Delivering More Connected Care Through AI: CVS Health’s Josh Weiner

Josh Weiner, senior vice president of consumer engagement and analytics at CVS Health, is passionate about making health care more personalized, connected, preventative, and accessible. On today’s episode of the Me, Myself, and AI podcast, Josh joins hosts Sam Ransbotham and Shervin Khodabandeh to explain how the integrated health care company is structured and how it is using AI to achieve those goals.

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Transcript

Sam Ransbotham: Organizations can use AI to improve health care by making it more personalized, connected, preventative, and accessible. Find out how one leader thinks about these four dimensions on today’s episode.

Josh Weiner: I’m Josh Weiner from CVS Health, and you’re listening to Me, Myself, and AI.

Sam Ransbotham: Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I’m Sam Ransbotham, professor of analytics at Boston College. I’m also the AI and business strategy guest editor at MIT Sloan Management Review.

Shervin Khodabandeh: And I’m Shervin Khodabandeh, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities, and really transform the way organizations operate.

Hi, everyone. Today, Sam and I are speaking with Josh Weiner, senior vice president of consumer engagement and analytics at CVS Health. Hi, Josh, welcome to the program.

Josh Weiner: Thanks, Shervin. Pleasure to be here.

Shervin Khodabandeh: I’m sure most of our listeners know what CVS is, but maybe you could give us an overview of the company and your role there.

Josh Weiner: Happy to do so. Let me actually start with health care. Health care is deeply personal to me. I was born with a rare genetic condition, and from an early age up until now, I’ve dealt firsthand with challenges, whether it’s [dealing with] cost, or authorizations, or this premise of navigation and figuring out what’s covered, and trying to repeat 35 years of medical history to every new clinician I see, and wishing there was just a summary of that or a way to share that. So for those reasons, I’ve decided to dedicate my career to improving health care. And my personal passion is affordability of health care, the intersection of that.

CVS is one of the largest integrated health care companies in the U.S., with subsidiaries that include Aetna, a health insurer; Caremark, [a] pharmacy benefits manager; the largest retail pharmacy, our core CVS pharmacy; as well as increasingly some care delivery businesses you may have seen in the news: Oak Street Health [and] Signify [Health]. With respect to my role, as you said, I’m the senior vice president of consumer engagement [and] analytics. My priority is developing experiences for consumers to help them improve their health. Increasingly, we’re using AI to revolutionize those experiences. And I believe that’s a focus for today’s discussion.

Shervin Khodabandeh: Wonderful. So let’s just dive right in. What are some examples of the kind of experiences that you’re trying to create, and what is AI’s role there?

Josh Weiner: Great question. As a structure, I’m optimistic about AI’s opportunity to revolutionize consumer health care experiences. There are four areas I’m focused on: more personalized care, more connected care, more preventative care, and more accessible care. And within each of those, we have priorities that we’re confident will make a big difference for you and I, and our 150 million or so consumers. When I think about some of the most exciting examples, several come to mind, but I’ll start with our development of personal health agents.

Historically, the opportunity to develop truly personalized care plans for consumers has been impossible. I mean the unfortunate reality is for a given person — based on a condition and, if you’re lucky, your gender, your age, and some understanding of behavioral preferences — you’re assigned a care plan. That care plan doesn’t really update depending on how well you’re progressing. When you get another checkup in a year or so, maybe you review it, and maybe at that point you’ll make some changes. That’s about the best we could do before AI. The reality is there [are] just too many people that we need to create some clusters. We need to go ahead and use humans to develop care plans and move forward. AI [has] changed this.

With AI, we can now make the most of our longitudinal set of unstructured health data, medical claims, electronic medical records [EMRs], labs, as well as wearable data and use agents to say, “Here’s what a personalized goal might look like based on how you are actually progressing on a day-to-day basis. Here’s an updated goal.” And from streaming those health data sets [we can] identify emerging gaps in care or opportunities to help achieve your health goals and, hopefully, more likely get where you want to go, from a health care perspective.

