call.      

They said, hey, can you come and help us out? We did an initial assessment and then we were able to create a 97 percent improvement in their accuracy score.      

[Greggory cave]       

Wow, that’s awesome.   

[Meghan Gaffney]      

Right, with millions of dollars of operational savings and also some great benefits that we weren’t predicting. Things like a reduction in direct mail costs, a reduction in call center calls, which like looking back makes sense if the data is right, they don’t have to call and ask you, how do I call the doctor? And a reduction in out-of-network payments because they were able to find in-network providers.      

And so, you know, those kinds of impacts are the ones that we love when somebody has this aha moment of holy cow, we need to fix this problem and we can get in and really work with them quickly to solve a problem that both saves money and improves the experience for their members.      

[Greggory cave]       

Yeah, a win-win situation. You can’t beat that. That is an awesome story.      

[Meghan Gaffney]      

There aren’t enough of those in healthcare, right?      

[Greggory cave]       

Unfortunately, not. So, as AI and automation continue to evolve, what do you see as your next major innovation in reducing administrative burdens in healthcare?      

[Meghan Gaffney]      

I mean, there’s so much work to be done. This industry is ripe for all sorts of innovation, but for our place in that process, it’s broadening the reach of provider data that we touch. So, thinking about how do we innovate in the retail pharmacy space?      

How can we help bring these solutions to the dental market? And then helping hospital systems connect in this more seamless way with their payer partners. And so, we’re adding features and tooling to continue to make things like provider onboarding and enrollment more efficient.      

I think the biggest motivator for us in this process and the thing that we want to continue to solve is that we still see this delay in care. And so, as we think about the features we’re adding, we’ve added new information on telehealth providers, for example. We continue every day to work on adding information about behavioral health access to help our health plans who want to add more providers find the right ones so that they can add in the kind of behavioral health support that their members need.      

So, it’s exciting. There’s a lot left to do, and we’re looking forward to continuing to broaden our impact.      

[Greggory cave]   

No, it sounds like it. And I’m glad to hear that because you guys are providing such a well-rounded solution that’s needed. And the fact that it’s expanding and you’re still finding opportunities just means that, like you said, there’s a lot to do.      

But it’s great to have a company like Veda out there that can take on those challenges and meet them head-on. So that’s awesome. So, Megan, was there anything else you wanted to mention to the listeners and anything about Veda or yourself that you’d like everybody to know?      

[Meghan Gaffney]      

I mean, I will just leave folks with this, AI can be a positive force in making health care work for people within the right ethical framework. And so, there’s a lot of fear and concern in the news that’s mixed up with all of this excitement. But over the past 10 years, we’ve seen such an incredibly positive impact when you’re doing it right.      

And so, I just leave it with folks to think about what doing it right looks like and keep their minds there, because there’s a lot that we can do to make health care work better for people and the future can be really exciting.      

[Greggory cave]       

I think that’s an awesome point, because, again, for a layperson, we think AI just started last year or something. And, you know, we’ve all just got to recognize that it’s been out there for a long time. And companies like Veda have been taking it head-on from an ethical kind of standpoint and making it matter.     

And that’s so obvious in this conversation. So, Megan, thank you so much for joining us today. Your insights into the role of AI and automation and healthcare administration have been incredibly valuable.       

I appreciate that. And Veda’s approach to transforming data into actionable solutions is making a huge impact. And I can’t say that or underscore that enough.      

So, to our listeners, thank you so much for tuning in. Stay with us for more episodes where we dive into the technologies and innovators shaping the future of health care. See you next time on digital health care transformers or I should say see you next time on digital health transformers.       

Thanks a lot, Megan. I appreciate the time. It was pretty awesome.        

[Meghan Gaffney]      

Thank you.