← Becker’s Healthcare Podcast
Becker’s Healthcare Podcast
Ratnakar Lavu, Chief Digital Information Officer at Elevance Health
00:21:27
Loading summary
Transcript23 lines
- [00:01]
A
Today's episode is brought to you by Abridge. Named Best in Class for the ambient AI segment, Abridge is the leading AI platform for clinical conversations designed by clinicians and scientists to meet the diverse needs of the largest and most complex health systems. Abridge automates clinical documentation in real time and integrates directly within the electronic health record workflow, reducing the burden and distraction of repetitive administrative tasks across care settings, specialties and languages. A bridge has been shown to reduce burnout by 55%, improve clinician job satisfaction by 85%, and significantly increase patient satisfaction scores, according to surveys from Press Ganey. Bring this impact to your health system today and join the community of users across leading health systems, including Duke Health, Emory Health and John Hopkins Medicine and Mayo Clinic. Visit abridged.com to learn more today.
- [01:02]
B
Hello everyone and welcome to the Beckers Healthcare Podcast. I'm Scott King, joined by a very, very special guest today, Ratnakar Lavou, Chief Digital Information Officer with Elevance Health. Ratnakar, thanks so much for joining us. Really excited to have you. How are you doing?
- [01:18]
C
Good, Scott, thank you for having me here today. Really excited to talk through things with you.
- [01:25]
B
We have some big things to get into in the payer forefront and in healthcare and technology. But before we dive in there, can you please just tell us a little bit about yourself and your background?
- [01:36]
C
Yeah. So my name is Ratna Carlovu. I'm the CDIO here at Elevance Health and I've always been in technology and really focused on how we can leverage technology, data and AI to simplify actually consumer experiences and member experiences. Here I'm really focused on simplifying health care to improve outcomes, leveraging technology, data and AI. We're embedding AI responsibly across all our operations, not just as an experiment, but we're doing it at scale to make care more affordable, accessible and personalized. Scott. And so that's what's actually really exciting about the work that we do here at Elevance Health.
- [02:23]
B
Yeah, I feel like in health care we're hearing just more and more about how vital, especially on a payer side data is and then across the board in the industry, AI and the uses there and we're just tapping into that. But what do you have your eye on right now as far as opportunities go?
- [02:41]
C
Well, Scott, you know, health care is very complex, right? And every one of us who interact with healthcare there see some of this complexity, whether it is trying to understand the benefit, trying to connect with the provider to get care and really focused on kind of, you know, the healthcare outcomes itself or the clinical outcome. So in elevance health we're really focused on three things. One is simplifying kind of the member experiences itself. We want to create personalized and more seamless experiences. We want to actually simplify the care provider experiences. Also the interoperability. We want to empower care providers to improve health outcomes. Then finally we want to simplify how we work to actually better serve our customers and members. I'll give you a couple of examples. Let me start with the member or the customer example itself. We have something called proactive member engagement. And what this is is it's actually an AI driven tool wherein we identify members who could benefit from an outreach to proactively address care gaps. So we study the members, their clinical data, we understand who could actually benefit from closing care gap. We do an outreach to them to explain to them what will be beneficial as they think about going to their care provider, what will be beneficial for them to address so that they can actually have that meaningful interaction with their care provider with a lot more information. And we do millions of these outreaches today and it's the AI, the data and then the kind of the intelligence that we build that enables that to happen. The other example is when members actually call in to our call center. We provide our call center agents with the intelligence to be able to actually address the member issues. So we correlate all the information, the clinical data, you know, kind of their claims data, their benefits data and automate the things for our call center agent. Not only that, but once they finish the call, we actually do a post call wrap up which is we use AI to understand how the call went, how did our members feel, what were their issues. And we do millions of these post call wrap ups every month. And the reason for that is we actually understand the sentiment of the member so that we can proactively address that. The reason these examples are important is because we are very focused and obsessed about the member experience, member interactions itself and I'll then talk a little bit about the care providers. So in the care providers we want to make the interaction between us and the care providers easy. So within umai where they actually kind of submit their prior on, we actually look at what they've submitted, analyze it with the data that we have. And our entire goal and objective is to approve things as fast as we can for the care provider. And we use a lot of AI to look at medical record, to look at their prior art submission, to look at the members benefits data and orchestrate that approval in a more seamless way for the care provider. And then the other thing is the care providers actually submit a lot of claims. Our providers who submit a lot of claims, sometimes those claims are not complete. And so we have applied AI to look at those claims and see where the information is not complete. And the AI actually help in completing that information so we can process the claims on behalf of the providers. Why is this important? Because a lot of care providers spend a lot of time in administrative work and we want to take away that or simplify that administrative work for them so that they can really take care of our members and close those care gaps that are extremely important for our members. Now let me come to how we are simplifying the work itself for our associates. So we're deploying a lot of AI productivity tools so that we can automate a lot of their workflows so they really can actually service our members and providers. So we have over 52,000 associates today that are leveraging some sort of AI capabilities within their workflows. But we are also rolling out now chatgpt across the enterprise. So not only is it that they can do their work more easily, but they can also kind of understand leveraging these ChatGPT and other capabilities to be able to serve our members effectively. And then not only are we rolling out these tools, we're actually going to take them through AI certification working with OpenAI. Because one of the things we find is it's not just about the tool, it's our associates need to understand how to use those tools, what it means to leverage AI to actually do their work better. And that's where we're focused on quite a bit with our associate. So again, to recap, we want to actually create a more personalized and more seamless experience for our members. We want to empower our care providers to improve health outcomes and then we want to simplify how we work to better serve our customers and members itself.
