Charles: I am Chuck Christian. I’m the Vice President of Technology and CTO for Franciscan Health. Franciscan is a 12 or 13 hospital system, depending on how you count them. We cover a swath of the Midwest from just south of Indianapolis all the way to Chicago, basically following the I-65 corridor.

We have between 350 and 400 locations, including physician practices, imaging centers, lab draws, urgent cares, and oncology centers. It’s a pretty large organization. We have about 29,000 team members, both employees and contractors, at Franciscan Health.

We are truly mission focused. We are a Catholic healthcare system with a big C. We are owned by the Sisters of St. Francis of Perpetual Adoration. That means there are two sisters in the chapel praying for whatever they deem important and anything we ask them to pray for, 24 hours a day, seven days a week, 365 days a year. That’s where the “perpetual adoration” comes in.

We are a mission-driven organization. I believe in that. A lot of our hospitals are smaller and in underserved places, and we take care of that patient population. I think we’re really good at it.

I’ve known this organization almost 40 years. The CIO previous to Charles, who is our current COO, was a good friend of mine. I was CIO of a hospital in Southern Indiana for 24 years, and Bill and I ran a similar software stack. I watched Bill and learned a lot from him as far as how he ran this large organization.

I’ve been here for six years. I joined in April of 2019, so in dog years that’s like 35 years or more. We are very busy. I’m very blessed to have an outstanding team that manages all this, and I get to stand in awe and watch everything we accomplish every day.

Rohit: That’s fabulous. Thank you, Chuck for that intro.

Ritu: My name is Ritu Roy, and I’m the Managing Partner here at Damo and BigRio, and also the co-host of The Big Unlock podcast with Rohit. Thank you for being our guest today, Chuck. We are looking forward to an engaging and insightful conversation. With that, we can dive right in and get started.

Charles: Thank you.

Rohit: Hi Chuck. I’m Rohit Mahajan. I’m the Managing Partner and CEO at BigRio and Damo Consulting. It’s great to have you on the podcast. Like Ritu said, we’re looking forward to an engaging discussion. I’d like to start with the first thought on my mind. You’re in a mission-driven organization, and you’ve been a healthcare leader for many years. What started you on this journey? Tell us how you got started in healthcare, what attracted you, and what you’re passionate about.

Charles: Well, it depends on how far back you want to go. I’m an X-ray tech, radiologic technologist if you want to use the term. The first 14 years of my career were in radiology.

I stepped out of high school on June sixth in 1971, and on June seventh I stepped into the hospital, and I haven’t left since. Interesting enough, I did a lot of things in the radiology department and became part of the management team of that department. I guess if the chief tech had not been just a few years older than me, I’d still be there, because that was the role I wanted. But Roy just retired a few years ago, and I wasn’t going to wait that long.

I’m a geek, I’m a nerd. I was a nerd in high school. It wasn’t cool to be a nerd in high school back then, but it’s cool to be a nerd now. I did a lot of programming classes on the old System Threes with punch cards. Then I learned how to code for Z80 processors.

When we started automating hospitals back in the mid-eighties, I got chosen to run the ambulatory implementation of order management after we had put in patient management. I realized I liked it, and I knew that was where healthcare was going. Radiology has been a high-tech department in hospitals for a long time. I was trying to automate the patient record in radiology, but it was so expensive I couldn’t get any funding for it.

So I jumped ship and moved over to the vendors for about five years. Eventually I was asked to move to either an implementation manager role or the director of an outsourced IT department in southern Indiana. I did that. I had four daughters at the time, and it was the right thing to do because it was a great place to raise my girls.

I spent 24 years there. It was during the time the role of a healthcare CIO was defined. When I left that job, I was Vice President and CIO. I moved to Georgia to a health system there as Vice President and Chief Information Officer. Then I came back to Indiana and worked at the Indiana Health Information Exchange, which is now the only exchange in Indiana. I had been involved with it since 2005. I worked there for a little over four years, and then I took this role here. That’s my stint in healthcare, which has spanned over 50 years.

Rohit: That’s awesome, Chuck. You’ve been there, done it, and seen it as well. I was curious because a few days ago, when we were chatting, you were talking about being either back from UGM or about to go there.

We all know it’s a week-long affair, people go deep, and there are so many things to cover. We were wondering if you could share some of your experiences or a heads-up on topics you see coming our way.

Charles: Sure. I came home with a great deal of anxiety because of trying to figure out how we’re going to do everything and where healthcare is going. The nice thing about Epic is they now cover the entire gambit. I remember when Epic started; they were only in the ambulatory space and then only in large academic medical centers. They cover quite a scope of product these days.

