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 along with Rohit. Today we are welcoming Daniel Barchi to our podcast. Daniel is the CIO at CommonSpirit Health, where he leads enterprise technology strategy, digital innovation, AI, and cybersecurity. He brings more than two decades of healthcare technology leadership, having previously served as CIO at NewYork-Presbyterian, Yale New Haven Health System, and Carilion Clinic. Under his leadership, CommonSpirit has scaled its AI portfolio to approximately 250 applications, generating more than $100 million in annual savings. Welcome, Daniel, and thank you for being our guest today.

Daniel: Thank you so much, Ritu. It’s great to be here with you and Rohit.

Rohit: Hi, everyone. I’m Rohit Mahajan, CEO at Damo Consulting and co-host of this podcast. Looking forward to an engaging discussion. Back to you, Ritu.

Ritu: Let’s start with how AI adoption is moving toward enterprise-wide transformation. CommonSpirit has scaled from approximately sixty AI tools in 2023 to about 250 now. But you’ve made the interesting observation that having 400 or 800 tools doesn’t mean you’re doing better. At what point does adding more AI become counterproductive?

Daniel: We’re very proud of what we’re doing with AI at CommonSpirit — we’re using it across clinical operations, HR, revenue cycle, and technology. We’ve been doing robotic process automation for well over ten years, but we leaned hard into AI over the past three years and grew from about sixty applications to about 260 today. That said, I don’t think 300 or 500 or 800 is the right answer. The number I’m actually most proud of is twenty — the number we’ve retired. And there’s another twenty or so things we’ve said no to. It’s not about saying yes to everything; it’s about being very selective in what is aligned with our mission and has positive ROI, clinically, financially, or from a patient experience standpoint. Being smart enough to retire tools that are no longer meeting their purpose — because they’ve been subsumed by others, are no longer sufficiently secure, or no longer meet our raised standards — is just as important as adding new ones.  The big picture: we’re at about a three on the Gartner maturity scale. We’re doing a lot and getting very mature, but we have ways to continue to mature, as most health systems would say. What we’re focused on is scale with discipline — strong AI operational governance, tracking who’s using each tool, the ROI, the benefits, the risks, and the plans for each one. We’re also leaning hard into platforms. At CommonSpirit we operate across twenty-four states, with more than 150,000 employee colleagues and more than 150 hospitals, so standardization matters. We’re going from nineteen EHRs to one, from nine different ERP systems down to one, from many business process tools to one instance of ServiceNow, and from many third-party ERP platforms to one instance of Salesforce. As we consolidate onto these platforms, we want to enable their embedded AI rather than always going to a third party or building it ourselves.

Ritu: You mentioned a multidisciplinary AI governance structure. With so many stakeholders involved and meetings happening bi-weekly, do you think governance can become a bottleneck to innovation? How do you counterbalance governance with speed — especially in scenarios like ambient documentation, where patient data use raises specific requirements?

Daniel: This is a question I’ve been asked in different forms, including when people ask about full AI autonomy. I think back to about ten years ago when I was CIO in New York City and one of our teams said, “We can build and deploy an app within forty-eight hours.” My first response was: “What app would we want to build and deploy in forty-eight hours?” Speed isn’t the point — strategy, planning, benefits, and integration are the point. Anyone who says good governance is a bottleneck to innovation misses something important: healthcare technology is really about 80% people, 15% process, and only 5% technology. Anyone who says “let’s ram AI through quickly with light governance” is hoping that technology alone will move the needle, when in fact there is so much work to do with people — better training, better resources, better tools — and process — better standardization, documentation, and use of data — before you even get to the technology piece.  Having the right governance committee evaluate every AI tool is not getting in the way; it makes us better and aligns with our mission. And when you think about who is on our governance committee — nursing, physicians, finance, operations, mission, IT, compliance, and our ethics team — they help us make better long-term choices than a tight group of technology and finance people making decisions alone.

Ritu: You’ve attributed $200 million in value captured from AI. We often hear that ambient scribing saves around sixteen minutes per physician per day — meaningful for quality of life but hard to quantify financially. Where is this return actually coming from?

Daniel: So many organizations, inside and outside healthcare, throw out big numbers about AI with very little to back them up. What you mostly see is people talking about their spend on AI — not the outcome. At CommonSpirit, we’re focused on defining value and then tracking it. We’re better at tracking the financial value because it’s quantifiable. Ambient scribing is one where many quality-of-life and quality-of-care benefits are not financially quantifiable but are genuinely important, and we plan to lean into tracking those non-financial benefits more. But we remain tightly focused on financial benefits because we want to know what we’re getting for our investments.  We’ve created a five-gate system for every AI tool. Gate one is the opportunity itself — say a physician identifies a new AI tool and process that could save $10 million in our operating theaters. Gate two, she comes back with a business plan showing perhaps $8 million in benefits after working through the details. Gate three, our finance team reviews it and determines the real value is closer to $5 million. Gate four, IT implements it — with available capital and resources, perhaps we can only implement half, realizing about $2.5 million in potential value this year. Gate five, we go back and measure what we actually generated — and in this example, the actual value captured is about $1.5 million of the original $10 million estimate. Health systems could say they’ve delivered half a billion dollars in AI value, but we say we’ve generated about $100 million because we can track the actual realized value for each tool.

Rohit: Daniel, you described a scenario where a clinician generates the idea that ultimately gets implemented — that’s very powerful, because they are the domain experts. How do you look at AI through the lens of clinician experience and reducing burnout, and what metrics are you tracking there?

Daniel: Clinicians don’t wake up every morning thinking about AI. They’re thinking about patients, quality of care, and the sustainability of the intensive clinical work they do. So AI is one tool we provide to help them — ambient scribing and others. But there are many other platforms technology leaders should be putting in front of clinicians to make their clinical lives better and give them the data they need. And we can’t focus solely on physicians. For ambient scribing, for example, we’re not only rolling it out for physicians — we’re exploring how to make it work for our nurses as well.  Beyond ambient, I’m proud of other things we’re doing to reduce bedside friction. Our Chief Physician Executive, Tom McGinn, has been working with colleagues nationally to create something called Predict PE. We just rolled it out at one of our hospitals in Texas — it automates the calculation of the Wells score to determine whether an ED patient may be having a pulmonary embolism. Based on clinical features the clinician observes, it recommends either a D-dimer blood test or a CT scan. Making the right choice improves efficiency, saves cost, reduces unnecessary radiation exposure, and speeds up the clinical process. Rolling out tools like this — generated by our chief physician executive or other frontline physicians — is exactly how the technology team can support clinicians in the greater good.

