Resilient Edge: A Business Vitality Podcast from Deloitte

00:00:11 Tejas Desai: Welcome to this special episode of Resilient Edge, A Business Vitality Podcast, paid and presented by Deloitte. I'm your host, Tejas Desai. And today we're exploring one of the most complex challenges any organization can face, rebuilding trust in healthcare. For decades, the system has been defined by fragmentation, too many barriers between people and the care they need. But what happens when you flip that model? When empathy becomes the foundation and technology becomes the enabler. That's what Deloitte, AWS, and Elevance Health are doing together, using the power of cloud and AI to make healthcare more human. We'll dig into how digital technologies such as AI and automation can give clinicians back their time, how data can build empathy at scale, and what other industries can learn from this transformation. Joining me are three leaders making the vision real, Ratnakar Lavu, Bret Borota, and Jim Zhu. Bret Borota is the global head of healthcare for strategic ISVs and partners at AWS. Bret partners with leaders to build cloud- first strategies that improve outcomes and reduce costs. Ratnakar Lavu is the chief digital information officer and executive VP at Elevance Health. Ratnakar leads the digital and technology transformation that's empowering Elevance Health as a tech- driven health company that's working to enhance healthcare experiences. Jim Zhu is a principal at Deloitte, helping enterprises harness cloud, data and AI to drive scalable transformation. And he's been leading these transformations at Elevance Health for more than 20 years. Let's get into it. Welcome, everyone. Ratnakar, how are you doing?

00:02:00 Ratnakar Lavu: Doing great. Really excited to be here and excited for this conversation.

00:02:04 Tejas Desai: Jim?

00:02:05 Jim Zhu: So excited to be here with you guys. I think this is a worthwhile topic and right now is the right time to go through it.

00:02:13 Tejas Desai: And Bret, thanks for taking the time as well.

00:02:14 Bret Borota: Yeah, absolutely. Thank you personally for having me. And then on behalf of AWS, thank you for being here at AWS Reinvent in Las Vegas with a amazing venue, amazing opportunity to bring customers and partners together alike to have meaningful conversations. So, look forward to today.

00:02:30 Tejas Desai: All right. So how about we just dive in? So when we think about healthcare transformation overall, and what does that mean to you?

00:02:37 Ratnakar Lavu: We all experience healthcare. And sometimes it's actually pretty complex. The benefits, how we understand the benefits, do we understand it in detail, finding a care provider. And then once you find a care provider, the process of actually getting care, we've all gone through some complexity there. What we are trying to do at Elevance Health is actually simplify that. We're really focused on three things. One is simplified personal member experiences. And the second thing is we want to empower the providers to drive the right health outcomes. And then the third thing is we want to simplify work for ourselves so that we can better serve our customers and members. So for example, we have sophisticated data analytics and insights, leveraging AI and other things, to understand the care journeys of members. And we share that insights with our providers to drive the right outcomes. We also have this notion of interoperability because interoperability is going to be key in actually driving the right health outcomes. So we have a health OS platform, where it consolidates clinical data from multiple sources, and then we share that with our members and providers to focus on the right outcomes for the member itself. For example, in the call centers, when a member calls, we actually consolidate that member information for the agent so that they can actually have a better dialogue about the member's issue or concerns, versus trying to look at multiple screens to serve them. We also have personalized plans for them using technology and AI itself. We want them to be able to understand their benefits, find the right care, and then eventually we want to be able to schedule that. And this is where we are really excited about Deloitte and AWS to bring some of these experiences to life.

00:04:35 Tejas Desai: Thanks, Ratnakar. Jim, I mean, speaking of the intersection, with the providers, the plans, how do you see this transforming at that actual intersection?

00:04:44 Jim Zhu: The healthcare system in the US is fairly complex. What I'm truly excited about is for the first time, I feel that AI can actually help us to redefine the relationship between a provider and a health plan to truly focus on serving the member. On the provider side, really focus on creating the best care possible. We have been talking about that vision for the past 20, 30 years. I do think we're on the cusp of truly being able to realize that.

00:05:13 Bret Borota: Yeah. I think if I listen to what both Ratnakar and Jim have said here, we have the shared mission that we're so excited about is healthcare is personal to all of us, but I don't believe healthcare has been personalized to us as members and patients yet. And if you think about the 30- year journey we've been on from a digitization of healthcare, we have actually just created a lot of silos of data. And if you look at other industries that have transformed through technology, say retail or manufacturing or others, they've been able to structure and bring data together in a much more meaningful way to then drive insights that are much more personalized to the consumer for that particular space or industry. And so one of the things that we're really focused on is how do we take all of this healthcare data that is part of the complexity? Because if you've been around the industry long enough, you know that you go to one organization and all the healthcare technology that's been deployed at a specific provider, they'll say, “ If you've seen one implementation, you've seen one.” Together, what I think we can accomplish is really finding a way to democratize that data and create true interoperability from it, assuming we always work backwards from the personalized experience from a patient experience that I think is the transformation that we truly need to see in the industry.