- [08:36]
B
Incredibly fascinating. A lot of so much great information in there. Rat in a car. Let me ask you, I guess, to play and to play devil's advocate a little bit. You know, even the most practical uses of AI the average person uses, there's still some, some trial and error. So just curious with something like the the AI member outreach that Elephants has been doing and even those summaries that it provides, what's kind of some of the, I guess the errors that there might be and are they kind of fixed through the AI self learning? How's, how's that working? How's it getting kind of past any, any hiccups there?
- [09:10]
C
Yeah, actually I wouldn't say it's a lot about the hiccups. What we have learned actually is we need to obsess about the journeys themselves. So we are really kind of focused on the member journeys overall or the provider journey. So I'll give you an example. In the um, AI case, when a prior auth is submitted, our entire focus is to how to get to real time prior authorization. Right. So when we focus on that journey, what we have found is when we deploy AI solutions, our own learning has been don't just deploy it in a part of the workflow, really think about the entire workflow. So when we deployed it in the case of prior auth, we were only looking at it as the kind of the approval for the prior auth. In fact, there's a lot more in the process that we'll have to think through. And so we went and pivoted. Instead of just a portion of the workflow, we pivoted to actually looking at the entire workflow. And that has been the big learning for us is when you think about AI, just don't think about it in a silo of a workflow, think about the entire workflow and reimagine that workflow as you think about building solutions and deploying AI solutions. And that's how actually you make the entire workflow simple. And then we'll also get them to be more kind of connected as we think about the interoperability between provider and payer.
- [10:47]
B
Right. It's a journey, so focus on the journey. I think that makes complete sense in terms of, you know, the potential maybe that people or payers haven't even tapped into for AI. What are some of the things that elevance is kind of looking to in terms of focus for the future?
- [11:05]
C
Well, I see tremendous amount of potential in terms of the focus for the future. Again, we're deep rooted into creating personalized and more seamless experiences. So I'll give you an example. We have deployed a virtual assistant in our Sydney app for our members to use that. It's like a ChatGPT experience so they can actually ask questions, is this particular procedure covered in my benefits? And then the AI has intelligence to bring that information back to the member and then not only that, it can then connect that member to the right care provider so who can drive quality outcomes at an affordable price. So we have built that intelligence where the AI understands what the member is asking for and how to Connect them to get the care and the clinical outcomes that we actually want to guide the member through. What I'm excited about is creating these personalized and more seamless experiences at scale now. Leveraging AI the other thing that I'm really excited about, as I mentioned, is improving this interoperability between payer and provider because we want to streamline and make it simple for the providers to actually engage with us. And I think there's tremendous amount of potential there. And finally, I'm really kind of excited about how we can transform internally our company so that we can kind of automate a lot of the mundane tasks that are there within a large company like ours. Leveraging AI and really focus on the member and the provider and, and the high value added tasks because that's where we need to be spending a lot more time to simplify healthcare. Yeah.
- [12:59]
B
And for anyone listening who wants to get more information or read about this virtual assistant elevance is working on. Ratnakar did a great interview with Becker. Looks like the end of October. And let me ask you, how are the total number of members that will have access to the virtual assistant? How's that tracking as we're here almost, you know, at the end of 2025.
- [13:22]
C
Actually it's tracking really well. So we're getting a lot more adoption. You know, as we roll this out to kind of a large set of our members, we're getting a lot of adoption for the virtual assistant. What we are seeing is the members are highly engaged, they want to understand their benefit. And the first, for the first time, we made it actually extremely simple for them to understand the benefit. Not only that, it is actually making them easier also to connect with the right provider because the intelligence that we have built through this actually connects them with the right provider who can drive high quality of care at an affordable price. And so we are seeing a lot of engagement. What actually it's helping us do is the member is able to kind of do self service instead of calling and trying to understand their benefits or calling and to trying to actually get to the right provider. Now all of it is moving towards self service. And that's what we're really excited about because we really want to empower our members to kind of navigate this complex healthcare system and we want to make it easy and simple for them to do that.