Rohit: A quick follow-on, Daniel: you have a very large employee base — around 150,000 people. How do you bring tools like ChatGPT or Gemini to an audience that size, given everyone is already using them personally and that naturally spills into professional life?

Daniel: Most healthcare is driven by labor — labor is more than 50% of any healthcare budget, and it’s people who deliver the care. So we want to make sure AI tools are supporting our colleagues. The fear around AI is mostly about whether it will replace people, and that’s not the right frame. With 10–15% annual turnover in healthcare, thousands of new people join every year. As a healthcare leader, I want to provide tools that support long-tenured colleagues in what they do, and also provide the most cutting-edge tools that our newest employees would expect when they join an organization like ours.  At CommonSpirit, we’ve made large language models available to colleagues through what we call a walled garden — a tool we built internally called Insightly. Instead of simply sending people to Gemini or Anthropic, Insightly aggregates the LLMs we use within our own network, allowing clinical and non-clinical colleagues to ask questions we wouldn’t want going into the public domain. You write a question, choose the model — Anthropic, Google Gemini, OpenAI — and get an answer from the model best suited to your need. It also lets us manage our token spend. A few months ago a specific model from one of our vendor partners was driving significantly more cost than we wanted, and we simply removed it from the catalog. That’s how we make LLMs safely available across the entire health system.

Rohit: What is your approach to buy versus build on new applications, not just AI? You mentioned retiring around twenty tools — is there still significant internal development happening, or is the trend shifting toward off-the-shelf solutions?

Daniel: We’re doing much more internal development than I would have expected a few years ago. About 47% of all our AI tools are built internally, about 31% are embedded in a core platform like our Epic EHR or Workday ERP, and only about 22% — and decreasing — are externally purchased tools. For external tools, there’s always a challenge: contracting, financial implications, due diligence on whether it’s better than an internal option. We prefer internal or platform-based solutions when we can. But sometimes, like with ambient scribing, it’s simply better to buy what’s available. And increasingly I’d like to grow that 31% platform number — if our EHR or ERP already has AI capabilities available, enabling those is the quickest path to ROI.  The 47% internal development surprised me. What I’ve come to see under strong leadership from our AI and data team is that we can quickly build light, nimble applications that meet a specific need. One example: nursing leaders in one of our markets needed a specific dataset and clinical workflow to focus on length of stay. They found a third-party Silicon Valley tool that would have cost roughly $5 million over three years, plus integration work. My team said: for what they actually need, we can build this in three or four weeks, stand it up, and run it for twelve to thirty-six months while we make longer-term platform decisions. When I say we’re building internally, we’re not building tools meant to last a decade — they’re light and nimble, they fill gaps, and they let us focus resources on immediate needs while we make thoughtful longer-term decisions about what we buy on a platform or from a vendor.

Ritu: You said 80% of healthcare is people and only 5% is technology. Other CIOs and C-suite leaders always tell us about the challenge of bringing everyone along and equipping them to handle new tools without triggering job displacement fears. How are you bringing 150,000-plus employees along safely and setting the right cultural tone around AI?

Daniel: It really starts with our board and our CEO, Wright Lassiter, and the framework he’s shared for how we use AI — in alignment with our mission. Wright said: “When we think about AI, we think about how it should help us accomplish our mission and our priorities: safety and quality first, then a better caregiver experience, then a more frictionless experience for our patients, and then — as an outcome of getting all those right — lower cost.” That ordering matters. It’s not about cost savings first; it’s about clinical care, caregiver experience, and patient experience first, with cost as the fourth priority. In that framework, AI is non-threatening — it’s there to support our people and our mission.  Beyond Insightly, which I described earlier, we’ve also created an AI Learning Academy — this was insisted on more than two years ago by Sunil Kakade, an outstanding AI leader in our organization. We have eleven classes, and more than 16,000 classes have been taken by our colleagues: caregivers, custodial staff, support teams, IT teams, food service workers — anyone in the organization can take them. By being thoughtful about why we’re using AI, making it available to everyone through Insightly, and offering free online courses, we create an environment where all of our colleagues can lean into what we’re doing with AI together.

Ritu: We would love to hear your origin story. You have an interesting background with the Navy. How did you get into healthcare and what brought you here?

Daniel: I was fortunate to serve our country as a naval officer driving ships at sea for about seven years. After leaving the Navy, I became a project manager in the telecom industry. When telecom went through major challenges in the early 2000s, I became a turnaround person — running small companies, finding investment, selling them, and making strategic decisions alongside a larger organization. In 2002 I got a call from the CEO of Carilion Health System in Virginia who said: “We understand you’re a bit of a turnaround person. Would you come run a biomedical institute for us?” To be truthful, I got a B in high school biology and was not well qualified for the job — but I ended up leading a team of PhDs and MDs doing thalidomide and small molecule research, along with private equity and venture capital investment into companies spinning out of university IP.  After a couple of years of success there, the CEO called and said: “We’ve let the CIO go. We need you to be CIO.” I had no healthcare technology experience whatsoever. That was 2006, twenty years ago, and I’ve had the chance to do it at progressively larger institutions in Virginia, Connecticut, New York, and now here at CommonSpirit. I’m really proud to bring that varied experience to healthcare technology and to what we do for our mission and our patients.

Ritu: Great story — thank you for sharing that. Any closing thoughts for our listeners? We used to ask for a year-out crystal ball, but now we ask about the next three to six months because the pace of change is so rapid.

Daniel: Leaning into AI in a thoughtful way is absolutely where we need to go — but nothing moves us away from providing human-based care at the bedside. Technology and AI support that care, but it is care delivered by humans that will win the day for the foreseeable future, and that human element is what makes the care we deliver genuinely better.

Ritu: Thank you so much, Daniel. Thank you for being on our podcast.

Rohit: Thank you, Daniel, and thank you for your service as well.

Daniel: Thank you both.

—

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 7 of the Big Unlock podcast. My name is Ritu M Uberoy, and I’m a managing partner here at BigRio and Damo Consulting. I’ll be your co-host today along with- Hello… Rohit for The Big Unlock podcast. And today, we are welcoming Dr. Richard Zane to our podcast. He is the chief medical officer and chief innovation officer at UCHealth, which is a ten-billion-dollar non-profit health system spanning fifteen hospitals and more than two hundred clinic locations.

And it’s really interesting that he has this dual role where he oversees clinical quality and safety while helping lead UCHealth’s work across AI virtual care and new models of care delivery. And he’s also a co-founder at the Care Innovation Center, where UCHealth partners with technology companies to develop, validate, and scale solutions, which is, very interesting to us, and we are going to ask him a lot of questions about that.

