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.

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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 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.

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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.