00:06:30 Tejas Desai: So as you started your journey in healthcare, what are some things that you're seeing that are perhaps holding you back or actually are enabling you to transform even further?

00:06:40 Ratnakar Lavu: We've been on this journey of how do we actually consolidate data that includes member data, provider data, clinical data, because those three have to come together to actually create those personalized experiences at scale. And then how do you actually create the right platforms that connect this data together to drive the right experiences? And that's where our entire focus is.

00:07:06 Jim Zhu: Yeah. I'll just say the connected intelligence Ratnakar just talked about is super critical from a go- forward perspective because we all know healthcare, anything that can happen, right? There are permutations and the possibilities of something happen with someone's health. One, it's unpredictable, and when it actually happens, it require immediate action. So having a platform that can not only learn, but also anticipate the potential needs from members and providers, it's going to be really, I think, changing the dynamics of the healthcare industry.

00:07:39 Ratnakar Lavu: Jim, it's a great point. And you and I have talked about this, which is if you go attack use case by use case, you'll always create siloed solutions just because you're trying to solve a single use case. But when you think comprehensively about the experience itself, because the experience is what we should all be obsessed about, then you create the right platforms and capabilities that power multiple use cases at the same time. And that's been kind of the shift, the from/ to that we've been on right now, and that's actually helped us to create better experiences.

00:08:11 Bret Borota: Yeah, and Tejas, you started the conversation around asking Jim about what does transformation mean? And to date, I think a lot of these conversations around innovation have been stifled by the concept of it has to fit into the current workflow today.

00:08:26 Jim Zhu: Right. That is true.

00:08:27 Bret Borota: And the current workflow of the provider, or the current workflow of that organization. And I think the opportunity that agentic and AI offers is taking this platform concept and looking at new constructs around how can we actually re- engineer or rethink about the personas and the workflows that exist today and not necessarily do them the way that we've always done them in the past? How can we leapfrog into a different concept or a different angle of what that looks like? And I think the opportunity, whether it is agentic that does it, I think agentic is opening the door to the conversation to rethink about how we engineer those workflows differently as opposed to always trying to fit into the ones that have always driven healthcare today.

00:09:09 Jim Zhu: AWS bring the actual technology stack in which platform can be built. We used to spend 80, 90% of time obsessed about what technology, what we're going to use and how we're going to implement it. Now with that type of foundation, really free up the human capacity to think about how do we reimagine or redefine the work going forward.

00:09:28 Tejas Desai: If you step back and look at the industry, there's a lot of POCs, there's a lot of initial starts, a lot of zero to one effort, but the one to 100 is pretty rare so far. So Ratnakar, from your point of view, what type of mindset do we need to have and how do you go really from a use case to a platform to actual production implementations?

00:09:50 Ratnakar Lavu: We had a great learning over the last 18 months. We started with the use case mindset. We actually built solutions, both traditional automation and automation with AI. And what we realized was it actually was very successful. When you take a workflow and you take a portion of the workflow and you want to solve that with AI, you can actually do that. And we've seen the result. Now that we've seen the proof points of a sliver of the workflow, let's fundamentally rethink that entire workflow, either using agents or automation or kind of generative AI, whatever it is, let's actually rethink that entire workflow. But that requires us to move from an end- to- end workflow, map it out in the current state, and then rethink what the future state will be, where it is technology enabled to transform that workflow. That is the hardest piece, I think, that companies like us are going through right now. But the other thing that we learned was as we built a lot of these solutions, we have seen kind of efficiencies, we've seen better intelligence where we can serve our members better. We're now thinking about how to take them more horizontally. So not only within that business unit, but take it across business unit, across the enterprise, things like document intelligence. And within healthcare, we deal with a lot of documents itself, whether it's RFPs, whether it's policy. So we've been able to now build a document intelligence capability, working with all of you, and now we're able to take that across multiple business unit and across the enterprise. That's another aha and learning that we had, which I think you'll have to experiment to begin with to see whether the actual initiatives translate into value. You have to show results. Then once you show the results, you then can scale and then you actually think about the transformation that's actually required when you think about workflows and just overall experiences.