He’s a practicing emergency physician who previously held leadership roles at Brigham and Women’s Hospital and Harvard Medical School. We’re really happy to welcome Dr. Zane to our podcast today and glad to have you here. Thank you for being here with us today, Dr. Zane.

Richard: Thank you for having me.

Ritu: Would you like to add anything to that super long introduction?

Richard: I think that was an introduction that my mother would be proud of, so thank you for that.

Ritu: Happy to hear that. Rohit, would you like to introduce yourself, and then we can get started.

Rohit: I’ll say a quick hi. Hi, everyone. I’m Rohit Mahajan. I’m the CEO at BigReo and co-host of The Big Unlock podcast, and, very excited, Rich, to have you on the podcast with us.

We are looking forward to an engaging and interactive conversation. So back to you, Ritu.

Ritu: Thank you. So let’s get started, Dr. Zane. You’re now wearing both these hats, the chief medical officer and chief innovation officer at, two hats at UCHealth. Bringing these two responsibilities together, has it changed the way you think about innovation and particularly about these new thresholds that, AI solutions are now bringing into the picture?

And what do you think about the thresholds that AI solutions now have to cross before you’re willing to put them into this hospital setting and healthcare setting? Would love to hear your views on that.

Richard: That was a big question. To answer the first part, yes, I think it’s quite symbiotic that the chief medical officer role and the chief innovation officer role are together, at least it is for me.

Until recently, I was one of two people who had that role. Now I’m just one. We like to say here at UCHealth that when we think about innovation, we think about intelligence, and two terms that mean everything and nothing. And intelligence to us is making better decisions in healthcare. And those better decisions could be choosing pharmacotherapy at the bedside.

It could be thinking about ordering procedures for complex cases. It could be how to deploy capital. It could be how to better run an infusion center, outpatient clinic, et cetera. And we’re also very fond of saying that our innovation strategy is our quality strategy, is our virtual health strategy, is our informatics strategy, and is our IT strategy.

So the entirety of the way in which we run a healthcare system is focused on our patients, what’s better for them, and the best things for patients are making better decisions on their behalf So AI fits clearly within that because AI helps us make better decisions. The question is, where do you bring in human adjudication, and where do you allow AI to make or inform decisions?

And that’s the journey that we’re on now.

Ritu: Thank you for that answer, Dr. Zane. I think that’s becoming, more and more pertinent now because agentic AI has entered this picture, and UCHealth also seems to be moving beyond these individual AI applications towards a broader platform. We’ve all seen, A Bridge for Ambien, virtual health center applications, and you have a collaboration with Verily around data and AI.

So how do you see all these pieces coming [00:04:00] together? And in your opinion, what does this underlying architecture of like an overall AI-enabled health system look like rather than these fragmented pieces that are now, being put into place across health systems?

Richard: So I think that everybody starts off with point solutions and thinks about, do we have a point solution for detecting atrial fibrillation?

Do we have a point solution to optimize revenue cycle? And the question is, how do we set up a platform? And every third word now in the innovation world is platform, platform, platform. And how does it all come together and coalesce in one intelligent architecture? And I think it all has to do with culture, process, and tools.

So people, process, and tools is all healthcare is. And when you think about AI, I like to say that AI is math, it’s not magic. And there’s a lot that AI has in common with the practice of medicine. And if you think about how medicine is practiced, it really is pattern recognition, variable adjudication, and understanding the human connection.

Humans, on their best day, can adjudicate eight, maybe 11 contemporaneous variables. AI can adjudicate seven million. And I just made up that number. Could be more, could be fewer. And then from a pattern recognition, that’s why they talk about experienced physicians practicing because we can recognize patterns the more we see them, and it’s just as important to recognize when a pattern fits as when a pattern doesn’t.

That is by definition what AI does, is recognize patterns. So if we think about where it fits best and easiest, it’s usually in things that are purely math without A direct immediate human consequence. So I think if you look across all of industry, especially in healthcare, there isn’t a single institution that hasn’t implemented some form of AI, end to end or not, in revenue cycle and administrative automation.

And then many healthcare systems, including ours, have implemented AI end to end in patients as a customer, meaning when a patient asks for an appointment, when a patient asks for directions, when a patient asks for a prescription, when a patient asks for

prior authorization. The holy grail, so to speak, is can we have AI take a history, prescribe or recommend a treatment without a human in the loop? And the answer is not yet. We have not done that yet. I think that there are some point solutions that have done that under very, very specific circumstances.

So that’s how we are approaching this, is it’s really a no-brainer to do it in administrative automation. It makes perfect sense, just like every single person will book a flight or learn how to get to grandma’s house or grandma’s friend’s house using AI or an application. But it’s when can we rely on AI to inform a medical or clinical decision?

And I think every day it gets better and better. The biggest barriers, at least to me, are not the agents, and it’s not the engines, and it’s not the industry, it’s the regulatory burden on healthcare, both from a national perspective and a local perspective, on where the tolerance or allowance for the utilization of AI can be deployed.

Ritu: This, dovetails nicely into the next question, where we are talking about the Care Innovation Center, where, you’re co-developing, validating, and then sometimes even investing in these companies, and you have this hundred million dollar venture capability behind the Care Innovation Center.

So when you’re seeing these startups and when you’re looking ahead to these new technologies, which will do this piece, this clinical piece that you’re talking about, what makes you decide that the startup is worth partnering with, and what are the warning signs that tell you that this technology is never going to scale within the health system?

And we are particularly interested in this answer because we see companies in our venture studio as well. So would love to hear your perspective on this.

Richard: So the first most important thing for us is how did we come up with the problem statement? What problem are we trying to solve? And for us, the best problems are the ones that come from the people doing the work So the people at the front line identify a problem that they think technology may be a solution for.

And then we look at the problem statement, and we look at it in a very specific way, in a very structured way. And then we try and do an environmental scan and see if some other health system has solved this problem. And then we will learn from that other health system, and we will adopt, implement that solution.

But more frequently than not, there isn’t out-of-the-box solution. So we will think about how we can partner with a company, most commonly a technology company, sometimes a services company or combination of the two, and then partner with them. And that’s when how do you evaluate the company comes in. And as there’s a right size to it because, an angel investing company often doesn’t have the resources, so we’d have to think about are they really special and different.

And then on the other end of the spectrum, immensely large companies often can’t work in small point solution fashion because it just won’t move the needle. Google is not gonna do something that is gonna take three years to come to fruition and is not gonna generate billions and billions of dollars because it just doesn’t move the needle for their shareholders.

