Ritu: Hello, listeners. Welcome to Season Seven of The Big Unlock Podcast. My name is Ritu M. Uberoy, and I’m managing partner at Damo Consulting and your host today. We are very happy to welcome Ann Bilyew to our podcast. Ann is a prominent executive in the healthcare sector, currently serving as Executive Vice President and President of the Healthcare Solutions Group at Internet Brands, where she leads WebMD Ignite, the leading B2B growth partner for healthcare organizations. She has over twenty years of experience investing in and leading healthcare companies, and plays a crucial role in shaping strategies for patient engagement and healthcare solutions. Really happy to have you here with us, Ann. Welcome.

Ann: Thank you, Ritu. It’s a pleasure to be here. Just a couple of sentences about WebMD Ignite so people understand what we do. Most people think of WebMD as the place consumers go with a health question, and that is still very much who we are. We also have Medscape, the leading professional source of information, reference, and news for clinicians. Ignite is our institutional or enterprise business for payers and providers and health systems. What we provide is a comprehensive patient and member engagement platform powered by industry-leading content and data, which enables personalization at scale and omni-channel orchestration. We are really the entire backbone of a patient or member engagement platform for payers and providers.

Ritu: Thank you for telling us about that, Ann. That leads directly to our first question. People are actively searching the internet for health information, and we’ve moved away from “Doctor Google” behavior toward AI summaries — where people don’t even know where the information is coming from; they’re just reading AI-generated answers and acting on them. Patients are now coming to doctors so prepared from ChatGPT that they’re essentially seeking a second opinion on something they’ve already formed a view about. How do we maintain trust in this environment, and how do patients know what to trust?

Ann: A great question, and we don’t really know yet — all of this has happened so quickly. What we do know is that there has been a complete collapse of the traditional funnel for health systems and payers doing traditional digital marketing. By our count, roughly seventy to eighty percent of a health system’s digital budget has been focused on search — specifically Google search. Over the past fifteen to twenty years, they’ve spent billions of dollars trying to be one of the little blue links at the top of a results page. What we’ve seen evolve over the last twelve months is that health is squarely in the crosshairs of AI Overviews — AI-generated answers instead of links. A study by BrightEdge from December of last year found that ninety percent of health-related questions put into Google now return an AI Overview. And when an answer is returned, click-through rates drop dramatically. A Semrush study from around the same period showed that when an AI Overview is present — which, remember, is now ninety percent of health-related searches — click-through rates drop to around one percent. That whole strategy of search engine optimization and search engine marketing has been completely blown up. The information is now readily available whether a consumer goes to Google or uses ChatGPT or Claude or whatever LLM they prefer. What’s increasingly scarce is credibility and trust, and that’s where a health system can maintain relevancy in the marketing funnel. That is where they increasingly need to focus.

Ritu: You’ve made the argument that content is scarce, and AI makes that point so much more consequential. Historically, patient education content could be reviewed, approved, and published. But now an LLM isn’t just giving back that same information — it’s combining it with other sources and generating new answers in real time. How should healthcare organizations treat their content layer in this environment?

Ann: When you look at what used to be called the digital front door — which was always a fancy phrase for a website — traditional SEO was all about keywords, volume of content, and number of pages. Scale. We know that no longer matters as much. Our team studied about fifty markets and well over a hundred and fifty health system websites to see not how they were ranking in Google, but how they were being cited by the LLMs. What we found is that the most important thing you can do is have the right information. When an LLM gives an answer, it is increasingly listing sources — and you want to be cited as one of those sources. To be cited, the content you publish has to be credible: complete, corroborated by third parties where possible, and connected to the overall pathway to care. What we see mattering most is physician profiles that are complete and comprehensive — that really tell a full story about those providers. You need robust information about the service lines that are most important to you, demonstrating your competency and capability, corroborated by third-party sources.  Beyond the citation, what ultimately matters is being listed as the handoff — the place to go for care. It used to be: question in Google, blue link, user lands on your site, and you try to guide them through the patient journey. Health system websites, frankly, are pretty awful. I would encourage any listener who says “my website is great” to actually go on it and see — the friction, the homepage trying to do everything at once between paying a bill, finding a location, and figuring out where to park, none of which has anything to do with getting a person interested in care to the right clinician. Health systems have to own the handoff from question to answer to care. The answers will increasingly be provided by LLMs — full stop. We’re not going to own the information layer. What we can own is the conversion layer, the conversion to care. That is where health systems need to optimize their content, their website, and their outreach.

Ritu: Both of those points are absolutely right. The handoff is so important because information is now coming from so many different sources, and the validity and trustworthiness of citations matters more than ever. Let’s change tracks a bit. We always love an origin story — how did you get into healthcare, how did you get comfortable with tech, and what has your journey looked like?

Ann: I’ve always been in healthcare my whole career — not as a clinician, but trained as a business person. My journey really has three or four chapters. Coming out of business school, I joined a healthcare-only boutique consulting firm called APM, really focused on M&A. This was during the first major transition to integrated delivery networks. Doing M&A, integration, and deep operational work was a great training ground — even post-MBA — to really learn how these organizations operate and how to design solutions that can actually work in the real world.  Then I joined a private equity firm called Advent and became a partner there, investing in healthcare technology and healthcare services, both in the US and internationally — Europe and Israel. I wanted to try running a company, so I took a portfolio company and became CEO of one called MD Everywhere. That’s where I really learned to lead large organizations through periods of rapid technological change. I’ve seen a lot of technological transitions over my career, and a lot of what I call technology theater — most of which ends badly. But this time feels a little different to me in terms of the impact of AI and the velocity of change. It feels different from most other transitions I’ve seen.  We successfully sold and exited that business, and I then joined Internet Brands just after they completed the acquisition of WebMD and took it private. My charge was to build out the institutional or enterprise business — what we now know as Ignite — and scale it. Today we work with 95% of the hundred and fifty largest health systems and 80% of the twenty largest payers, so we’ve gotten a lot done in the last four or five years.

Ritu: Loved hearing those chapters — but still want to go back to the origin. When you came out of business school, why healthcare? There were so many options. Usually people have a personal connection. Is there something that led you there?

Ann: Coming out of undergrad, I was accepted to Harvard Business School — which is unusual right out of college. They said, “You’re young — go work for a couple of years and then come back.” So I had a year to do essentially whatever I wanted. There was a community health center in Kansas City — I went to KU for undergrad — that wanted to build a new facility. They’d outgrown their current one and needed someone to come in on a project basis and help raise the capital through bond initiatives at the city and state level. It was a great project, and that’s really where I got the healthcare bug. I became genuinely intellectually interested in the complexity of the industry and the impact it has on communities and on the country as a whole. We raised the capital, got the building built, and I went back about a year later and saw the groundbreaking — knowing that project had helped many generations of families in Kansas City since then. That’s how I got into healthcare. Coming out of business school, I really only looked at healthcare and went directly to APM — sort of the mothership of healthcare boutique consulting, deploying to all of the leading health systems and payers at the time.

Ritu: Thank you for sharing that. WebMD Ignite describes itself as both a patient engagement organization and a growth partner helping health systems attract patients and strengthen referrals. Most of the time those goals align — but not always. What happens when the commercially optimal action for a health system isn’t necessarily best for the individual patient? And what governance do health systems need to ensure personalization is optimizing for the patient’s best interest rather than just becoming a more sophisticated marketing engine?