The role of CVS is that of what is typically characterized as care management or clinical support programs, and is something that either a pharmacist from the pharmacy business or a nurse from one of the health plans typically provides. And they are not prescribing medication or performing surgeries. Their role is to help you close gaps in care, understand and interpret clinician care plans, and help you achieve your goals, often with telephonic outreach on an ongoing basis to check in on how things are going.

Areas like managing side effects, adhering to your medication, or understanding and scheduling the importance of preventative care visits — whether it’s diagnostics or screenings — fall within the scope of the clinical teams in CVS Health’s umbrella.

Now, historically, it has been difficult to share those experiences and insights with provider groups, the largest systems. We have integrations with their EMRs, and there is interoperability, and it’s seamless. They know about your interactions with the care management team, and we know about your interactions with clinicians. But, as you well know, we aren’t where we hoped we would be from an interoperability perspective. This is actually another area that AI is transforming, because we can work around different systems and different data models and have agents talk to each other to facilitate data sharing without spending three years and a lot of money trying to integrate legacy systems.

Shervin Khodabandeh: And when you say “agents,” you mean AI agents.

Josh Weiner: Right. AI agents.

Also, I would say our role is on the prevention side. You’re typically seeing doctors and entering the health system when you have a problem and for management of that problem. But up until that point, and after that point, that’s where we come in. There’s a variety of things you should be doing to keep yourself healthy: taking medications; moving; sleeping a certain way; getting the right number of screenings, vaccinations, and so forth. That’s where we’ve typically come in with humans. It’s been difficult to scale, and AI presents an opportunity not only to do it for everyone but to also develop more personalized care plans.

Sam Ransbotham: You’ve mentioned “personalized” a couple of times, and that really resonates with me. I think we all have our own anecdotal stories, but as I think about this, I [think about] charts. Let’s say radiology reads a chart, and they describe what they see there. I think that they’re speaking in English, but I’m not sure because they use so many big, complicated words that I spend 20 minutes going through [the chart], word by word, trying to understand and google things to see what they mean. And part of that is, I think, the radiologist may be writing for my physician, but I’m reading it, too.

What you described is pretty appealing to me: the idea that this agent could talk to me [based on] what I know versus what everyone else in the system knows.

Josh Weiner: Translation is one of the most beautiful and successful use cases we’ve seen to date. Translation comes in a couple different forms. There’s the version you are describing, where we just ingest language that was intended for the clinical community, whether it’s CPT codes or prior authorization criteria, and we translate it to something that makes sense for a consumer. You know, it’s almost an X-ray of what’s being shared through the [electronic health records] for you in plain English.

There’s another version of that that really excites me as well, and that’s how we communicate with our Medicaid consumers. Historically, it has been extremely difficult to serve these communities well. These are other great examples where we’re today using AI to translate, to provide more personalized care and more accessible care. And it’s particularly for the folks that tend to be more underserved.

Sam Ransbotham: I could see the argument [going in] the other direction that if you’re talking to me about statistics, I think you can hopefully talk at a little bit higher level, and you don’t need to say what an average means or what a distribution is, or I hope not. But we need that coded information for the clinical community to rapidly understand things in the system. But a lot of that depends on the idea of this data sharing and this universal access to data. I think I’ve heard this for the last decade or more about how we’re going to get there.

Where are we in this? Is this tomorrow? Is this yesterday? Is this infinitely always two weeks away? How are we going to get there?

Josh Weiner: I think AI is the unlock we’ve been waiting for.

Let’s talk about what we’re doing within CVS to start. We have all these subsidiaries with different clinical platforms, different data structures. So for us, developing even an internal Customer 360 has had the issues you’ve just described: “Oh, we’ve got to figure out how to integrate these systems. We have to build the common data structures, and it’s truthfully extremely difficult.” It’s written in different languages and so forth.

AI agents present an opportunity to interpret unstructured data and to share the data across applications that are programmed in different languages with entirely different architectures in a way that was previously extremely costly. And that’s a hypothesis, but we’re able to do that today internally, to finally create consumer 360s for our patients. And it’s reasonable to me that [by] using the same set of technologies, we can begin to actually leapfrog the interoperability challenges of the broader health care system.