So it’s picking the right size company. And then we think about our ideal partners are ones where we’re a customer. We need the solution to work. We’re a co-developer. We can drive the solution, and then we participate in value creation. How do we invest in the company or how do we generate revenues from the solution, and then how can we help them scale?

We know that ninety-eight percent of medicine happens in the community, not in academic medical centers. So we’re very comfortable being customer number one, but we design for customers two through two thousand because if it won’t scale, it’s not gonna be of any use for us for longer than a couple of months.

So that’s the overarching approach. To answer your question specifically about when do we think something’s not gonna work, it’s really one by each. We are not afraid to fail. If my charge for my board is if a hundred percent of the things I work on are gonna work out of the box, I’m not trying hard enough, I’m not taking enough risk, and I’m not being sufficiently creative.

So one is, have they done it before? Have they started a company before? Have they run something before? As better than I, you bet on the jockey, not the horse. Do I think that they’re gonna be capable of pivoting? The most successful things we’ve done ended up doing something far different than what we initially thought that they were gonna do.

So can we pivot together? And then most importantly, do our cultures fit? And our approach is that if we evaluate a company, it’s usually, depending on the [00:11:00] size, the CEO or a very senior leader with myself and our senior director for innovation, and we just have a conversation about our approaches and whether our approaches are aligned.

And it’s more of a cultural temperature check, so to speak. If they are pretty compelling from a company perspective and a leadership perspective and a technology perspective, I then do three processes in parallel as much as I can. One is a subject matter expert evaluation. So the people who actually do the work will evaluate the company.

And what I ask them to do is to meet the company and not like them because I like them, but to like them if what they say comes true, does it make their jobs better and our patients’ lives better? Full stop. Do they think they would need this technology to do their job? Second evaluation is a technology deep dive.

Does their technology fit with our technology? And then third is an investable asset evaluation. Do they have financial promise? And we can de-risk a lot because we’ll know early, often before we invest, whether it’s gonna work or if we think it’s going to work. So if they pass those three things, then we’ll do something small together.

On purpose small, not because it’s gonna solve a problem, but because it’ll just show us that we can work together. And once we’ve demonstrated that we can work together and the problem’s worth solving, we make an investment, and then it’s a self-fulfilling prophecy.

Ritu: Thank you, Dr. Zane, for those very valuable insights.

I think the listeners, have a lot to learn there. Rohit, would you like to ask a question?

Rohit: Yeah, sure. I was, thinking about all the activity and the interest, Dr. Zane, that is in rural healthcare transformation. I’m sure you have rural locations as well in your state. So how do you think about that, and what are some of the suggestions or advice you have for health systems or entrepreneurs or even physicians who are thinking about this huge rural healthcare transformation that is happening at this time?

Richard: Right now, rural transformation is a political statement that has federal funding behind it, so it’s very difficult to develop point solutions that are going to be scalable and sustainable for something like that. Technology, I believe, is the big equalizer, which means that it allows us to care for patients in remote areas and deliver care closer to home.

We have 15 hospitals. We are in three regions hundreds of miles apart. We have three Level 1 trauma centers, and currently the hospital that’s right behind me has over 900 patients in it. I currently have three hospitals that have less than 10 patients in them. And everything in between. What technology allows us to do is absent procedures, it allows us to deliver complex, sometimes tertiary care, in a rural environment, and we have to think about how that works.

Do we have the right people in place? The right tools, the right procedures, and the right processes, and the right safety net. Safety net meaning if something isn’t working, we have the ability to move that patient to a place that’s of higher complexity and capability. So from our approach to caring for patients in their communities, and that’s really what you’re saying about rural health, it’s caring for someone in their community when their community could be dozens or hundreds of miles away from a more complex delivery system.

Technology is what we’re gonna rely on to do that.

Rohit: That’s great. Now talking about all the changes, Dr. Zane, that are happening so fast in the AI world, and the fact that there’s a lot of adoption of things like even, I’m just saying a name, open evidence, which may or may not be allowed by your AI governance policies or the health systems’ thought process around how AI should be used.

How do you deal with this adoption, which is at such a fast pace, which everyone’s using in their daily life, and now they’re bringing it into their workplace?

Richard: So let me be really clear. Let’s say for a second that I had the power to tell every doctor at UCHealth that they can’t use open evidence, and I don’t have that power.

But let’s say I did, and I told them they couldn’t, I would fail. Because they would use it one hundred percent. Because the reality is it’s a remarkable tool that’s just getting better the more it’s used. Yeah. What we have to think about is how we can bring that into workflows, because our guiding principles for intelligence or clinical decision support or whatever you describe it is- It has to be easier and not harder, fewer clicks and not more clicks, path of least resistance, bulletproof right, and it has to be within their workflows.

The bulletproof right is an interesting concept, and I’m not sure if you’ve spent any time on OpenEvidence, but I sure have. And I think it’s a remarkable, almost inspirational tool, and it allows doctors to do far more complex things than they otherwise would feel comfortable with. That statement might be scary to some people, but the same thing is true for Google 10 years ago.

The same thing is true for textbooks 100 years ago, which is immediately available information. And I have been able to deliver pretty complex diagnoses using OpenEvidence that I otherwise would not have been able to three or four years ago.

Rohit: That’s great to

Ritu: know. That’s amazing. So just switching tracks a little bit here, Dr.

Zane. Whenever we get our guests, we like to ask them their origin stories, and sometimes we hear really interesting anecdotes or personal stories about what brought you into medicine and what brought you into technology specifically. So we would love to hear your story as well.

Richard: My story, the reason why I’m a physician, is I met a beautiful woman who I wanted to marry, and she was going to medical school, so I went to medical school to impress my wife.

Ritu: That’s… We haven’t heard that before, so that’s a good one.

Richard: My wife is a surgeon, and we were elementary school teachers, and we met as elementary school teachers. I had- Wow… applied to law school, medical school, graduate school, and decided to go to medical school to impress her, full stop. I didn’t even know that you had to do residencies.

So I went to medical school in Philadelphia at Temple, my state school. Had a great experience, and at the 11th hour decided to do emergency medicine because it checked all the boxes for me. I didn’t know it was a specialty. I thought I was gonna do family medicine or OBGYN. And then I did my training at Johns Hopkins and did a fellowship there, ended up staying there and running their helicopter and transport program.