Ann: Just a quick clarification first — we work with over 650 health systems in total. The 95% figure refers to the 150 largest, but we work with organizations across the full spectrum: very small, local, regional, and standalone all the way up to the largest health systems in the country. That breadth gives us a tremendous perspective on what’s working, what isn’t, and what strategies are being deployed in different markets.  To answer your question: the way I think about it is that we have to earn the right to communicate with people. The only way to earn that right is to give them something of value — to show them that we know them, understand them, and have taken the time to understand the unique set of circumstances that make them an N of 1. What are their unique clinical characteristics? What are their unique socioeconomic circumstances? What are their preferences — how do they like to receive information, in what format, through what channels? Only when you put all of those things together can you say: here is what matters to this particular individual, here is where they are on their care journey right now. That is how you earn the right to communicate.  This is what retail and other industries have done well and have trained consumers to expect — they want to see that you’ve taken the time to understand their needs, and that you’re giving them information that is relevant, timely, and in their preferred format through their preferred channels. When we give people quality, tailored information, the patient will make the right decision for themselves. That is really our job: give them the right information personalized to their needs, preferences, and circumstances so they can make a good decision — and then facilitate whatever that decision is. Remove the friction from making an appointment, from signing up for a class, offer tools like health risk assessments or calculators. Remove the friction, help people make good decisions. The ultimate decision always belongs to the consumer, to the human, to the patient.

Ritu: What I gather from that answer is that you’ve tried to unify the patient journey. You’ve mentioned removing friction several times, and we know healthcare is deeply siloed. Given that unique perspective of working with 650-plus health systems, what has been the most difficult part of unifying the patient journey, and what goes on behind the scenes?

Ann: I can’t think of another industry that treats its customers — its patients — in such a siloed way. When we think about the patient journey, there’s content as they’re making a decision or selecting a provider or deciding whether to make an appointment — we call that marketing. Then once a patient is on a journey, has selected a provider, made an appointment, scheduled a procedure, the information they start receiving at that point we call education. But it’s all the same thing. It’s all designed to influence someone to make a decision and take an action that is hopefully in their best interest. Whether they’re early in the journey, deciding whether to see a clinician, or they’ve already scheduled a procedure and are receiving information on how to prepare — it’s all information and engagement designed to influence at different stages.  The challenge is that those parts of the organization are completely siloed. Clinical owns patient education; marketing owns patient acquisition and activation; someone else owns retention and adherence. Separate budgets, separate teams, separate technology stacks — yet it’s one person going all the way through that journey. It’s one human being we’re speaking to throughout. Getting our clients to think that way is the work. Some are getting there — you’re starting to see new roles like VP of Patient Experience or Chief Patient Experience Officer, and budgets beginning to come together. But we’re still in the early innings.

Ritu: Time has flown, as usual, and we’re almost at the end of the podcast. Any closing thoughts or crystal ball predictions? We used to ask about the next year or two — now it feels like the next few weeks.

Ann: Anyone making predictions today is taking a big risk. The speed at which we’re evolving as an industry, as a culture, as a country is astronomical and truly unprecedented. I wouldn’t venture any predictions other than this: the information layer will increasingly be owned by the LLMs and the foundational models. Our job is to figure out how to make the connection point between those foundational models and the actual provision of care. That is where we have to concentrate, and that is where we have to figure out how to build.

Ritu: Thank you so much, Ann, for joining us on the Big Unlock Podcast. It’s been our pleasure having you as our guest.

Ann: Thank you. It’s been my pleasure. I appreciate it very much.

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Subscribe to our podcast series at www.thebigunlock.com and write us at info@thebigunlock.com    

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.

Ritu: Hello, listeners. Welcome to Season Seven of the Big Unlock Podcast. My name is Ritu Oberoi, and I’m managing partner at Damo Consulting and your host today along with Rohit. We are thrilled to be welcoming Don Woodlock to our podcast. Don is the President of InterSystems, where he leads the company’s day-to-day operations after spending eight years leading its healthcare business and global healthcare strategy. Prior to InterSystems, Don spent a long time at GE Healthcare. He is also a founding advisory board member of the MIT Generative AI Impact Consortium and hosts Code to Care, a series focused on practical applications of AI and data technologies in healthcare. We’re sure this will be a really interesting conversation — Don brings a unique perspective on how data interoperability, AI, and trusted technology infrastructure will shape the next era of healthcare. Looking forward to it, Don. With that I’ll hand it to Rohit, and then we’ll get started.

Rohit: Welcome, Don, to the podcast. I’m Rohit Mahajan, CEO and managing partner at Damo Consulting and co-host of this podcast. Looking forward to an engaging conversation.

Don: I’m glad to be here. It’s such an exciting era that we’re in — we really need each other in the community to figure out best practices, what’s working and what’s not, so we can direct our energy into what’s best for patients and clinicians everywhere.

Ritu: Let’s get started. You stepped into the president role at InterSystems after spending so much of your career deeply immersed in healthcare technology. As you look across the technology landscape now, what do you think healthcare leaders are still underestimating about the transformation AI is bringing? And what do they really need to do? We’re seeing time horizons shrink from years to months to weeks — it’s almost daily with new models and new headlines. How does anyone keep up, and what should CIOs be thinking about?

Don: In terms of AI, there’s the center everyone talks about — the models, the safety, the applications coming out of the frontier model companies. But there’s a front end and a back end that I think we need to spend more time on. The front end is the use cases, the ROI, the change management. We’re kind of kids in a candy store, and there’s a real danger of eating too much candy — of not being judicious about where we invest our time and everyone else’s. The back end is the data and integration requirements that actually make AI work. It’s not just ChatGPT or Claude applied to healthcare. You need to wire it up, give it data and context, procedures and skills and all kinds of things to make it really perform for your organization. All three legs of that stool need to be attended to for AI to work really well.

Ritu: Great answer. People are really underestimating that. I heard a term recently on LinkedIn — “AI wishing,” as opposed to AI washing — where most people think they can wave a magic wand and AI will just solve all their problems. That leads directly to my next question: healthcare is fundamentally different from other industries where AI agents can simply execute tasks and correct mistakes afterward. In healthcare that simply doesn’t work. Where is the boundary between AI that recommends and AI that acts, when the accountable party always has to be the clinician?

Don: Healthcare is serious business, and AI can’t just be let loose. There are areas — revenue cycle management, administrative scheduling — where you might treat it more like other industries. But for core physician and patient decision-making, AI has to play an empowering role: making sure facts are clear, opening the aperture of what else should be considered. There’s a lot AI can do to support those conversations, but it still has to be human-led. I heard a physician speak recently about a patient who asked what AI thought about their case — but what they really wanted to know was what the physician thought. Patients can figure out AI’s opinion on everything at home. When they come to a healthcare organization, they’re there for human expertise and human touch. Human in the loop is the obvious thing to say, but we need to make it work in a way that truly empowers people. In the end, it’s clinicians and patients who make the choices.

Ritu: I think that’s a great distinction — AI is helping patients prepare for visits and be better informed. But when they get to the doctor, they’re really looking for expertise and reassurance.

Don: From the doctor’s point of view, AI is very good at gathering facts — summarizing a giant chart, surfacing any cardiac history buried in the record, those kinds of things. AI can quickly read a big chart and tell you what’s pertinent. What’s also valuable is the “what else should I be considering?” function — before a clinician zeros in on a diagnosis and treatment plan, AI can open the aperture. What else should I be looking at? The human still makes the choice, but AI is a great input into that choice. It has a lot of positive benefits even from a clinical practice standpoint. But in the end, it has to be used in support of human decision-making.

Rohit: You’re captain of a very large ship that has been sailing for a very long time. From your vantage point, what are some of the things you’re navigating from the AI perspective to ensure your customers and users get the best possible experience?