Shervin Khodabandeh: I agree with that because we’re also seeing it in other industries as well. The challenge of data management and connecting different data domains that have different names and conventions in disparate data sources has always been a manual challenge because of the nature of those data sources, which has always been structured data that has to fit in tables in a certain way. … By the way, what was the purpose of that, to have weaker analytic models use it? What you’re saying is the models are so strong now that they could do a lot with data without the structure. In fact, the structure actually confuses them, to some extent, and I hope that we see this at scale. That might be the unlock you’re talking about.

Josh Weiner: AI is able to do what was very difficult for humans to do in figuring out how to integrate different types of data structures across different architectures. We have a whole NoSQL vector database of electronic medical records. It was previously impossible to interpret, impossible to track the data, structure it, and share it. Now we can just throw [language models] on top of it, and it works. And we can use prompt engineering to figure out how to convert it and share it with all different types of recipients.

Shervin Khodabandeh: Josh, you talked about four priorities, and we talked about personalization. Do you want to cover a couple more?

Josh Weiner: Sure. I think we also talked about more connected care, indirectly.

We certainly feel it at CVS, where our mission is to create better integrated experiences. And we haven’t been able to solve that problem. We just spoke about how, through AI, we’re delivering more connected care. But what’s also fascinating, what I think will ultimately have the most profound effect on health care, is accessibility. This is a bit further out but worth discussing. Just last week, I saw [what] I think [is] the third study in mental health where they’ve demonstrated that an AI therapist through text can perform at a level of clinical parity to that of humans.

Today, the behavioral health landscape is really challenging. There’s far more demand than supply. If you’re lucky, you can get an appointment in four months, and even then, you may be told, “Hey, we don’t really take your insurance, and there’s this large out-of-pocket [cost].” I expect in the next two or three years, everyone can have their own AI therapist in their pocket available to them at all times. [That is] incredibly affordable. And that’s not today, that’s hopefully tomorrow, but that’s also part of our research agenda and what really excites me.

Sam Ransbotham: What I think [is] beautiful about that is the idea that I would hope that the best therapists are better than next week’s AI agent. I think that’s going to be true for a while. But the way these technologies tend to work is that [they] meet an underserved market. There’s a market of people who are not getting access, who, let’s say, wait four months or maybe wait infinite months to get access to therapy. This idea that you can connect these people through technology, to give them some minimum level of service, is pretty appealing because, right now, I think the competition is not — you know, I think you mentioned — on par with [humans]. I think the argument right now is that the competition is on par with nothing.

So all we have to beat is this non-inferiority trial here. We don’t have to beat the human. We just have to beat nothing at this point.

Josh Weiner: We’re giving humans superpowers, which are enabling them to serve more patients and community members. I had previously referenced how we employ nurses, and our nurses work with you to build care plans, help you adhere to care plans through the use of AI. We expect them to be able to serve more patients, given how much of the busy work AI can help automate. And then you add on things like messaging and digital channels and technologies. Maybe you can even get more leverage from a panel perspective. So … we’re not going to replace you anytime soon. But with superpowers, we can make you more effective. You can help more people. It’s more accessible. It’s cheaper.

The other thing we haven’t talked about yet, and I would be remiss not to mention, is the importance of empathy and trust as we build these experiences to be able to actually revolutionize health care and transform our trajectory. And that’s something we’re being really thoughtful about. We’re putting in quite a bit of process and governance to make sure that we’re extremely rigorous in how we test, how we codesign these experiences, how we measure accuracy and effectiveness. The starting point is back office and enablement. It’s only over time, as we demonstrate success, and as we’ve made clear that this can be trusted, and we’re continuously looking to help you and do what’s right for you, that we can progress to these broader, more transformative use cases.