And I just liked running things. That’s the story. And then my wife finished her residency, did a fellowship- In pediatric orthopedics at Brown in Rhode Island, and I thought I was gonna stay at Johns Hopkins, and turns out I didn’t wanna leave my kid for a year, so I got a one-year job at Harvard Medical School in Brigham and Women’s that lasted 14, and it was defining for me.

And then I came out to Colorado and started a new Department of Emergency Medicine that didn’t exist before. And the reason why I got into technology was absolutely pragmatic, which was I did not have the tools to care for the numbers of patients that I aspired to care for, and I couldn’t afford them.

So I found companies that had developed unique technologies, and I convinced them that I could make them better if they let me have their technology for free. And that’s where we came up with the concept of the Care Innovation Center, was partnering with industry, co-developing, having revenue share or some type of participation in value creation.

And then that became within an emergency department, then it became within 27 emergency departments and 25 urgent cares and a virtual health center, and then a healthcare system and a university. So it just grew that model seemed appealing to both health systems and technology companies and investors.

Ritu: Thank you for sharing that. That’s an amazing story, and I’m sure, the listeners would love to hear that. It’s really interesting to hear you talking very comfortably about technology, Dr. Zinn, because a lot of time we get physicians and they say that, this is one of the barriers, that in healthcare people are, slower to adopt these technologies because of the safety factor and the fact that you can’t afford to fail.

But hearing you talk about failure and innovation was very interesting. So would you like to tell us a little bit more about that, that how do you get that buy-in culturally from the other physicians in your organization, and how are you able to make people comfortable with these ideas?

Richard: So first, we have guiding principles – which are that our patients come first, right? Full stop, without exception. We exist to care for patients Just like a stethoscope was somehow controversial when it first came out, AI may be controversial. To me, it’s not, and to our health system, it is not. So why do we exist? To care for patients. So we create solutions that benefit people.

That’s a guiding principle. We fear inaction, not failure, and abhor bureaucracy. Those are the guiding principles for how we think about things. The other is that we don’t do pilots. I hate the word pilot. We won’t do pilots. We’ll do phased deployments. So we have a healthcare system that is based on one single mature instance of electronic health record, Epic, and we use our health system as a learning laboratory.

So first, the problem statements that we are building solutions for have been identified by someone who’s responsible for the work, so they are our early adopter. So we identify early adopters, we ensconce them in resources so that they are using their judgment, they’re deploying the technology, and we are making it easy for them.

And then we show that it works there, and then we show it works somewhere else. We rapidly iterate, which means if I say to Johnny or Timmy or Sally, “I’m gonna deploy this technology in your environment, I want you to use it,” it’ll never work. I’ll say to Timmy, Johnny or Sally, “Here’s the problem we’re trying to solve.

Here’s how we think an approach makes sense. Do you agree?” If they do agree, then we deploy it, and we rapidly iterate. So we check in, maybe not every minute or every hour, but certainly more than every day. And when something doesn’t work, we either fix it or pivot. So we don’t deploy technology that doesn’t work and people are stuck with it.

We sometimes deploy technology that is initially clunky, but then have the resources and have the commitment to fix it and make it better. And then success begets success. So now that we’ve done a number of deployments that sometimes have started off clunky, but then have gotten smoother, people have confidence in us.

Ritu: Great. I think we’re almost at the end of the podcast. Time, has flown by as usual. So Rohit, would you like to ask a last question? Or then we can ask Dr. Zane for some closing thoughts.

Rohit: Yeah. Dr. Zane, any closing thoughts or anything you are seeing from the Periscope peaking around the corner.

Richard: So I think it’s very important for people to understand that healthcare is broken. I think everybody who’s experienced it knows that. There isn’t a single solution. There isn’t even three or four solutions. There’s 1,000.1 solutions, and the only thing that will move us across the finish line, if there is a finish line, is adopting and understanding how to rationally deploy technology to make all of our lives better, just like we’ve done over the last two hundred years.

Ritu: Great answer. Thank you for being our guest on the podcast today, Dr. Zane. It was a pleasure having you. Thank you.

This episode is brought to you by the HealthImpact Fall Forum, which happened in New York City on October 6 -7, 2026

—

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 along with Rohit. We are really excited and thrilled to have Hal Wolf on our podcast. Hal is the President and CEO of HIMSS, the global organization dedicated to transforming healthcare through information and technology. He has previously held senior leadership roles at Chartis and Kaiser Permanente, and has over thirty-five years of experience as a respected international healthcare and informatics executive. We are really looking forward to this conversation. I’ll ask Rohit to give a brief introduction, and then we’ll get started. Welcome, Hal, to our podcast. Thank you for being here.

Hal: Thank you for having me. This is a real honor.

Rohit: Thank you, Ritu. Hi, Hal. I’m Rohit Mahajan, CEO of Damo Consulting and co-host of this podcast. Looking forward to an interactive discussion, and hoping to meet you in person at one of the HIMSS conferences.

Hal: That would be wonderful.

Ritu: Let’s dive right in. The first question is about the growing digital divide between healthcare organizations. We’ve been talking to a lot of C-suite leaders who describe their AI spend and the technologies they’re accumulating, while at the same time rural organizations are financially challenged. What needs to change in the economics of healthcare technology to ensure AI becomes an equalizer rather than another source of inequality?

Hal: This is a really good question, and one that comes up every time a new technology emerges. I’ve been around long enough to remember when EHRs first came forward, and the first question was: only the large systems can really afford this — how will we ever get it out to rural organizations or urban organizations serving people without insurance and trying to deliver the best care possible? We often think about access as rural versus urban, but many people live in the shadow of some of the finest institutions in the world and still don’t have access to them.  What we’ve seen over time is that when new technologies roll out, early adopters are the organizations that can afford to take a chance and explore innovation. Once that happens, we gather information, learn what’s working and what isn’t, standardization takes place, and competition enters the market as others replicate what works. That tends to lower cost by creating abundance — and that’s when mid-level and lower-resourced organizations start to adopt. We’ve seen this with our own devices: a few started with AI years ago, and now you can’t find a device without it built in.  So I do worry about the digital divide, and in the short term it can feel like it’s growing. But it really depends where you are on the seesaw. We’re seeing more and more AI capabilities built into basic consumer products and services that can aid in the healthcare space, and clinical decision support and operational applications are being reproduced and showing their worth, then moving downstream. This is especially important because the further behind you are in monetary capability, the harder it is — and the more we need digital capabilities to compensate. This is something we spend a lot of time on at HIMSS, working with the WHO and the Gates Foundation, asking where we can lend expertise and help strategically. I think it’s a short-term glitch. New technology, early adopters, standardization, mainstream adoption — it always works its way through.