Don: InterSystems is a software company that supports healthcare as well as other industries, and the software industry itself is going through massive transformation because of AI. So a big part of this is internal — how should AI be used in our own processes to build software, implement it, support it, sell it, market it?  We’ve gone through a few transitions. Step one was basically giving everybody a license to OpenAI or Claude. That gets the wheels turning — people have a personal tool they didn’t have before and can discover how it helps them in whatever role they’re in. Step two, which is where we’re focused now, is embedding generative AI systemically into how we work. It’s no longer a personal choice about whether to use it right now — it’s cemented into the way we operate. For example, when you check in code, it automatically does a code review and posts the results in our system. Nobody is choosing to ask AI to review the code — it’s just built in.  Or take our EHR implementation process outside the US — a year-long program. We’ve built 42 different agents to help employees get their implementation jobs done. One simple example: the first step in an implementation is to read the sales contract and identify what matters to you as an implementer — penalties, specific dates, service level agreements, unusual requirements. We wrote a contract summarization agent. As soon as you post the contract, it automatically summarizes the document — not a generic summary, but one tailored for the implementation person, pulling out implementation-specific items and posting the summary where the rest of the team can benefit from it. That’s embedded right into the work process. The idea is to make the company AI-infused in the way it does its work — that’s the phase we’re in right now.

Rohit: What are you hearing from clients? They’re becoming more well-versed with AI and generative AI, running their own experiments and pilots. You must have a great listening machine over there. What are the common themes bubbling up from the customer side?

Don: There are developer customers and end-user customers. Developer customers are going through a similar transition to ours — generally a little behind us since many are smaller companies or startups. They’re mostly interested in best practices: how should they set up their development environments to best leverage AI on our technology? How do they get our documentation into the context so Claude Code can see it and read it? It’s really about helping them build the best generative AI-assisted development environment with our technology as one of their key platforms.  On the end-user front, it’s use cases, ROI, safety, and governance — all the right issues around how to adopt AI in a way that actually works. And every month the conversation shifts. Right now a big topic is token cost — how do I predict and manage the cost of AI? The implicit question is: will I get an ROI for this use case, or am I spending money without a proportionate benefit? It’s a little like cloud computing a few years ago when cloud costs got out of control. Now people’s token costs are doing the same thing, and they’re trying to figure out how to manage it. The lesson is the same: don’t max tokens — max benefits.

Rohit: The parallel to cloud computing is very pertinent. The same runaway cost dynamic, the same need to get strategic about it.

Don: Both cloud and AI are transformative — but that’s not the same as just letting loose. There’s value in a period of exploration at the beginning, because it’s hard to think of everything when you’re facing a genuinely new transformative technology. But at some point you have to take a strategic view of it, and I think we’re getting into those more strategic views now.  The other big trend, of course, is agentic AI and how it changes the equation for use cases. Healthcare is a little conservative from a technology adoption standpoint, and I think we appreciate that as patients — we want things thought through. In software development, agentic AI is here and now; in healthcare it’s more of a “what could this bring?” question. But I think agentic AI has a bright future in healthcare, particularly for administrative tasks. Imagine telling an agent: “Set up this patient for surgery — get the prior auth, schedule the surgery, check for any transportation needs.” The key is that the agent presents a plan, and the human approves or adjusts before hitting go. That ramps up the importance of human-in-the-loop design and user experience even further. We’re at the beginning of what’s going to be a multi-year era on that front.

Rohit: We’re seeing that directly with one of the startups we work with in AI ophthalmology — the physician is in the loop while the agents work in the background. That’s exactly the model that scales, especially for anything patient-facing.

Don: Healthcare isn’t purely scientific. It involves ethical issues, personal goals and preferences, family considerations — it’s not like software development, which is more straightforward and less wrapped up in medical ethics. AI has a lot to contribute, but it doesn’t carry the risk of wanting to remove humans from the equation. Healthcare is a very human endeavor.  The other dimension specific to healthcare is staffing shortages. Unlike other industries where people are worried about job elimination, healthcare has a genuine shortage of clinicians — physicians, nurses, social workers, physical therapists. People are already waiting too long for appointments. If AI can lower the administrative burden, speed up access to care, and let physicians and nurses go home when their shift ends — those are all very positive things for the industry.

Ritu: Don, would love to hear your thoughts on the AI-native EHR, since InterSystems is also positioning in that direction. With generative AI, ambient interfaces, and agents that can take action — where do you think the EHR is heading in an AI-native world?

Don: Let me take you back to the concept of AI-native for a moment. When generative AI first arrived, some EMR vendors were simply partnering with third parties — bringing in ambient listening technology or chart summarization through partnerships. You partner when something isn’t core to what you’re trying to accomplish. When we looked at it, we said: AI is going to have a huge impact on healthcare, and the last thing we want to do is make it a non-core activity. AI needs to be our competence — built right into the product. Partnering would have cost us six months to market, but what you get when you build it yourself is that it’s yours, it’s core, it’s in the middle of your value proposition. That was the original idea behind AI-native. The rest of the industry has since moved in the same direction, because that’s the right approach.  The goal is always to simplify the workflow for the user. It’s already clunky to use computers when you’re trying to take care of a patient. If you have to jump between multiple software programs, alt-tab, re-log in, and transfer information — that’s clunky. Getting AI right within the workflow, making it a natural part of a unified clinical experience, was important.  I’ll share a story. We implemented a chart summarization assistant — a button at the top of the EMR that opens up the right third of the screen as a chatbot. We visited our first customer who had used it, an ED doctor. I was over his shoulder as he opened a patient, asked four questions of the assistant, then went to see the patient. When he came back, I asked, “Do you ever use the left two-thirds of the screen anymore — the thing we spent fifteen years building?” He said, “No, I never use that.” It had only been live for two weeks. That’s how quickly the world changes. The primary way someone used to learn about a patient was to open the chart, click through the tabs, review the last visit. Now that’s gone.  That’s a read-only use case. But imagine agentic AI delivering more impactful use cases: “Get this patient ready for surgery — do all the steps.” “Work up this patient for this situation.” “Negotiate with the payer’s AI on getting these claims paid.” You state a higher-level intent, and the system figures out what you mean and goes and does it. Software in five years — or less — is going to look completely different. All the user interaction patterns we built over the past twenty years may give way to a new model. That’s what AI-native really means: not bolted on to the last twenty years, but a fundamental rethinking of how end users interact with systems.

Ritu: Great story — I’m sure our listeners loved that. Time has flown by and we’re almost at the end. We usually love to close with an origin story — how did you get into this field, and particularly into healthcare and technology?

Don: When I graduated college I was a math person — I went to MIT, where computer science and electrical engineering were in the same department. I interviewed for both electrical engineering and software jobs. For electrical engineering I pictured myself in a basement with an oscilloscope, wiring things together, and that little circuit would eventually end up in a speakerphone at some hospital — but I would never meet anyone who got value out of what I did. I’d just be part of a long chain. With software, I always imagined I’d be talking directly to end users and building things directly for them. So I took a software job at a healthcare software company — actually an InterSystems customer at the time.  It all turned out exactly as I’d imagined. I remember one of my first projects, in Albany, New York at a healthcare system. I would gather all the users in the morning and ask, “What do you want the software to do?” They’d tell me what enhancements they wanted, I’d code all afternoon, and at 4:00 I’d gather them again to show what I’d built. That direct touch with end users — delivering what they need and seeing them find value in it — has been a core part of my whole career. Fast-forward a few decades, and InterSystems is still a very customer-focused company. I still get to do that all the time, even if I’m not writing production code anymore.