Now that they have grown the applications, they have de-identified shared data, which I think is going to be a plus. The two-letter acronym was everywhere, AI, and how it’s going to be leveraged and used. They did a nice job showing scenarios of how it could be used and how organizations are using it.

We’re a risk-averse organization. We’re taking a more moderated approach. We’re getting our governance in place first. We already have a few things going through the AI mill, and we will have more. We split it into two pieces, one on the clinical side and one on the operational side. Epic has both, and I think they’re well positioned to do that work. They partner with Microsoft, and they continue to do so.

They announced they are working on their own ambient listening. They have business partners already, but they are creating their own product. I assume it will be predicated on the Microsoft stack, but they didn’t say, so I don’t know.

They also mentioned they are working on their own ERP and starting with workforce management. That makes sense because the workforce is in Epic all the time. Nursing staffing, scheduling, shifts, and how all that ties together. It’s an interesting leap.

Years ago, when Lawson, before being purchased by Infor, said they would create a patient accounting platform, I was in a CHIME focus group. When they mentioned that, a bunch of CIOs in the room asked why they would do that. You need to get your ERP right first. But I think the way Epic is approaching it makes sense.

It was great. I was there for about four days and spent most of the time listening to presentations. Judy did a great job with a big screen about what’s next and what’s coming. The rest of her team did a great job showing what you can do now and what’s coming. They do a good job setting expectations around timelines. They release quarterly. We do two a year, so we’re current from their perspective but behind. We don’t have the wherewithal to immediately adopt everything when they release it, so we have to plan accordingly.

Ritu: Yeah. So Chuck, when I was reading about the UGM, it was interesting because they said their unique proposition with AI is the de-identified patient records they have in Epic Cosmos, which is more than 15 billion patient records. They said that for the first time, it can actually move toward healthcare rather than sick care because doctors can predict trends. And I think they released two new things called Emmy and Penny, which will help doctors see the trajectory of what is going to happen with patients.

So I was curious about your thoughts because you’ve been in this industry for such a long time. Do you think that this USP—this huge bank of patient records—is really going to set them on a differentiating path compared to all the other AI startups trying to do the same thing?

Charles: I think that having the data is huge, honestly. It reminded me a lot of—if you remember years ago—they had a thing called PatientsLikeMe, where people with unique and rare diseases could find others and compare notes and treatment approaches. Working at the Indiana Health Information Exchange, I know they have about 30 years of data. Not all of it is discrete, but the majority is.

One question I asked the CEO, who is a friend of mine—and a lot of researchers use that de-identified data—is that when you create an AI model and just let it learn, there are all kinds of interesting determinations you can make once you have the data. So I think it’s going to be a game changer. Epic is also trying to outdo themselves. Given the market of EHR vendors, there aren’t many left standing. There are three or four. Others are creating similar repositories, but I’m not sure they have the long-term vision or the wherewithal to get it done. Knowing the talent Judy has pulled together, I think it will be very interesting to see what comes down the pike.

Ritu: Thank you.

Rohit: Chuck, you mentioned you’re taking a conservative approach to AI adoption and setting governance before taking major steps. How do you think about innovation or typical problem-solving—for example, reducing cognitive load across the organization? How do you balance this conservative approach with the fast-paced changes happening in the marketplace?

Charles: I think we have to be very clear about what problem we’re trying to solve. There are so many solutions being thrown at us—“Hey, we can do this, we can do that”—but often it’s not a problem we actually have. So we’re trying to pick and choose which targets to shoot at.

I’m married to a critical care nurse, so I’m very careful about getting in the way of the nursing staff. She’s retired, but for me, technology needs to be invisible. If it gets in the way of people being able to do their job, then it’s a problem.

If you think about it for a minute—and I’ll give you Chuck’s opinion—we don’t really have electronic medical record systems for documenting the care of the patient. What we have are electronic systems that capture information required for billing. That’s part of the problem. We have all these required elements clinicians have to document—physicians have to dot all the i’s and cross all the t’s—to get the appropriate words in so it can be translated into billing codes, ICD-10 codes, HPS codes, and so on. It truly gets in the way of taking care of patients.

But once we get that discrete data, we can use AI and other tools to help determine a better course of treatment. You’re never going to hear me say that we should depend solely upon AI. It has to be moderated and reviewed by someone with clinical training. Physicians have shown me I’ve been wrong more times than you can imagine. Working together and having good data aggregation is important.