Ritu: That leads into the next question. Do you think it’s time to rethink digital maturity in this age of AI? HIMSS has helped establish global benchmarks through models like EMRAM, and historically the industry has associated maturity with digitization and EHR adoption. Now with AI and AI agents making independent decisions and taking autonomous actions, will the definition of digital maturity change? And what would distinguish a truly AI-native health system from one that is simply bolting on more and more applications?

Hal: The short answer is yes — but let me unpack that. We have been developing and dramatically changing our maturity models over the last three years. The old model was essentially a checklist: do you have this technology? Check. That doesn’t accomplish much. People, process, technology — you have to have the training of the people, the governance, the processes in place to take advantage of technology, and you need to be measuring the impact on access and quality. If you’re not impacting those two things and can’t measure it, the effort is interesting but of limited relevance.  I just came from a presentation by our analytics leader, Dr. Anne Snowdon, who was walking through 121 data points spanning multiple maturity models but leaning heavily into our analytics maturity model — because that is the foundation of how we’re measuring AI, how it’s being utilized, what access to data and information looks like, and what impact it’s having on operations or care delivery. So yes, we’ve already been shifting our models in that direction.  I’d also differentiate between the medical model and the health model. The medical model — hospitals, clinics — is already using AI for predictive modeling around bed capacity, resource utilization, and supply chain, with AI giving strong indications of future consumption and revenue. Behind that is clinical decision support, which is critically important given the workforce shortage. The WHO expects an eleven million clinician gap by 2030, even as we’re diagnosing more and more people with chronic disease — partly because we’re living longer, and partly because we’re so much better at early diagnosis.  On the health model side — individuals using wearables to monitor steps, blood pressure, sleep, food intake — AI apps are scrutinizing that data and detecting trends that indicate a potential problem before it’s been formally defined. I’m actually wearing a prototype ring that measures my blood pressure every hour. That is so much more relevant to my doctor than a snapshot in the clinic once a year. What this means is that the nurse practitioner who used to take my blood pressure is now freed to practice at a higher level of their license.  We’ve done this before — about fifteen years ago we shifted scope of practice from doctors to nurse practitioners, which required legislative approval and navigation through associations, but we did it because it created more capacity and higher-level clinical response. Doctors didn’t lose their jobs — they moved to higher-acuity patients where their expertise is most needed. Nurses felt more appreciated because they were dealing with higher-acuity work. The same thing is going to happen with AI. We need to figure out what it does well, where it can substitute for human interaction, when human in the loop is critical, and where we need the highest level of scrutiny in measuring outcomes. That is the exciting challenge right in front of us.

Rohit: Hal, I’m curious about innovation and how health systems are approaching change given that business models themselves are shifting because of AI. There are still silos in healthcare that need to be broken down, and interoperability needs to come in. What are your thoughts about collaboration, innovation, and the aspects that might accelerate the pace of change?

Hal: The way change is taking place — and how people are responding — differs significantly depending on where you sit in the world. In the United States, we are pilot-happy. AI is either being built into existing EHR platforms, or you have a dozen doctors running a dozen AI-based applications doing pilots left and right. A large HIMSS study done this past spring and published in June found the US is broad but not deep: lots of pilots, minimal depth of integration. In Asia-Pacific it’s the opposite — fewer pilots but much deeper integration into operations and care delivery. That’s really intriguing to us. We just launched our AI Operations Framework Program, with eight organizations signed up across three countries, adding a fourth now. In phase one we’re defining the frameworks, how to measure them, and how to measure success — and we chose the Asia-Pac market because they’re doing fewer pilots and more deep-seated integration models alongside analytics. That is where the rest of the world will get to.  Europe has the AI Act, so they’ve put guardrails up and are working through the challenges, proof points, and transparency requirements. But we’re beginning to see through the fog of war: AI is a tool, here’s what it does, here are its dependencies. We need to be clear about who the author is, because every AI application comes with its own point of view — it depends on who built it, what problem it was designed to solve, and who delivered it to you. How will you integrate it, model it, and govern it? And you have to take into account the regulatory bodies playing in this space.  In the United States the range runs from “all of humanity is going to die” to “what, me worry?” — the truth likely lies somewhere in between, as it usually does. Our biggest concern around agents being developed is: what are they being asked to do, how much latitude are they being given, and what problem are they trying to solve? Agents can go a little rogue if given too broad a target. Some advanced AI systems, when given constraints, seem to reason that they’d be better off without them. That’s a level of self-determination everyone is trying to get a grip on quickly. Fortunately, the vast majority of healthcare AI applications are restricted within specific data sets to accomplish specific actions in a modular fashion. Turning agents loose across the entire healthcare ecosystem is not yet a target, and I’m glad — we have a chance to watch that carefully and make sure we approach it thoughtfully.

Ritu: Going back to the point about wearables and the continuous stream of data — we’ve had physicians and CMIOs on the show who really question the concept of an annual physical as just a snapshot in time. Bringing that back to HIMSS: you do an annual conference, but the pace of innovation is now measured in weeks and months. Does HIMSS itself need to evolve and approach this age of AI differently?

Hal: We’d like to think we’re adjusting, focusing on the areas of AI most important to our members and from an analytics standpoint — which is really the tip of the spear for HIMSS in both thought leadership and our views on how AI is impacting healthcare and what its best uses are. We get requests constantly from governments around the world for guidance on AI: where are the best opportunities and how should they move forward.  Internally, yes — we’re figuring out how to use AI agents to look at our own analytics, find patterns we may be missing, and ensure our maturity models are current. We re-release them every two to three years. And in basic operations, we’re asking that question all the time. But bringing it back to the healthcare ecosystem: we need to use AI because we don’t have enough people to accomplish what we need to. We need to integrate how our patients and citizens are using AI so we don’t miss anomalies.  The annual health check will evolve too. At-home blood draw tools are already coming. But some things won’t show up in a blood test. One look by a physician — at your hands, your nails, the telltale signs of an illness you might not even be aware of yet — is something that still matters enormously. There will always be a need for encounter-based care with a clinician. But now those observations can be backed up by AI and the full body of research. In the 1970s there were about 450 published medical articles a year in a handful of journals — Lancet, New England Journal of Medicine, a few others. A very diligent physician could read one a day and keep up, because an editor made the curation decision for them. Last year there were tens of thousands of peer-reviewed articles. There is no way for any individual to keep up. Using AI to aggregate and surface what’s relevant at a patient level is critical — and we can’t go backwards on that.