00:11:59 Bret Borota: I think there's been this learning curve of the technology while also trying to maintain the privacy, security, and concerns around the sensitivity of data that we deal with in healthcare. I think PHI is super important. So I think there's been this learning curve to not step too far. I think now that the comfort level with the technology is getting there is we're now going to start to see this next turn and pace of, okay, we believe we have the platforms that are secure enough to maintain that patient privacy, to maintain the fidelity of the data, to then now get to level of production. But everybody wanted to be comfortable with the technology first before they stepped into that because of the sensitivity of the data.

00:12:39 Ratnakar Lavu: It's a great point. So when we built these AI solutions also, we had to redesign some of the process. We wanted to start with responsible AI first, so we had to have the right governance, we had to think about the guardrails, the testing that we needed to do from a hallucination bias. And so you also had to learn how to build the capabilities within an engineering organization, and then do it at scale so that you can automate a lot more of those processes. And that's the next phase also. We're trying to automate working with all of you to automate how do you do evals, guardrails, responsible AI governance, and I think that's the next evolution. Then you can speed up delivering a lot of these capabilities to create value.

00:13:27 Tejas Desai: Jim, I would love to get your perspective. I mean, when you think about this zero to one, one to 100 journey, what is something that is a lesson learned for you as part of this journey and what are you doing differently now than any other implementation in the past?

00:13:39 Jim Zhu: The one benefit of working with LLMs for so long is that there is not a zero to one because zero to one doesn't count. For a organization that size, it's always, what are you going to do in production and how do we actually going to positively impact member and providers? So a couple lessons learned, the transformation Ratnakar and Bret, both of you were talking about, I think fundamentally require a upskill in talent. When you think about both from a business standpoint, as well as the technology resources, who for the better part of 20, 30 years been working in a legacy environment and they're really good at it. They created an environment that's resilient. Now we're basically asking them, “ Hey, while we're still serving the customers, still serving the member and providers, we need to start transforming, and transforming not in an incremental fashion, but transforming and redesigning the end- to- end process.” I think that's the scalability that few other industry probably have faced, but it's particularly important in healthcare. We cannot stop, transform, then move on, right? We're going to have to fly the plane at the same time changing the engine.

00:14:43 Tejas Desai: So I think pulling on that thread a little bit, we talk about obviously the need for speed and to go fast. So how do we balance this need for results in days and months and weeks versus building something that is going to be production ready, it'll be resilient?

00:15:02 Ratnakar Lavu: As I mentioned, governance is extremely important. When we start with responsible AI, we want to have it transparent and explainable AI because when AI makes the decisions, you actually have to go back and say, “ How did it make the decisions? Why did it make that decision?” And you should be able to have traceability towards that. So you have to design that upfront. We cannot do it after the fact. As we are thinking about building AI capabilities, what are the components that we actually have to build upfront so that we are doing it in a very responsible way? And so we have a robust process wherein every AI solution is evaluated through a responsible AI governance committee. As engineers are building out the AI capability, we have the design of transparency and explainability much upfront in the cycle and not an afterthought. And that's the way you actually build great AI solutions in an environment that has a lot of PHI data and where we need to actually protect members' data and take that extremely seriously. And we do that and do it in a secure way. 

00:16:11 Jim Zhu: That part is so important, right? It actually highlights the need of not only having the expertise in AI, agentic AI or generative AI, but also need to have the operational expertise to say how do we protect our members' data? How do we protect the enterprise from an end- to- end perspective?

00:16:31 Bret Borota: And I think there's a balance that has to be struck there to really make strong decisions of what is the minimum level of modernization that needs to be done to be able to adopt and embed and support the newer technologies without saying, “ Hey, I've got to start over. I've got to do a full rewrite or enter this three-, four- year journey of a full modernization strategy.” How do you find the right balance points to incrementally do the things that you need to do to advance the capabilities that you know you need to, to move fast enough? And I see a lot of organizations and customers that are struggling in that decision- making process. That's some of what we're doing from an AWS perspective is trying to bring the tools to leverage AI to accelerate the modernization where needed to get to that point of acceleration in those pieces.

00:17:20 Tejas Desai: We talked a lot about transformation, need for speed, but empathy is typically not a word that's associated with any of those, right? So how do we actually incorporate empathy as we're building these solutions? So Ratnakar, I'd love to start with you. 