One thing I learned early on when implementing the first physician order entry and clinical documentation systems was that physicians said: “Don’t tell me what I already know. Tell me what I don’t know. Better yet, tell me what I need to know about the patient in front of me right now.” There are things they don’t know. That’s where data aggregation from health information exchanges helps, because patients don’t get care in one location or from one physician.

I’m living proof of that. I get care in two—actually three—health systems because that’s where my specialists are. My primary care doctor wants to know what my orthopedist did or what my cardiologist’s course of treatment is, because he’s managing my diabetes and a few other things. Having access to information—recent labs, imaging studies—is extremely important.

We talked about interoperability, and that’s where it comes into play. Most hospitals in Indianapolis are on Epic, so you can get data easily. From non-Epic systems, there are mechanisms too. When I see my cardiologist—who uses a different system—and he already knows what my medications are because they were recently changed by another physician, that’s positive. I don’t have to list everything. When they know my latest labs, that’s positive too, because we’re not hunting for information.

It’s about providing information that is important to the treatment at that moment.

I had the privilege of sitting in a presentation—maybe eight or nine years ago—at Scripps Institute. They showed a demo of what a patient encounter could be. It was very Star Trek–like. The computer or AI interacted with the physician and patient appropriately. It listened in the background and captured information about the encounter. When the physician said, “We need to order a CT scan of your lower abdomen,” it was already getting that scheduled. When the patient was ready to leave, everything was set. It was also checking for recent labs and reminding the physician if the patient—say a diabetic—was due for an eye exam or foot check.

I think it’s about having access to the information so we can inform—not determine but inform—the physicians. Because at the end of the day, physicians are accountable for the outcomes. They have to be in control, not the AI.

Ritu: Yeah,

Charles: we’re not ready for Skynet yet.

Ritu: I think you described a multi-agent system where the agents are off doing things and then bringing it all back for the physician to review. With that being said, we all know that AgTech is one of the top trends everyone’s talking about these days. What are your thoughts on voice agents? Where is Franciscan with that? Have you had exposure to or tried voice agents in the hospital?

Charles: Yeah, we’ve got a trial. We’ve got over 200 physicians working on those. Is it going to be the end-all, be-all? I don’t know. The physicians seem to like it. It assists them; it helps with their pajama time.

I’ve listened to conversations from other health systems that were early adopters, and I have to go back in time to when we were looking at automating physician practices in Southern Indiana. We visited a group of 14 family practice doctors. The husband and wife who started the practice mostly did OB and family medicine. Their use of computers was minimal—they were still mostly on paper. But they had other physicians who, I think, slept with their laptops.

The interesting thing was that depending on how well a physician adopted the computer system and molded it to how they practiced, they got to take advantage of it. I think it’s going to be the same with AI and voice agents. If they allow it to help and figure out how to incorporate it into how they think and practice, they’ll see the benefit. The systems are pliable enough now that it’s easier to do.

When I was in Georgia, we needed to automate a lot of OB practices on the same platform. One OB had been practicing almost 30 years and already had a solution he had customized. He told me I would tear it out of his cold, dead fingers. So we worked with him. The new system was more flexible and pliable than his old one, and he became a champion because he was willing to take the time to understand how he could use the technology to help him practice.

I think that’s the key. If you’re resistant to it, that’s fine—that’s perfectly okay. But people who write software often think all physicians think the same way. They’re absolutely wrong. It depends on where they trained. I learned that when implementing emergency room electronic medical records. The physicians who helped design the software were trained with a very different approach to critical thinking than our physicians. We had to relearn and figure out ways to adjust, because once clinicians are trained a certain way, it’s hard to change those habits and the way they gather and maintain information.

Ritu: Thank you. Great answer.

Rohit: Chuck, I’d like to ask your thoughts about the innovation process. How do you approach it, and what are some of the things you do to foster innovation?

Charles: One of the things we did was stand up a Tech Innovation Lab. Honestly, it was a selfish move because people were just bringing technology into the organization. All the enterprise architects report to me, and we work together to understand what will work in our environment and what won’t. We try to standardize as much as we can.

So I created the Innovation Lab to bring these innovations into a controlled environment and try them there. It’s a walled garden. It’s not connected to the rest of the network. It has its own connections to the internet. So if we blow something up, it only blows up in the lab. That’s why we did it.