Rohit: Great story. Don, you mentioned the Code to Care series in your introduction — would you like to tell listeners a little more about that?

Don: It’s a video series of five-to-ten-minute videos explaining AI concepts in simple terms for this audience. I enjoy teaching, and I find that AI can be confusing and full of buzzwords. So I started the series to explain things clearly — a recent one covered the shift from prompt engineering to context engineering to harness engineering. Those are terms people hear and don’t quite know what they mean, and I take five minutes to explain them. I have a nice little audience that appreciates learning in bite-sized segments.

Ritu: Thank you so much, Don. Really appreciate you joining us today.

Don: Thank you both. This was a very engaging conversation. I appreciate it.

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Subscribe to our podcast series at https://thebigunlock.com/ and write us at info@thebigunlock.com    

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.

Ritu: Hello, listeners. Welcome to Season Seven of the Big Unlock Podcast. My name is Ritu Oberoi, and I’m managing partner at Damo Consulting and your co-host today along with Rohit. Today we are welcoming Roberta Schwartz to our podcast. Roberta is the Executive Vice President and Chief Innovation Officer of Houston Methodist Hospital in the Texas Medical Center. As EVP, Roberta oversees all operations at this 979-bed hospital, which US News and World Report named the number-one hospital in Texas for fourteen straight years. As CIO, she is responsible for advancing and expanding Houston Methodist’s digital innovation platforms, including telemedicine, AI, and big data. We are really looking forward to this conversation. Welcome to the podcast, Roberta.

Roberta: It is a pleasure to be here. Thank you so much for having me.

Rohit: I’m Rohit Mahajan, managing partner and CEO at Damo Consulting and co-host along with Ritu. Looking forward to chatting with you, Roberta. Over to you.

Ritu: Roberta, you’ve talked about 2026 being the year of maturation and said that healthcare is now well past the AI hype phase. But we’re still seeing CIOs mostly jumping onto a tool-based strategy and acquiring siloed tools — which leads to interoperability problems all over again. With all the success Houston Methodist has had, give us your viewpoint on how AI is actually changing the operating model of a health system, and what you’ve done to make this such a success.

Roberta: If you look at that technology adoption curve that ends in maturation and then starts the next curve, we’re right at that center point. All of us now have fairly mature EMRs that continue to make modifications and improvements — whether you’re on Meditech, Epic, Cerner, or Oracle — layering in more recently available technology: search, symptom checker, chat, texting functionality. That’s the maturation happening in the base tools.  The second thing — and I credit Darren Dworkin as a mentor and friend who articulated this — is that it used to be when we met a company, the job was to keep everyone in their swim lanes so technologies didn’t cross into each other. But that’s no longer possible. What a company could do yesterday, today, and tomorrow are essentially infinite. You used to tell companies, “Stick to your lane,” and now the lane is the entire swimming pool. So picking companies becomes both the challenge and the exciting part — because once you get into the pool with one or two, they can do multiple things with the same technology. The maturation for those of us on the provider side is identifying what companies can actually do, because everyone will sell you that they can do everything. It’s going to be impossible to work with ten companies and keep them each in their lanes. Now you work with one, two, or three that can cover the entire pool for you.

Ritu: That’s a really good answer — especially pertinent today with all the recent announcements from Epic and the other major players. You’re right, nobody’s going to stick to their lanes, and AI is what’s making that possible.

Rohit: Roberta, tell us a little about your background and how you got to where you are today. Listeners usually like to hear that journey — how did you get interested in this space, and what’s been motivating you?

Roberta: I have a 24-year-old daughter who’s currently working for FEMA in Washington, DC and applying for medical school. She’s very focused on disaster medicine — she’s an EMT, got involved with the Texas Department of Emergency Management, and has known exactly what she wanted to do for years. That clarity was partly taken from me, because I also decided what I wanted to do when I was 13 or 14. I started candy striping in hospitals, fell in love with hospitals, and knew I didn’t want to do hands-on medicine. If you’re not going into hands-on medicine, you get involved in the business side. So right out of college I worked at an insurance company for a year, went back for my master’s at Hopkins, then went to work for what is today CMS — Medicare and Medicaid — under the Clinton administration. From there I went into consulting, then in-house at Mount Sinai, and have been at Houston Methodist for 25 years now.  Looking back at that career trajectory, I got into this field to improve healthcare — not from the patient care side, but from the administrative and policy side, recognizing that policy alone could not do everything. My passion for innovation came from watching what innovation was doing in other industries and feeling strongly that it could be brought to healthcare. I’m a late bloomer on the tech side — I’m not even sure I’ve fully bloomed yet. But I understand operations deeply: how you staff, how you change practitioners, what it takes to make things work at the front line. Marrying that with an understanding of what newer technologies are doing and how patients, physicians, and staff are interacting with them — and recognizing that what those technologies did for other industries, they can do for us — that’s the intersection. The issue with many technology companies is they don’t understand the inner workings of how something would have to get approved, what it takes to drive change, or how fragile the healthcare system actually is. You can’t disrupt one piece and assume it won’t impact everything else. But as we chip away carefully, piece by piece, we can make significant improvements in how we deliver care and in the cost of healthcare.

Rohit: That leads naturally to my next question. You mentioned innovation and how you’re embracing it at Houston Methodist. I was fortunate to attend your presentation at the HIMSS AI conference in Boston, and I know Houston Methodist has a defined center of innovation strategy. Would you like to share a little more about that?

Roberta: We began this process years ago. It’s called the Center for Innovation, and we sometimes refer to it as the “digital innovation-obsessed people.” It’s a group of roughly half IT and half operations people who sit together and think through every technology that comes through the door — evaluating the ROI, identifying what it can bring to the front line to improve the clinician’s world and the patient’s world, but also what it returns to the institution. The goal is to give people back time, give people back joy, and give them back the reasons they got into this field. Nobody got into nursing to click buttons or be a slave to an electronic medical record. They got into it to treat patients and feel that sense of accomplishment when they’ve done their job really well. I sometimes get in a little trouble for saying this, but my view is it’s not about ratios and benchmarks. The question is: did you come to work, feel like you did an amazing job, and leave without the anxiety that something was left undone? In these new scenarios, old benchmarks are becoming less relevant — the studies behind today’s benchmarks were done on old technology in different points in time. It’s up to us not only to introduce new technologies, but to push the limits and change the way we deliver care, whether at the bedside, in the home, or in the clinic. And we’re seeing it happen. We’re seeing behaviors change and the way people do things change, and that’s really heartening.

Ritu: At that presentation Rohit shared with me, you described some remarkable numbers around your agentic AI implementation — Houston Methodist achieving a 95% patient satisfaction score for ease of scheduling and generating significant ROI. But as you said, you can’t use the same ratios to measure the clinical side or physician satisfaction. Why have those proved so much harder to quantify, and where do you see the boundary between what AI agents can safely take over and where the human factor still plays an irreplaceable role?