00:17:34 Ratnakar Lavu: I think it's essential in everything that we build to actually focus on personalized care, but with empathy in it. Empathy can actually come into form in many different ways. An example that we do right now today is when a member calls the call center, we actually use AI to transcribe all those calls and then we analyze the sentiment of the members and we follow up when they're not as content with the service that we have provided. And that is another way to show empathy because we are really caring for the member. Deep in our organization, we really obsess about, are we providing the right service, right care that should be ingrained in everything that we do? And that's how we think about it. It's not technology for the sake of technology. It is technology to actually bring personal experiences to life.

00:18:31 Jim Zhu: I completely agree with everything you just said. The part that I always think it's a little different that we can do now versus kind of the time past is that the lack of empathy is not purposeful. We're always trying to do our best, but oftentimes because the information's asymmetrical, there's certain conditions or experience the member had, certain lifestyle decision that they make, or their schedule, when's the best time to call them or reach out to them? Does member with asthma also have a behavioral health or depression condition? All these conditions from a member standpoint is, you should already know about me, but from a healthcare information standpoint, all sits in the different data silos. So now we have the capabilities of actually being able to stitch that together to say, “ Okay, this is the holistic picture of that member, their lifestyle, their behavior pattern. Okay, based on that, what's the best way to engage them?” The engagement score per member will be much higher now going forward. It's a reflection around empathy because if a member felt like you're being empathetic, their engagement with you becomes a lot more active and meaningful as well.

00:19:38 Tejas Desai: That's great. So switching gears a little bit, one of the themes that I've heard quite a bit from all of you is around creating a better member experience, right? You talked about a seamless experience across chat, across phone and other channels. So what is the highest priority you have at Elevance Health this year to truly drive a unique member experience?

00:19:58 Ratnakar Lavu: There are a couple of things that we're really obsessed about in terms of the personalized experience. We want to bring personalized, seamless, simple experiences so that the member can get the best care the fastest, right? What we have done is we have actually launched with Experian on our Sydney application, which is now personal to the member. So they can ask, “ Hey, what are my benefits? So if I have a knee issue, am I covered or not?” Then it can say, “ Well, I'll also help you find the right provider.” The right provider is not just about the provider who's the closest, it is actually the provider who can provide the best quality care at the lowest cost. And so we do that matching through AI and intelligence that we have built that is extremely personal to the member.

00:20:51 Tejas Desai: Building on this, if you think from an AWS perspective, what are some things that you all are doing that can actually accelerate this journey?

00:20:58 Bret Borota: There's two pieces to what Ratnakar described there. We're in this journey of getting to this better patient member experience, which had to start with the data that would inform, here's the right provider for the highest outcomes, highest quality at the lowest cost. But now what's exciting is we're starting to talk about this world where the transformation's going to come from you have that analytics, now how can the system help you take the next action? In this particular example, it's how can you go actually automate or use an agent to go schedule that next appointment for you? It's like, not only did I identify it for you, and sure, you could have provided that in a list to choose from, but that still was going to require the member to pick up the phone or go into their patient portal and try to schedule that appointment. What if we can now start to anticipate, hey, we know this is where you need to go through our Amazon Connect platform. Now we can start to specialize it for healthcare and take it into this true patient engagement, patient experience and say, take that analytics, take the information that an organization like Elevance, which there's not going to be very many organizations out there that have all the different data sets together in one place to get that type of insight, to then now use a technology to drive the next action and take that off of what is probably already a stressful scenario for a patient, take those low- hanging fruit actions off of their plate and automate those different pieces.

00:22:24 Tejas Desai: Speaking of that, I mean, I think look, this all takes a village. We all have to do this together, talk about collaboration. So Jim, would love to get your perspective on how is it that Elevance Health, Deloitte and AWS, what can they do together to truly drive this transformation?

00:22:41 Jim Zhu: There is a pattern that, when we first started this journey that emerged, AWS was a good cloud provider. We started a journey with, okay, let's start looking at assets that can be moved to the cloud. What we also very quickly realized is once you're on the cloud, the speed to value increased probably five times. I think with the introduction of AI and this agentic framework, a lot of the things that currently even still relied on teams of people, there are literally teams of people talking to each other in order to stitch together that seamless experience for that member. Now we can actually leverage agent, building on the AWS platform to go orchestrate that. Does that mean the human's completely out of the loop? No. That means the resources actually can dedicate more time to figure out what's the next set of best actions for that member and how do we actually work with a provider in more holistic and a seamless manner?

00:23:37 Bret Borota: One of the things that we definitely see in collaborations like this is Amazon and AWS's culture of innovation. You mentioned people, you mentioned teams a lot, you mentioned all these pieces, and we understand and see it across multiple industries at these very large companies, organizations, this culture gets created of, well, this is how we've done things and this is how we do stuff. And I think one of the abilities that we can bring is how to work differently. And the technology enables it, but until the people and the culture start to align around it, there's a lot of change management that comes associated with that. And I think that gets overlooked a lot of times around how we can definitely play a role in helping organizations truly transform to be more innovative and accelerate the pace of delivery.