Ritu: Thank you, Hal. You’ve shared some very interesting perspectives. Before we wrap up, we would love for you to share your origin story — how you came into healthcare, how you came into information technology, and what brought you here.

Hal: Innovation and technology was not where I started, though I had some background in computer science. My real background was in the entertainment industry. I was involved in a very important project in the early to mid-1990s in Florida for Time Warner Cable — the development of video on demand at scale. It was the first platform of its kind in the world, called the Full Service Network, a technology experiment to stream video into people’s homes and give them control via a remote, from servers sitting in a central environment, delivered through a brand-new AT&T switch using MPEG. Every time you use video on demand today, it is a relative of that foundational work.  In that role I was labeled Vice President of Programming — which on the cable entertainment side meant negotiating contracts with motion picture studios and figuring out the TV lineup. I got an urgent call and flew out to Mountain View, where our supplier Silicon Graphics said: “We really need you — we’re having a problem with our MIPS processor not keeping up as we download from the satellite, and we need to figure out how to parse the data components.” I said, “Wait — what are you talking about?” They said, “Well, you’re Vice President of Programming.” We had a classic George Bernard Shaw moment — two people separated by a common language.  I called my boss, Tom Fini. He came back into the room twenty minutes later: “Okay, I’m now Vice President of Content. How can I help you?” What I really learned was that either 150 of the best programmers and architects in the world needed to learn the television industry, or the television industry person needed to learn software. I took my Fortran and COBOL training, moved to Mountain View, and spent six weeks learning from some of the best architects in the world. Ever since the mid-1990s, part of my role has been translating business requirements into technology, and technology into business requirements, and finding the middle ground so you can actually deliver.  Through a couple of stops in between, I was brought over to Kaiser Permanente. The CIO of the organization I was at — Qwest Communications — became the CIO at Kaiser, and the call came: “We know your temperament, your love for innovation, and how you think. We need you in healthcare, on the nonprofit side.” I made that move in 2003 — twenty-three years ago — and it was one of the greatest things I ever did. I love what I do every day because I’m a bit of a change junkie, and this is about technology enabling the betterment of individuals.

Ritu: Thank you for sharing that story, Hal. And when you said Mountain View — I worked at Silicon Graphics years ago. That is such an amazing coincidence.

Hal: Are you kidding? You worked in one of the most advanced technological organizations in the world. How wonderful to make that connection. Wow.

Ritu: Thank you so much, Hal, for being on our podcast today. Thank you.

Hal: You bet.

—

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 welcoming Jean-Claude Saghbini to our podcast. He’s the Chief Technology Officer at Lumeris, where he integrates technological innovations, clinical processes, and operational excellence into leading value-based care solutions. Prior to Lumeris, he has held senior technology leadership roles at Wolters Kluwer Health and Cardinal Health. At Lumeris, JC has been closely involved in developing Tom, the company’s AI-powered primary care as a service platform, and we’re really excited to discuss that today. Welcome to the podcast, JC. Looking forward to an engaging conversation.

Jean-Claude: Absolutely. Thank you for having me. I’m excited.

Ritu: You’ve made the argument that the primary care problem is fundamentally a supply-demand problem, and that adding more digital health applications is not really changing this equation. If AI is going to be different, what exactly needs to change in the operating model? At what point do AI tools stop making physicians ten percent more efficient and actually allow health systems to care meaningfully for more people with the same clinical workforce they have today?

Jean-Claude: Let me unpack that supply-demand problem a bit. Today, about a hundred million people in this country either don’t have access to primary care or have access to extremely inadequate primary care. The reason primary care matters is that it controls about three trillion dollars of spend because it operates so far upstream — and the fact that those people lack access has both healthcare implications for them and significant financial implications at a national level.  The primary reason for the gap is actually simple: there are too few primary care physicians in the country to do the work, and that delta continues to grow. In about eight or nine years, we’ll be approaching a shortage of 90,000 primary care physicians. When you look at it that way, tweaking things and applying AI for small efficiencies is not going to work. The problem isn’t at the scale of five or ten percent — it’s at the scale of a hundred million people. So what we want from AI is technology that can bridge that access gap. With the same infrastructure of physicians and clinicians in primary care that we have today, we need to expand access to the hundred million who aren’t getting it — and to the others who are getting primary care, but only whatever time is available to them, not complete primary care.

Ritu: That’s a great perspective, and we’ve been hearing about healthcare deserts — especially with the rural healthcare transformation project. The problem is even more acute in certain areas. We’ve spoken to an ophthalmologist in Alaska where the wait time can be as much as six months. Tell us more about Tom, which is positioned not as a co-pilot but as an AI-powered member of the care team. Tell us about Tom and how it brings in agentic AI to address this problem.

Jean-Claude: We refer to Tom as two things. Tom is an AI primary care team member, and Tom is also an AI primary care operating system — a platform. I’d love to dig into both, but let’s start with value creation. On the value creation side, Tom is an agentic AI primary care team member that effectively becomes part of the care team of a primary care physician. Just as a physician has staff members to help take care of patients, they add one more: Tom. Tom has access to data about the patient, can compute and reason on that data, identify what to do — from closing care gaps to proactive management. This is what we call the BNA, or Best Next Action engine. It determines the best next action for that patient. But more importantly, Tom can act. Tom can act in a conversational way — we use conversational AI through text messages integrated into the workflow, so Tom can proactively engage with patients and effectively manage them in between visits.  Between visits there is a desert of information. You leave your physician’s office, and the only thing they know about you next is when you show up again. In that time, nobody knows anything about you. Tom not only brings information about you — it actually helps you manage your medical condition between those visits. That is how we’re able, within a fixed human infrastructure, to use agentic capabilities to manage more interactions with patients and a broader set of patients.

Ritu: Tell us a little more about the OS part that you mentioned.

Jean-Claude: To be able to deliver that, you have to build all of the mechanics underneath. The expression of that delivery might be a conversation Tom has with a patient around a cardiometabolic condition they have. But underneath the cover, we had a bit of an unfair advantage: fifteen years of learnings that Lumeris has accumulated on how to manage patients and populations to better outcomes. We’ve partnered with health systems across the country to do that. We took all of that knowledge and embedded it in the operating system.  That gives you components like data ingestion, normalization, and creating a 360-degree view of a patient — whether the data is coming from the EHR, claims, or social determinants of health. We had to build reasoning components that can reason on patients, understand their gaps, and determine what to do about them. We built bidirectional EHR integration — not only to get data from the EHR, but to push data back in. We built patient engagement capabilities: telephony, voice conversational AI, text conversational AI, and a series of agentic services in preventive care, scheduling, chronic condition management, and pre- and post-visit management. We built analytics to surround all of that so you can analyze what’s happening across your population. And last but extremely important — something I’d love to talk more about — we built a safety platform that monitors all of these agents: what they’re doing, tracking clinical metrics, patient sentiment metrics, and objective accomplishment metrics, issuing alerts if things have gone sideways, and ensuring everything stays within the guardrails. The totality of all of that, to us, is Tom.