Roberta: Scheduling is a great case study in itself — I could talk about it for a long time, and our leaders Tisha Montgomery and Adam Meyer could talk for hours. But it’s a great example of how technology and change management have to fit together. One without the other doesn’t work. Layering technology onto an old process just costs you more, because you still have the old process and you’ve added the technology on top. Think of the old paradigm where one scheduler serves one or two doctors. If you try to layer technology into that same model, you can’t get any ROI because that person still needs to answer the phone and handle surgical scheduling. You can’t eliminate the role. But when you say, “I’m going to take five schedulers, put them together, and add technology,” suddenly you probably don’t need five schedulers anymore — maybe three, maybe two. That requires change, because people are used to having a dedicated scheduler. But to get from five to two, you first do online scheduling, which requires doctors to put their templates online and keep them accurate. Once that’s in place, there are still phone calls — so who answers them? That’s where our bot, Zoe, comes in. We defined exactly what Zoe handles and when the call drops to a human. Tisha told me a great story where people were calling to say Zoe wasn’t working — it wasn’t that Zoe wasn’t working. Zoe was doing exactly what she was supposed to do, routing clinical questions to the clinic. The issue was the clinic staff weren’t logged in and weren’t picking up. If you don’t have the end of the pipe staffed, it can look like your agentic system is failing when it’s actually working perfectly. Setting up those workflows properly — Zoe gets three tries, then it drops to a human; certain phrases automatically drop to a human — means the human now has more time for the truly complex calls. The combined result of template work, online scheduling, and agentic AI has allowed us to schedule for far more doctors without adding staff. One scheduler can now handle ten doctors instead of four, and a much higher volume is handled online. None of that was possible without rethinking the process first.

Ritu: You’ve made a point we’ve heard from other CIOs as well — the bolt-on approach doesn’t work. You really have to rethink and reevaluate the process before adding AI on top, otherwise you won’t get the transformation you’re expecting. Thank you for explaining that so clearly, Roberta. Rohit, would you like to ask a question?

Rohit: There’s been a huge shift toward virtual medicine at Houston Methodist, Roberta, and it’s especially interesting to us because we’re working with a startup in teleophthalmology as well. Could you tell us how the virtual portfolio has grown over the last five years, and how you’re expanding it with more specialty services?

Roberta: This is without a doubt the direction we’re all heading. We have an enormous number of baby boomers moving through the care system who are older and need more care, while at the same time we have a reduction in the number of practicing physicians in some areas — rural and otherwise — and real access issues in many places. We’re spending a lot of time transferring patients who don’t necessarily need to be transferred, which increases their length of stay, increases resources consumed, and is often unnecessary. Even for Houston Methodist, with eight hospitals ranging from a hundred beds to a thousand beds, each hospital has needs for on-call and consultative services in the moment. Should a patient have to be transferred to our flagship for ophthalmology? The answer is no — you connect with a company that can provide those on-call services. Companies like Genome, which provides genetic counseling on call, with neurological and cardiac genetic counseling specialists — I can’t own ten independent specialists in every subspecialty of genetic counseling. But when we pool our resources, we can each afford a piece of one, and that’s the centralized model. Whether I own the resources or contract for them, the concept is the same: centralize and deliver that care on camera.  We are all in. We have cameras in every room, every operating room, throughout our emergency rooms, and across our clinics. The concept is how do we bring care to you in the moment. I’ll give you a great example even for our current physicians. We got a note from one of our hospitalists who had just left the hospital when he got a new admission. He was picking up his kids, so he said, “I’m just going to camera in.” He did the full assessment, got the patient started, noted he’d camera back in with his findings, and then wrote to us: “It was so great. I didn’t have to drive back half an hour to do that admission. I was able to do it on camera.” That’s our own doctor. Beyond that, we have contracts with multiple providers who help us in areas where we’re short.

Ritu: Switching tracks for the last question — with LLMs and AI agents, performance can shift after deployment, but healthcare has traditionally validated technology only at implementation and assumed it stays static. Do you think this requires a new model of governance? Did Houston Methodist have to rethink that?

Roberta: One of the things I’m most proud of is that when we sat down a few years ago to redo our maturation plan, we said even then: “Be careful. Be wary. Make sure the agents you deploy will not increase costs.” We were already concerned about taking sufficient, baked-in, cost-effective technologies and layering in more expensive ones on top. Agents have the potential both for nirvana and to break the bank. For each one you deploy, the answer is “it depends,” and you have to think it through carefully. Your trials have to be very defined. You have to confirm you’re getting the outcomes and being responsible with the dollars — but that doesn’t mean you should ignore agents or pretend they’re not coming. Go in with an open, skeptical, questioning mind. And always remember: your job is to improve care, improve the workforce, and reduce cost. If you can answer all three of those with a yes for a given agent, move forward and don’t be afraid.

Ritu: We’re almost at the end of the podcast. Would you like to share any closing remarks or lessons learned for our listeners?

Roberta: Change is definitely the hardest part — getting people to understand, to change, to believe. I’m probably one of the greatest evangelists and also one of the greatest skeptics at the same time. I was talking with a group from Rice yesterday and they were excited about something. I told them: “I’m excited about it too. I still don’t think what you’re describing will mature for a number of years, and I don’t think it’s going to deliver the ROI you’re projecting.” Those of us in the field are working with precious few dollars, so anything innovators bring us has to return real value — not “this will bring value if you just get everyone to change and push the whole ball uphill.” That is a very hard ask. My advice: be prepared to really understand the struggles of getting your technology to a point where it returns value, and be honest about where you can and cannot reduce costs for a healthcare system. It’s both an excitement and a warning.

Ritu: Great answer — in a nutshell, ask the right questions and stay questioning. Thank you, Roberta. Thank you so much for joining us today on the podcast.

Roberta: A pleasure. Thank you for having me.

Rohit: Thank you, Roberta.

—

Subscribe to our podcast series at https://thebigunlock.com/ and write us at info@thebigunlock.com    

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.

Ritu: Hi, listeners. Welcome to Season Seven of the Big Unlock Podcast. My name is Ritu M Uberoy, and I’m managing partner at Damo Consulting and your host today along with Rohit. Today I’m delighted to welcome Shailu Verma, CEO and co-founder of Mila Health. Shailu has built technology businesses at the intersection of healthcare and AI, serving as Chief Product Officer for UnitedHealth Group’s Strategy and Innovation, leading product at Amazon Web Services, and previously co-founding Landwords, which was acquired by Reuters. At Mila Health, he’s focused on one of healthcare’s biggest operational challenges: how to use AI agents embedded directly into clinical workflows to automate care coordination and improve patient engagement. We are really looking forward to an engaging conversation today. I’ll hand it over to Rohit for a short introduction, and then it’s all yours, Shailu. Thank you for joining us.

Rohit: Thank you, Ritu. I’m Rohit Mahajan, co-host of the Big Unlock Podcast and CEO of Damo Consulting. Very happy to have you on the podcast, Shailu. Over to you for your intro.

Shailu: Thank you, Rohit and Ritu. It’s a pleasure to be here. I’m Shailu Verma, CEO and co-founder of Mila Health. Prior to founding Mila, I worked briefly as a consultant at McKinsey, then started my first company in the computer vision space, which was acquired by Thomson Reuters. I went on to join the AWS AI computer vision team in 2016, building some of the first machine learning and AI APIs that came out of AWS, and then led the machine learning team at UnitedHealth Group. Healthcare has been a deep passion — my wife is a physician, I have two daughters, one in medical school and one working toward getting in. Getting to apply technology in healthcare has been a dream, and that’s how Mila Health was founded.

Ritu: Most of the healthcare industry still thinks of patient engagement as reminders and messaging — more apps that patients have to use. But you’ve argued that the real opportunity is AI-driven care coordination that actually completes clinical workflows and helps the patient. What does that shift look like in practice, and why is it so much more impactful than traditional outreach? And this wouldn’t have been possible two or three years ago — but now, with AI, we finally have the tools. What does Mila Health see as the real opportunity here?