00:24:24 Tejas Desai: Ratnakar, any thoughts?

00:24:25 Ratnakar Lavu: Well, look, transformations themselves are really hard for a large enterprise like ours. We have really talented people internally who understand the industry, who understand our systems and the ecosystem, and we are really obsessed about our member and the providers and how to connect the dots. But at the same time, technology is moving at a much faster pace than even we can imagine right now within our enterprise. AWS and Deloitte come into play to actually help us rethink how we do the work and infuse that new thought into how we reimagine what we built because from an AWS perspective, you're leading the charge from a technology standpoint. From a Deloitte perspective, you actually see multiple clients that are trying to solve similar problems and then infusing that kind of thought process, along with the talented people that we have within our company, is actually what makes the secret sauce come to life. And I think that's how companies can actually transform much faster because it's not easy right now. There's so much changing so fast that we need to kind of absorb that, and we need that thought process and leadership and support to be able to do that well.

00:25:43 Tejas Desai: So speaking of change, let's just fast- forward the clock a year from now. What's the one change that you're most excited about in the healthcare industry?

00:25:53 Ratnakar Lavu: I think we still have a couple of things to get right. We've gotten to the point of where we can communicate what benefits a member has. We can connect them to the right provider for the highest quality, the lowest cost, but then we don't finish the play, which is a scheduling piece of it. And then we should be really more proactive. So once they get the right care from the provider, what should be the next best action? That is true personalized, proactive care versus reactive care, and then communicate that to the member in saying, “ Okay, you probably should get a blood pressure test done,” and then actually schedule that if they need to schedule it. So make that completely seamless. And I'm excited about that because I can actually see now that we will be closer to doing that ever before and I'm hoping to bring to life within the next year or so.

00:26:49 Tejas Desai: Great. Jim, thoughts from you?

00:26:50 Jim Zhu: No, that's exactly what Ratnakar said, right? We talked about N- of- 1, personalized care, personalized medicine. I think we're literally on the cusp of being able to realize that. The power that AI and the agentic AI actually unlock for us, so it's not just about the top 5% or the 10% of the member we really need to pay attention to. Honestly, when we do that, we basically are taking care of sickness, right? It's when people are actually already at a phase or at a stage where they need care. What about the remaining 90% of member? What Ratnakar just described can allow us to actually expand that much broader to serve the entire Elevance membership. And we can do that with assets and with AWS on Connect on the agentic platform, also just the ability to learn, sense, learn and act on a real- time basis.

00:27:45 Bret Borota: Yeah. I think getting the foundation in place is really, really exciting. I think the next year is going to prove some of that. And then when we keep talking about the complexity, when we start then being able to think about those chronic conditions that have these long care journeys that are associated to them, and being able to now have almost a digital care manager to really be at that patient's side through those complex journeys, we'll really start to be able to see, A, the path to higher outcomes at a lower cost and just an overall delighted experience from a patient perspective that can actually feel like there are people in the system that care about me. I think technology can enable that empathetic approach to patient care with the right foundation in place to get there.

00:28:36 Tejas Desai: That's a great point. I mean, I think if I think about it, we talked a lot about personalization, creating a better member experience, a better provider experience. N- of- 1, how do we not just think about use cases, think about platforms? How do we think about building things that are secure with the right governance, the responsible AI? How do we actually accelerate that journey through a lot of these solutions that AWS has to just connect and how we're actually going to transform this industry that is something that's very personal to all of us. So look, I really appreciate all of you being here. Thank you for the time and look forward on the next round. 

00:29:15 Ratnakar Lavu: Thank you.

00:29:15 Jim Zhu: Thank you.

00:29:15 Bret Borota: Thank you for having us. This was fun.

00:29:16 Tejas Desai: Thanks a lot. Thanks for joining us today. What's clear to me is that progress in healthcare isn't just about better tools, it's about redesigning the experience around the people who use them. We've heard how cloud and AI can simplify what used to be complicated, how automation can give clinicians time back, and how data can make care feel more personal, not less. For any leader listening, the takeaway is simple. The future isn't about choosing between technology and empathy, it's about combining them to make organizations more responsive, resilient, and human. I'm Tejas Desai, and this has been Resilient Edge, A Business Vitality Podcast, paid and presented by Deloitte and produced for Deloitte by BBC StoryWorks, Commercial Productions. Thanks, and until next time.