Sam Ransbotham: Let me push back a little bit on that because you mentioned an ecosystem of people that involves pharmacy and pharmaceutical companies and hospitals and insurers, a bunch of these people. And if they’re all developing an agent and those agents are going to maybe work more for that company than for me as the individual, how do we get the individual voice represented in this? Who’s going to take that action? Because I don’t think a lot of individuals are going to make their own agents.

Josh Weiner: Right. I guess I see the future a little differently. I expect that, Sam, you’ll have a couple agents predominantly maybe from some of the major consumer platforms and operating systems you work with today. And I want to make sure that those agents have the intelligence of the agents we’ve built. I don’t really expect you to be using your health insurance agent on a daily basis. That’s just not a daily need. But I want to make sure Siri, or whoever it is, has all that intelligence and power. Our focus is going to be how we integrate our agents with your real agent, who knows you and is really kind of looking out for you with your priorities.

I see it a bit more the way you think about APIs over the long term. It’ll take a little while to get there. There has been an advancement in those agent-to-agent protocols, but I don’t think any of us want a world where there’s just, you know, 20 different agents from every single company I interact with that are trying to build relationships with me.

Sam Ransbotham: I think that seems really important. And actually, I really like the way you frame that because what you’re saying is if all these agents can talk to each other, then we can let the invisible hand of the economy straighten this out. If Company A builds a duplicitous agent that is always recommending the expensive drug or whatever — I’m just making up stuff at this point — but always going down one path or clearly has some sort of vested interest, then what you’re arguing is that if we allow [it, we’ll] build in mechanisms for all these protocols for agents to talk to each other, then we can let the invisible hand work on that. That’s actually pretty appealing to me.

Shervin Khodabandeh: Well, those duplicitous actions don’t need automation and AI to exist, right? They very well exist right now with human agents. [Laughs.]

Sam Ransbotham: I deeply suspect they do. I like the idea of what you’re saying, that this allows this transparency in governance to show some of [what’s] probably going on right now.

Shervin Khodabandeh: Josh, let’s switch gears a little bit. Speaking of agents, I know you are a chess aficionado, and I’m guessing you’re using AI somehow in advancing your own knowledge of chess. Is that true, or? Are you doing anything in that domain?

Josh Weiner: Funny you mention it. So I have been using my love of chess as a way to stay on top of advancements in the space. I’ve been in the process of building out an AI chess coach using some of these tools like Cursor or GitHub Copilot and so forth. Being good at chess and certainly being good at teaching someone how to play chess requires planning and reasoning.

It’s not just machine learning or math. Certainly, there’s Stockfish and your incredible chess algorithm that always tells you the scientifically perfect answer, but that’s not useful in actually helping someone become a better chess player.

It’s not teaching them how to be better. So from what I know, for the time being, the breakthrough in chess coaching is going to have to wait a little bit for the next breakthrough in more generalized AI and AI that has more reasoning skills. But for the time being, for me, using AI to build an app when I haven’t seriously written code in about a decade — and I’ve been able to now roll out an app that’s 40,000 lines of code across five languages — just using AI with one person to do something that might have otherwise taken five people six months, that’s beautiful in and of itself.

Shervin Khodabandeh: How long did it take you?

Josh Weiner: Thirty hours.

Shervin Khodabandeh: There you go. Wonderful.

Josh Weiner: That’s incredible, right?

Sam Ransbotham: Josh, it’s been fascinating talking. I think there’s a lot of opportunity here, and we’ve touched on a lot of that opportunity, but you also mentioned how our barrier or our thresholds may be a bit off. We can make a lot of progress, but I think we’re going to have to be patient in this process. Thanks for joining us today, and thanks for taking the time to talk to us.

Shervin Khodabandeh: Thanks for listening today. On our next episode, Sam and I speak with Julian Adams, president and CEO of Stand Up To Cancer. We hope you’ll join us.

Allison Ryder: Thanks for listening to Me, Myself, and AI. Our show is able to continue, in large part, due to listener support. Your streams and downloads make a big difference. If you have a moment, please consider leaving us an Apple Podcasts review or a rating on Spotify. And share our show with others you think might find it interesting and helpful.