Shailu: Let me go a little deeper into the problem. Healthcare organizations have patient engagement tools, EMRs, and many different software systems that they’ve invested in over time, all connected in a complex web. But each platform follows its swim lane very narrowly — patient engagement does reminders, EMRs handle records, payment systems handle billing. The aspect of actually taking care of a patient through their care journey is still done mostly manually. Reaching out to bring someone in for an annual wellness visit, preparing them for a procedure like a colonoscopy, guiding them on medications and diet, following up afterward to determine whether they need another procedure in five or ten years — all of that is still largely manual work. It’s a $334 billion problem. Connecting these complex systems and closing the gaps in patient care was a hard problem a few years ago. Now, with large language models, we have the opportunity to reduce that enormous manual effort in giving patients access to care and coordinating care after appointments. That is Mila’s mission — and to answer your question directly, while patient engagement tools address reminders, there are still many gaps in full care coordination, and that’s what Mila is solving for.

Ritu: You’ve said that organizations moving fastest with AI are not just buying another point solution — they’re embedding intelligence into the systems clinicians already use. Why is that distinction so important, and what have you learned from driving adoption inside health systems? How do you get them to adopt yet another solution when they already have a whole range of apps and software?

Shailu: To automate the vast majority of patient care journeys that are done manually, and to make it easy for our customers, we knew we had to build systems that get embedded into existing software. We took inspiration from companies like AWS, Stripe, and Twilio — they have APIs that get embedded into an existing stack and are easily configurable. A health system shouldn’t have to reconnect Mila to their EMR; it should come embedded and connected through APIs. We built Mila to be completely embedded — you can turn it on in days or weeks rather than months of complex integration. That makes it part of an existing solution rather than another point solution. Point solutions have two problems: implementation time, and they become yet another swim lane. We want to be the interconnectivity between swim lanes, not create a new one.  We built Mila as a set of APIs that any existing software system can adopt to automate across other systems. To give you a concrete example: we are live in over three hundred dental organizations in the US. Each has an EMR, a practice management system, a payment system, and an insurance verification system. Our APIs are embedded in the practice management system those organizations already use. To adopt AI for care coordination, all they have to do is flip a switch in their existing system and say, “I want AI to reach out to patients who haven’t had their cleaning in the last year.” No separate EMR integration, no practice management integration, no telephony integration required — it’s already built in because the APIs are embedded in the stack. That’s how an API-enabled, easily configurable solution scales effectively and makes it easy for customers to adopt.

Ritu: We’ve heard repeatedly from C-suite leaders that healthcare has no shortage of impressive AI pilots, but very few reach enterprise scale. You’ve described Mila now supporting hundreds of organizations because of ease of integration. What were some of the other key architectural, clinical, or organizational decisions that enabled you to move beyond pilots to real operational deployment?

Shailu: Our enterprise scale has happened primarily by making it simple for organizations to adopt — because it’s embedded in their software stack and almost invisible, the way Twilio is invisible in software today. And we address problems that are critical to their immediate needs. Take the dental organizations I mentioned — they’re short-staffed, unable to consistently reach their patient panel or follow up effectively after care. Many calls go unanswered, hit answering machines, or come in over weekends when patients need help immediately. We embedded Mila into the practice management stack they already use and solved the real problem: answering patient questions 24/7. Patients can call any time, change or create appointments, ask questions. We also built in a human-in-the-loop mechanism — when Mila can’t answer, a human steps in. That makes it a safe, easily implemented solution, and that’s what enables enterprise scale versus ongoing pilots where you have to prove things over and over. The formula is simple: make it simple, solve real problems, and bring a human in the loop when needed.

Rohit: As you scaled the business, what were some of the core challenges you faced — both business and technical — especially given the wide variety of practices you work with, from small to large? How do you handle the integrations?

Shailu: Building a startup is hard, and building one in healthcare is even more complex. The challenges are mostly universal startup challenges — get more customers, get more funding, build the product, keep the team motivated. Getting that virtuous cycle moving is the core of the startup journey. On the technical side, three tenets that we established early have been foundational. The first was to build as an API-first product — white-labeled, easily adopted by any software system, easily configured. Not a point solution. The second was configurability. Every practice, small or large, has its own rules — their own three-inch binder of processes. Mila needs to adopt that binder for each office and adapt accordingly. The third was safety and governability. Mila must be consistent — if a patient asks the same question five different ways, Mila should give the same answer each time. It should be safe. And a health system of any size should be able to govern it and understand why Mila said what it said. We established these three tenets before we wrote a single line of code, and they have held true to where we are today. That is one of the key reasons we’ve achieved the penetration and enterprise scale we have — it makes adoption easier, governance easier, and change management easier for our customers.

Rohit: What are some of the new things you’re working on? Products in SaaS are always evolving based on customer feedback. What’s bubbling up to the top? And I’m curious about the fast-changing LLM landscape — with Chinese models emerging, cost structures shifting. How do you navigate that?

Shailu: There’s a common thread to both questions. On the technical side, we’ve built a neurosymbolic graph that sits on top of large language models. We are LLM-agnostic — we swap models regularly depending on the task, optimizing for cost and efficacy. Our thesis has always been that in healthcare, you cannot let an LLM use a stochastic bag of words to answer a patient’s question. The health system needs to be able to configure and control what Mila says, and there needs to be explainability for why Mila said what it said. With that architecture, we welcome the changing landscape — foundational models getting better and cheaper. Token costs have already come down a hundredfold, and we think they’ll come down another hundredfold. But the last-mile problem of governance, consistency, and configuration remains, and that’s where Mila has innovated. That’s also where our ongoing development is focused: moving from conversation to autonomous execution of tasks. It’s one thing to have a ChatGPT-style back-and-forth. What matters for our customers is execution. We’re working with a large payer right now where we’re calling Medicare Advantage members to schedule their first annual wellness visit appointment. Previously, the payer would spend three months preparing with a call center and achieve an outreach rate of fourteen to eighteen percent at a cost of twelve to fifteen dollars per patient. With Mila, we’ve reduced startup time from three months to six weeks, increased the response rate from fifteen to eighteen percent to over fifty percent, and reduced cost per outreach to one-fifth of what it was — all while having a conversation with the patient and actually executing the appointment scheduling. That last-mile focus on efficiency and outcomes is where we’re doubling down.

Ritu: Many CEOs and C-suite leaders are feeling pressure to do something with AI but are unsure where to begin. Based on what you’ve seen across your customers, what’s the biggest misconception about deploying AI in healthcare production today? And where would you recommend they start to generate real, measurable ROI?

Shailu: The place where I’ve seen success happen most consistently is where AI solves a revenue problem or a patient intake problem. Every provider in a health system has thousands of patients in their panel. A GI doctor may have twenty thousand; a primary care physician three to five thousand. If you ask almost any of them, they’ll say forty to sixty percent of their patients haven’t been seen often enough or haven’t completed things like their annual wellness visit. The problem is twofold: patients aren’t being seen, and health systems have quality and prevention metrics — colonoscopy screening rates, cancer screening rates, annual wellness visit rates — that they need to hit. And of course, each visit generates revenue. We’ve seen real success when we address the simple problem of reaching out to this disconnected patient population, activating them, and scheduling their appointment on a single outreach call. That solves the metrics problem, addresses the patient care gap, and generates meaningful revenue. A concern I hear from leaders is: what will patients think of an AI agent calling them? We’ve now done this millions of times. Roughly three to four percent of patients say they don’t want to speak with an AI — in which case, a human steps in. Some hang up. But the vast majority, ninety to ninety-five percent, are genuinely happy to hear from their care team and learn they can schedule a visit right then on that call. That is real change. And once they’re back in the system, everything else follows naturally — follow-up care, ongoing coordination. That frictionless starting point is the most powerful place to begin.

Rohit: Any thoughts about the future or closing remarks for the audience?