What we’re able to do is bring ideas in and fail fast—figure out what works and what doesn’t. We’ve done that several times. Virtual nursing is something we’ve worked on a lot. There were all kinds of interesting opportunities brought to us. One facility went ahead and put a solution into a live patient population, and we found out quickly that’s not how you do it. You don’t test that kind of thing in a live environment. It frustrates the staff and patients, and it leaves leadership thinking, “We already tried that—it doesn’t work.”

Well, you tried what doesn’t work. Let me show you what will work.

We needed the opportunity to rapidly figure out what would work. One issue with that failed experiment was that the people who built the carts didn’t understand our environment. They put a wireless access point in the cart that was incompatible with our network. Once we got the cart, we figured it out quickly. We re-engineered it, and it works fine now—but we’re not using that cart because it was over-engineered and very expensive.

We’re trying to use standard components that can be supported and replaced quickly. The idea is to generate a lot of ideas and figure out how to use them appropriately without getting in the way.

You also have to think about the aesthetics of the equipment you’re bringing in. The first cart had a big five-wheel base—kind of a star shape. In some patient rooms, it was in the way. Nursing quickly said, “That’s not going to work.”

So we found an iPad holder that hangs on the patient’s bedroom wall when not in use. It’s out of the way, easy to access, and uses magnetic connectors so if someone snags it, it just comes apart. No trip hazard.

You must consider not only the technology but how it fits in patient rooms.

Originally, the idea was that the Innovation Lab would review the technology, understand how it fits together, and then install it in our SIM labs. We have two—one north, one south. Then the simulation teams would put it in a physician office or patient room and see how it fits before we use it in live care. That’s our next step with virtual nursing.

We also have a lot of conversations with organizations that have fully rolled out these solutions. We learn from their experiences. A mistake is only a mistake if you don’t learn from it. If you do, then it’s experience. We leverage their experience so we don’t repeat the same things, and so we can move quicker.

Rohit: That’s awesome. As we are approaching the end of the podcast, I would like to ask if you would touch on the mentorship program.

Ritu: Yes, I would really like to hear more about that, Chuck, because it’s something unique and I think it would be interesting for our listeners as well.

Charles: Just making sure we’re talking about the virtual mentoring program. After COVID, we were bringing on a lot of new nurse graduates. When you bring someone into that role, they need a more experienced nurse for a procedure they may have never done before. That usually means waiting for that nurse to come to them.

We had a couple of nursing staff in the mentorship program who came up with a way to use technology for an on-screen virtual visit with the new nurse. The experienced nurse could walk them through the procedure and be there with them, or if the new nurse had a question, they could step out into the hall, ask it, and go back in. It improved speed to delivery of care more than anything else.

It also gave seasoned nurses a chance to step away from what they were doing instead of traveling to another location. If they need to go in person, they still do, but this gave us another option. We got great feedback from both the new nurses and our more mature nursing staff, and we rolled it out through the enterprise. I haven’t checked in on it recently, but I assume it’s still running. I only hear when things break, and if it’s not broken, I’m not going to fix it. I assume the technology is still working and paying dividends.

Ritu: Thank you so much.

Rohit: So, Chuck, as we come to the end of the podcast, any closing remarks or thoughts you’d like to share before we finish?

Charles: I’ve been in healthcare a long time. Healthcare is a target rich environment for creativity and innovation. But we’re still taking care of patients the same way we did, and it’s about the human touch and caring for people.

When I first started in radiology years ago, I was taken aback that people weren’t always treated as people. They were exams. Do this gallbladder in this room, do this hip nailing in that room. I was reminded they’re people. They could be my family. They could be my children. That’s why I’m passionate about making sure the technology works and doesn’t get in the way.

Have we reached the pinnacle? No. Is it better? I think it is. But we’re still trying to figure it out every day. As long as we have great people passionate about providing outstanding care and we understand where that ability comes from, we’ll keep moving forward.

We’re a Catholic healthcare system, and our rule is we start most meetings with prayer. We are called to love one another as God loves us, and we need to remember that every day. That’s why I keep doing what I’m doing.

Rohit: Awesome.

Ritu: Thank you so much, Chuck.

Rohit: Really appreciate it.

Charles: Okay. Thanks for the opportunity to share.

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

Ritu: Hi Jan, welcome to our podcast. We are so happy to have you here on The Big Unlock podcast, season six, and we are headed to 180 plus episodes now. Really great having you on the show today. Just a brief introduction — my name is Ritu Roy. I am the Managing Partner here at BigRio and Damo Consulting and a co-host of The Big Unlock podcast with Rohit. And with that, I’ll hand it over to Rohit. He can give a brief introduction and then over to Jan. Thank you.