- [14:32]
B
Yeah, it sounds like the rollout has been going great. As organizations continue to expand and implement AI driven solutions. How do they do so in a responsible and ethical way?
- [14:44]
C
It's a great question, Scott. By the way, we begin with responsible AI, we begin with transparency, we begin with explainability. We actually have an entire responsible AI governance framework within our company. Every AI solution has to go through that governance framework where we evaluate the AI solution for responsible AI transparency and explainability and how the solution is actually designed and being implemented. Only then will we actually approve that AI solution to go into production. And then we monitor that once it's in production to ensure that all the transparency and explainability has been implemented effectively once it goes into production. So you need to both have a framework in terms of the governance framework. That governance framework has to start from the beginning of the solution, when it's actually designed, not after the fact. And so it has to be part of the process. And then once you actually implement the solution, you actually have to also monitor it for its effectiveness. And that's the way we have actually done that. And we've been very successful in doing that across all our AI solutions.
- [16:07]
B
In terms of all the uses elevance has had for AI, a lot of the things you've discussed already, has Elevance been able to pinpoint like, you know, just a number for efficiency in regards to hours saved between payers and members?
- [16:24]
C
Yes, we do. And so one of the things we constantly watch is, is it improving the outcomes that we have kind of outlined in terms of the member efficiency to actually self serve? Right. That's a huge efficiency because the members are effectively getting all the information that they had to go to multiple places in the past. So self service and then self service, not only just self service, how satisfied are they about with the self service NPS scores? Another thing, so we monitor all of that. We also look at in the payers, in the payer provider interaction, we also look at kind of the effectiveness of our interaction overall, how satisfied are the providers, both in terms of how we kind of set them up to begin with. And then how do they actually, how do we process their kind of prior arts claims and any other administrative thing? We actually look at how effective those are, how are we reducing the time of processing those administrative tasks and then how satisfied are the providers there? And then in the associate population, we do a lot of self assessment. How satisfied are our associates in actually looking at the tools that we have deployed and, and is it actually helping them to do their job better? So we monitor a lot of KPIs across the entire ecosystem. And again I come back to the solutions that we're deploying. The KPIs that we're monitoring actually yield to a better and more simplified experience for all. And I think that's a way to actually simplify healthcare for everyone.
- [18:21]
B
Yeah, it really seems that for all these uses, you're, you're mentioning that Elvins goes to great lengths to get member feedback. How crucial is that feedback? Kind of during the early stages of some of these rollouts?
- [18:33]
C
It's actually. Scott, I'm glad you brought that point up, because it's actually extremely important to get this feedback both from members and providers. And we actually study. So even before rolling out a solution like the virtual assistant, we've done a lot of market analysis, so we brought in members to say, will this even be valuable to you? Is this going to simplify in how you look at your benefits, how you can connect your provider? So we do a lot of market research and assessment, and then when we deploy it, we get a lot of this feedback. And, and our teams are really obsessed about member feedback and provider feedback because we want to make sure that we are constantly listening to that feedback to simplify these experiences, because that's the way we actually learn. And we continue to evolve the solution because we're in service of them, and we want to make that very easy and simple for our members and providers. This feedback loop is actually very important for us in how we evolve our solutions.
- [19:44]
B
Last question I have for you, Ratnakar. What do you think 2026 will bring for digital technology and AI?
- [19:51]
C
Yeah, I think, you know, as AI is evolving, as we are evolving, kind of our thinking about data or understanding better about our members and providers and how they interact with the tools and capabilities that we're deploying, we see tremendous amount of potential to actually streamline and create more personal, more connected and seamless experiences for our members. We also kind of are bullish on simplifying the interoperability with our providers so that we can empower care providers to improve health outcomes. And, and finally, we really want to accelerate a lot of the tool adoption internally and the capabilities internally so that we can simplify our workflows so that we can better serve our members and providers. And I'm actually very excited about that because we've laid the foundation and now we believe we can accelerate this transformation 100%.
- [20:57]
B
It's so great to hear how elevance is kind of, you know, stepping on the gas in terms of AI and leveraging it for the member experience and then also constantly gauging that member feedback, which is crucial. Randy Gar. This is a great conversation. You know, we're ending now I feel like I could have talked to you for two hours about this. So thanks so much and really looking forward to working with you again soon.
- [21:17]
C
Thank you so much, Scott. It was really nice. Really excited about our journey, and I'm glad to share that with everyone. Thanks a lot.