Shailu: Watch for the conversational part of AI to move from conversation to execution — to actually getting work done. That’s super important for Mila, but it should be equally important for healthcare leaders trying to implement AI effectively. And people should not be integration layers. The vast majority of the manual work happening today is integration work — verifying insurance, closing scheduling gaps, following up on outstanding items. These are simple gaps that AI can close. We don’t need humans calling in to update an insurance record. Let’s start simple, focus on execution, and use the tools we have. It isn’t rocket science. We’ll solve bigger problems over time, but these easy wins are the right place to begin.

Rohit: Great thoughts, Shailu. Thank you for joining the podcast. Over to you, Ritu, for closing.

Ritu: Thank you, Shailu. It’s been an absolute pleasure. Thank you for sharing your insights, and I hope our listeners take away a great deal from today’s conversation.

Shailu: My pleasure. Thank you for having me.

—

Subscribe to our podcast series at https://thebigunlock.com/ and write us at info@thebigunlock.com    

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.

Ritu: Hello, listeners. Welcome to Season Seven of the Big Unlock Podcast. My name is Ritu Uberoy, and I’m managing partner at Damo Consulting and your host today. We are absolutely thrilled to welcome Dr. Ami Bhatt to our podcast. Dr. Bhatt is a cardiologist and the Chief Innovation Officer at the American College of Cardiology, and Chair of the FDA Digital Health Advisory Committee — two roles that place her at the center of how healthcare institutions adopt, govern, and scale technology. A Harvard and Yale-trained cardiologist, formerly at Mass General, Dr. Bhatt has spent her career inside the systems that deliver care, which is precisely why she understands where and why they break. Her work spans AI governance, digital health strategy, and health equity, and we could not be more thrilled to have her here. Welcome, Dr. Bhatt.

Dr. Bhatt: Thank you so much for having me. I really appreciate it.

Ritu: We like to ask our guests about their origin story — how they got into medicine, healthcare, and technology. Would you like to share something?

Dr. Bhatt: Like every first-generation kid in the US, I liked science, I liked people, I went into medicine — that’s what I always thought my story was. It wasn’t until I took the time to think a little deeper that I realized what I love about medicine. In no other profession do you get entered that deeply into somebody’s life within fifteen minutes of meeting them — not just their health, but their family, their hopes, what they want. That connection has been the driving force behind why I became a clinician and why I stayed one for so many years.  I practice adult congenital heart disease — a young population. Kids who grew up and came to me at 16 or 18, and those teenagers became my patients into their 20s, 30s, 40s, and 50s. They were living with lifelong disease. Coming to the hospital wasn’t a one-time thing — it was a reminder of a medicalized childhood. I had patients who said the smell of the hospital made them anxious. So about seven years before COVID, I started doing telemedicine for my patients. At the time, not many people were doing it, and colleagues would ask, “Dr. Bhatt, how are you going to take care of these complicated patients without touching them?” I started exploring what digital technologies I needed — digital stethoscopes, handheld ultrasound, image sharing — and how to teach patients to feel empowered and understand what they needed to do.  I had a patient once up in Maine whose local doctor said something was wrong with his heart because he had passed out at softball. He said, “No, I tripped — and my heart always sounds like that.” He played a recording of his heart murmur that I had recorded in 2014 with one of the first digital stethoscopes. I had told him: “If anyone ever tells you this is a problem, you play that recording.” That patient felt genuinely empowered. That model — staying at home, taking visits remotely, teaching local providers how to care for them, while knowing I was there when they needed me — carried with me through outpatient cardiology at Mass General, through COVID, and into how I think about innovation today. How do we empower patients in the community with wearables and other tools to understand their own baseline, and when a trigger or a change appears, know it’s time to escalate to clinical care? That’s how I ended up where I am today.

Ritu: Thank you, Dr. Bhatt. Starting telecardiology seven years before COVID — you were truly a pioneer. I was reading your work, and you’ve written that clinical acumen supersedes AI every time. That’s a powerful statement in this era when AI models are increasingly capable. You’ve also said that clinicians are not short on intelligence or judgment. So how do we build healthcare systems that benefit from AI while ensuring clinicians don’t lose the observational skills, intuition, and pattern recognition they’ve spent years developing?

Dr. Bhatt: Humans are short on compute power — that’s what we are. The amount of information available to us now far exceeds what the human brain can utilize. When you’re sitting in front of me, I have access to not just your health records but your personal and family history, your preferences, your social determinants, your wearable data, your labs, your imaging, the research on the multiple diseases you may have by age 60, and novel findings that haven’t yet made it into the guidelines. How can I process all of that in twenty minutes? I cannot. That is the role of AI: to surface knowledge, to surface information, and to use compute power to see relationships and trends that would take us a very long time to identify. Back in the day, there was less information and more time. One chapter about your disease. Just the patient in front of you. No EHR. An hour to ask the same question again and get a full story. Nowadays, who has an hour with a patient? We understand why colleagues go into concierge medicine — because that intimate connection is the magic of caring for patients. In its absence, we need compute power to get there faster, and then use that information and those insights to ask: does this fit this patient? Let me use my clinical acumen to decide what happens next. That’s the goal.  I always tell my fellows: you have to use AI until your brain hurts. A recent paper I read yesterday showed that knowledge workers actually spend more time, not less, because of AI — asking questions, interacting, refining. Most of us have accepted that AI is here. We see the numbers. But now the challenge is understanding when to use it. When should you deep dive with it? When does using it take time away from a patient conversation? We haven’t fully studied that yet. At the American College of Cardiology, one of our goals is to have our own members using AI for question-and-answer tasks and understand: how do they know when they’re getting better results? How can they recognize when they’re being led astray? We are at the very beginning of understanding how humans and AI will interact in clinical medicine.

Ritu: Context comes up repeatedly in your recent work. You’ve argued that medicine is not just about recognizing patterns but about understanding the patient’s lived experience and the clinician’s accumulated experience. As healthcare moves toward increasingly autonomous AI systems, how do we ensure that context becomes a design requirement rather than an afterthought?

Dr. Bhatt: The first thing I think about is starting with the patient. The patient is not given the level of respect they deserve in this equation right now — though we’re moving toward it. Rather than me trying to figure out your social determinants, why not have you bring that information directly to me? How do we capture a patient’s feelings, tendencies, preferences, and ways of engaging with healthcare? What is best for this individual, and how should they be engaged? We ask how a doctor likes their notes written, but we don’t ask how a patient likes to be engaged. We do a little of it — do you prefer telehealth or not? — but there’s so much more depth to that question. We have to start by engaging patients and helping them understand how they want to interact with the system, express it, quantify it, and then communicate it so the system can respond. That’s number one.  Number two is around agentic AI, and it’s actually one reason agentic AI can sometimes be better — because I can always tell my AI: say this at an eighth-grade level, I don’t like that tone, say it differently. I can’t say that to a colleague sitting across from me. My patients actually did this all the time. Young patients in their 20s and 30s would say, “I didn’t understand that, Dr. Bhatt. Can you draw it again? Your drawing isn’t very good — can we try something else?” Honest, direct feedback. I want that for every patient. Agentic AI can provide that, sometimes better than a rushed clinician. Any clinician given enough time will do all of that — we’re just not given the time.  The second thing in agentic systems is where the clinician places the guardrails. I wrote about this in my most recent Substack: in cardiology, there are specific places where AI should not make a decision without a human. We have to define those hard stops for the companies or systems implementing this. But beyond that, as agentic AI is being built, we should be able to give our input case by case. I was just talking this morning about a company called UpDoc that is trying to take clinician input as agentic AI is built and let individual doctors set their own guardrails — telling the system what they’re comfortable with and not comfortable with. That’s a lot to manage, but I think those are the right things to study: how to help agentic AI make the right decision at the right time, with the right guardrails, so physicians feel in charge — just as we want patients to feel in charge of their own process.