Ritu: This is really interesting because most of the CIOs we talk to are focused on summarization, documentation, or administrative AI. But you’re saying Tom has agentic capabilities that allow it to make decisions autonomously — some requiring clinician approval, some perhaps not. This raises a lot of questions about trust, governance, and orchestration. How have you approached the trust and governance lens? And when does the human in the loop come into the picture?

Jean-Claude: We use two words internally: guidelines and guardrails, and we live by those. Whenever we build any capability, we define guidelines — what it should do — and then we put the guardrails around what it should not do. We have very strict guardrails: Tom is not a physician, is not a licensed practitioner, and therefore Tom cannot diagnose, treat, or change medications. Those are the guardrails we do not go outside of. Within those guardrails, the guidelines define what Tom can do: engage with patients, check in on them, explain things about conditions, help them manage their condition, check whether they picked up their medication, identify barriers to filling a prescription, connect them back to the care team if gaps are identified, and check on symptoms if a patient expresses them. Those are the guideline capabilities. The guardrails are what we don’t let Tom get out of.

Ritu: Thank you — guidelines and guardrails is a good approach. That leads to the next question: most AI implementations we see have very obvious administrative metrics for ROI. But in this case, where Tom is actually becoming part of the care team, what metrics tell you Tom is improving care and not just creating more interactions?

Jean-Claude: The ultimate metric we look at is access. The definition is straightforward but hard to get to: can the same number of physicians in a health system manage a broader number of patients attributed to primary care? The ROI to the health system is clear — when you have more patients attributed to you, that attribution is monetizable through services the health system provides to that population. The human ROI to patients is that they are now attributed to primary care physicians, whereas otherwise they might wait six or nine months to find one.  On the path to those ROIs, there are first-order KPIs along the way: engagement rate between patients and Tom, care gaps that Tom identified and closed, issues patients raised in conversations that Tom connected to the right venue of care, and symptoms that patients expressed that Tom helped direct to the right place. Those are the first-order KPIs. The second-order KPIs are the ROI outcomes I mentioned.

Ritu: That’s a key point — as long as patients are actually getting access to primary care physicians, you can see the system is working. Let’s change tracks a bit. Tell us about your background and how you got into healthcare. We often get very interesting and personal stories from our guests on that question.

Jean-Claude: I’m an engineer, so I’ve spent my career solving problems. Early on I was in horizontal engineering — technologies across the board. At some point I wanted to get into a vertical I felt passionate about. I was looking at renewable energy as one option, and healthcare as the other. I joke that I got into healthcare because I thought it would be easy. I’ve been in healthcare since 2005 — twenty-one years now.  I got in on the startup side. I’m in Boston, so we started a company in Concord, Massachusetts. In 2005, we had the ambition of creating a SaaS platform to connect medical device manufacturers and health systems and help manage medical devices via RFID from manufacturing all the way through to implantation in patients. That was my entry into healthcare. The technology was very exciting at the time — RFID was the thing back then, the way AI is now. Now it’s ubiquitous. Back then it was early uses of RFID at national scale.  We sold that company — WaveMark — into Cardinal Health. I spent four years at Cardinal Health managing and growing that RFID business from startup scale to enterprise scale. Then I joined Wolters Kluwer Health, where I worked on frontline clinical decision support products like UpToDate, built up the AI Center of Excellence, and did a lot of AI innovation. And this is my third or fourth chapter in healthcare — joining Lumeris about five and a half years ago.

Ritu: Thank you for sharing that story. We have time for one more question. With Tom, you’re bringing together EHR, payer, claims, clinical, and other data to determine the Best Next Action. Do you think the real competitive advantage in healthcare AI will ultimately come from the model — since all we hear about is which model is better or faster — or from the ability to build this longitudinal data and workflow layer?

Jean-Claude: It’s not the models. When any one of the hyperscalers or OpenAI or Anthropic releases a model, eight billion people on the planet have that model the next day. So having that model cannot be a competitive advantage. What I put on my slide — and this comes straight from a conversation with healthcare executives last week — is that the competitive advantage is the data you have. It’s the knowledge you have about care delivery. Many have tried to fix healthcare from a technology lens alone over the past twenty or thirty years, and many have failed doing it purely through tech.  So it’s the data and the understanding of patients. It’s understanding the care delivery models and the domain knowledge — whether that’s primary care, surgery, or cardiovascular. And the third element is building actual applications that leverage these highly democratized LLM models, powering them with your data and your understanding of what you’re trying to do, and then integrating them into workflows — and more importantly, creating new workflows. Integrating into existing workflows is a form of automation, and that’s fine. But now we should be able to create entirely new, reimagined workflows. The totality of that — data, domain knowledge, and applications that create new workflows — is what the competitive advantage actually is.

Ritu: That’s a great answer, and it ties back to what you mentioned earlier — Lumeris had all this proprietary data and deep understanding of the problem that you integrated into Tom. Time has flown by and we’re almost at the end. Would you like to share any closing thoughts or advice for listeners in the healthcare technology field?

Jean-Claude: Three pieces of advice that are top of mind for me. First, don’t fall into the automation trap. There’s an urge to automate things because you can see where AI fits and it’s satisfying to do — but you can get stuck there. The bigger opportunity is reimagining new workflows and new ways of delivering value, not just automating existing ones. I see a lot of pure automation of existing workflows right now. You have to do some of that, but at some point you need to invent the new.  Second, don’t fall into the pilot trap. Pilots were essential in 2023 and 2024 — you cannot learn this stuff from a textbook, you have to do it. But we are in 2026. If you were still just running pilots, that was the top of the funnel. Now we can narrow the funnel, make big bets, and make those enterprise bets.  Third, don’t fall into the perfection trap. LLMs and AI are not perfect, but the problems we’re going after don’t have perfect solutions today either. We can fix enormous problems without perfect AI. The search for perfection will keep technology that patients and clinicians genuinely need locked in a box while we wait for it to be perfect enough to release.

Ritu: That’s great advice. Thank you so much for joining our podcast today. It’s been a wonderful discussion, JC.

Jean-Claude: Absolutely. Thank you so much.

—

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.