Ritu: You’re also challenging the industry to think beyond whether AI can generate the answer, and asking what happens after that answer reaches the patient. As health systems deploy more AI-generated insights, do you think the next frontier is actually communication — helping patients understand, trust, and act on these recommendations?

Dr. Bhatt: It’s already communication. Look at the wearable industry. The reason people across all socioeconomic levels, educational backgrounds, and geographies are increasingly wearing some form of wearable is that they want to engage, learn, and find data about themselves. And the reason it works isn’t only that the person wants it — it’s that the systems these apps create are inherently engaging. They know how to make a patient feel empowered. That’s already happening. What I’m focused on is how we take that industry and bring it into the flow of clinical care — and back out on the other end. We lose people everywhere in clinical care. Technology companies have a responsibility to come with us on the whole journey: “I see a signal. We’re going to provide some care. Now they need hospital care. They’re back out — and you’re back in the system that caught you in the first place.” That’s what we’re trying to build.

Ritu: Speaking of agentic systems, tell us more about the ARPA-H Advocate initiative, because they’re using agents and agentic AI to do exactly what you’re describing — continuously monitoring and intervening. What are your thoughts on that program?

Dr. Bhatt: Haider Warraich is a friend — he’s a cardiologist here in Boston, a heart failure physician. With ARPA-H, you can apply with your own idea and say, “Can I have funding to make this happen?” And that’s what he did. What I like about this approach is that it’s a pure research approach. The goal is to prove that you can have an agentic system that works, that can risk-stratify, that knows where a human in the loop is necessary, and that can monitor what happens as it goes along. The key problem we have right now is: we implement something — digital health, AI — and then it’s out there, and we don’t know what will happen afterward, what will change. We don’t have an infrastructure for post-deployment monitoring. What I’m most excited about with Advocate is that we’re going to have concrete examples of what post-deployment monitoring looks like — how we sense trends and shifts, how we decide to iterate and adapt. That’s the piece we’ve been missing. How do we keep track of what’s happening after we deploy?

Ritu: Dr. Warraich was actually on our podcast a couple of weeks ago talking about ARPA-H and the Advocate program — a very interesting conversation. Tell us more about your work on the FDA Digital Health Advisory Committee. This comes up constantly in our conversations with CMIOs and CIOs — things are moving too fast for regulation to keep up. By the time you get FDA approval, the underlying model has already changed. How do you see this gap closing?

Dr. Bhatt: I have the utmost respect for my colleagues at the FDA who are there full time. As inaugural chair of the Digital Health Advisory Committee, one thing became very clear over those two years: there is a real need to convene patient groups, industry technologists, and clinicians together to understand how the FDA can create an infrastructure for care — not simply regulate or deregulate, which is where we tend to get stuck. We need to understand from all of those perspectives what guardrails are needed, what the technology companies need, and what fits the clinician’s workflow. That unsiloing is what’s happening at the FDA right now. You’ll find them at every table, in every room, maintaining their commitment to scientific rigor. FDA approval is the table stakes — it means your technology works. But we’re now thinking about what comes next: not just post-deployment monitoring, but a pre-control change plan. With a medical device like a valve, you can tell the FDA what’s in the pipeline. With AI, I don’t necessarily know what model changes are coming. So how do we use real-world evidence to understand where things are going and how they’re changing?  The FDA did allow some trend measurements and monitoring capabilities in the cardiometabolic space to proceed without formal approval, which was announced earlier this year. That creates real-world evidence about how quickly you can adapt from community signals — sick or not sick, at baseline or changing — and route patients into the clinical arena accordingly. Agentic systems can handle the lowest risk; high risk clearly needs a human; we’ll be in the middle ground for a while. I’ve learned that real-world evidence is essential. Everyone is now at the table, including the FDA, which I love. But we’ve never had a time when change itself is the constant — not just the technology, but the uptake, the expectations of the clinical network, and the demands of patients. All of that is changing simultaneously. All we can do is keep working together, look for real-world evidence, and wherever we can find data, value it and study it.

Ritu: Tell us a little more about the Elevate Leadership Program at Mass General — with everything moving so fast, clinicians need to be up to speed, but you’re all already so busy. How do you find the time?

Dr. Bhatt: About four years ago, a grateful patient family foundation was willing to give $3 million to Mass General Hospital to fund a new style of leadership course. The course I co-wrote with my colleague Eytan Shapira from MIT was designed for leadership-level clinicians — cohorts of fifteen — who were about to lead large groups of people. The longstanding problem in healthcare is that the better you do as a clinician and researcher, the more likely you are to receive an administrative or managerial role. Those skillsets don’t automatically overlap. They can, but they need to be taught. There have been many leadership programs for senior people, but I wrote this one because I saw the technology change coming — I had already been in it for ten years. COVID was the culmination of many of us in digital health saying for a decade: high-quality care in the community where people live, tech is coming and it’s going to come faster. We weren’t ready.  The course works on how you first evaluate yourself as a leader and understand the discomfort that comes with change — how to express and share that discomfort while still being seen as a leader, and how to look at what’s coming and identify the true unmet needs versus the shiny things that aren’t necessary right now. We’re also reversing a long-standing dynamic in academic medicine where clinicians kept industry at arm’s length. Now we’re saying: let’s talk about what technology you have, because if you don’t hear from me what I need and how it would fit, you’ll build something that doesn’t get used. Eighty-six percent of cardiovascular digital health and AI technologies that pass the FDA sit on the shelf — they never get deployed. Passing the FDA does not mean success.  The goal of Elevate is to create a community of leaders who lead not only with empathy but with an understanding that discomfort is constant — it’s not going away, and it’s because of constant change. I am here with you, respecting that, understanding the challenges, and helping find the right next step to move forward. That was my last project before I left MGH, and I’m really proud of it.

Ritu: What you said is so valuable — change is inevitable, and you really need to face it. Preparing people to sit with discomfort, to accept that they won’t know everything, but that as long as they keep learning they can navigate it.

Dr. Bhatt: That’s exactly right. And change is no longer in the future — it’s right now. If you run a practice today, technology is coming at you out of a fire hose, constantly. If you are a healthcare leader in the next decade, there will never be a moment where things feel stable. They will always be bubbling. You just have to decide: when am I doing things, and how will I act despite constant change?

Ritu: Thank you, Dr. Bhatt. Would you like to share any closing thoughts with our listeners?

Dr. Bhatt: The most important conversations happening right now are those where technology, clinicians, and patients are put in the same room to think about what the systems of care should actually look like. I would love for every listener — whether you’re a venture capitalist, a clinician, or a patient — to ask: what is the infrastructure for care in my world? Do we have one? Do we have guardrails? If not, who do I need to talk to in order to put something in place? We’ll keep working on this from the national level at the ACC and through the technology companies I work with, but I think each individual taking a moment to ask “is there an infrastructure, or is healthcare just hard because we don’t have the right one?” is powerful. The infrastructure we have was built in the 1970s, and that is not today’s healthcare. Look for infrastructure — it sounds boring, but it’s everything. And if people want to reach out, I’m Dr. Ami Bhatt on LinkedIn and Substack — I’m happy to talk and hear more ideas.

Ritu: Thank you so much, Dr. Bhatt. Lovely talking to you, and I’m sure our listeners will take a great deal away from this conversation.

Dr. Bhatt: Thank you for having me.

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Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.