Rohit: Hi Jan, great to have you here, like Ritu said, and thank you for making the time in the evening from where you are in Germany. Really excited to have this conversation. I’m Rohit Mahajan, CEO and Managing Partner at BigRio and Damo Consulting. And with that, Jan, would you like to start with your intro?

Jan: Absolutely. First of all, thank you both for having me today and for the invitation. Truly appreciate it and looking forward to a good conversation about this exciting topic of artificial intelligence in healthcare. As you mentioned, my name is Jan Beger, Head of AI Advocacy at GE Healthcare. My mission is to transform AI in healthcare from a conceptual promise into a high-impact reality. I focus on equipping healthcare professionals, executives, and the next generation—like students—with the knowledge, skills, and mindset to thrive in this AI-enabled future of healthcare.

Ritu: That’s really great. Wonderful introduction and really interesting title you have, Jan, because we’ve talked to CIOs and CMIOs, but you’re someone who’s the Head of AI Advocacy. I think that’s something new. Would love to hear about your journey—how you came to be in this role, how you combine expertise in healthcare and AI, and what your vision for AI is at GE Healthcare.

Jan: Thank you so much. I hope you agree—based on the many conversations you’ve had in this space with different experts—when we talk about AI in healthcare, those conversations very quickly get technical. But I think in healthcare, and maybe as an entire industry, we have not focused enough on one important aspect: making sure that those clinicians and healthcare professionals we expect to use AI technologies are taken with us on this technology journey.

So, things like change management and education—making sure we focus on the human aspect—is something I think has not been emphasized enough across the industry, maybe even to a point that it slows down AI adoption. This is what I’m really focusing on—what we need to do to remove worries and fears among healthcare professionals, help them gain a solid understanding of the technology, build basic trust in AI, and get them interested in testing and piloting these solutions. Ultimately, the idea is this could lead to faster adoption.

Ritu: Yeah, that’s an extremely valid point, Jan, because I was just at a conference at MIT yesterday, and this was one of the key things that came up — that it can’t be a top-down approach because whenever you ask somebody to do something, their initial reaction is to go into defensive mode and say, “Why should I do something that’s going to take my job away?” or “What’s in it for me?”
So we would be really interested in hearing from you how you are able to get buy-in from the teams across GE and get everybody on board, making sure everyone has the literacy skills, like you said, to understand that AI can actually be a tool to do your job better, increase productivity, bring efficiency, and do all the good things AI promises.

Jan: First of all, I think across different domains and industries — in a health system, but the same in a medtech or healthtech company — everything is being reinvented right now in real time. There’s a lot of activity in that space.
We at GE also look into processes and how we can support our workforce with generative AI technologies. Again, the technology is one part of the story; the second is how we make sure we enable our workforce — 51,000 employees across the globe in 160 countries — to leverage this technology well and responsibly, to truly make a difference in their day-to-day work.

This is exactly the same in the healthcare space as well. We are entering a state of skill redefinition in real time. Things that were important in the past — like routine execution, static expertise, or hierarchical knowledge — are becoming less relevant. And on the flip side, we see more important skills emerging, like adaptability, systems thinking, tech fluency, and AI know-how.

We all have to prepare for this. There was a study recently done by Workday and LinkedIn, which said that 70% of job skills are expected to change by 2030, with AI driving much of this shift. This is not just healthcare — this is across different industries.
Maybe in healthcare, this kind of change will feel a little bit slower because it’s highly regulated. But what I want to say by mentioning this number is that there is a massive technology transformation ahead of us, and a lot of people don’t even understand that this is coming — and coming with quite some impact.

We not only need to continue building great technologies and integrating AI into products and workflows, but also need to create awareness and build AI literacy so that everyone across the healthcare ecosystem can use this technology responsibly for better care and improved patient outcomes.

Ritu: Thank you, Jan. That answer leads into two follow-up questions. First, like you said, the speed of invention is at an unprecedented scale, which we haven’t seen before. The speed is also leading to democratization, where anybody can do low-code or build a tool or a prototype.
That leads to the next thing — this whole concept of AI literacy. If the tools are so easy to use and can unlock so many new ideas, you want everybody across the company to understand how to use them and be fluent. So, how does GE, with such a large employee base — 51,000 employees across 161 countries — handle this? Do you have an overall AI literacy program with different levels, or what systems are you setting up to address this?

Jan: Great question. First, I should define what AI literacy means to me, because it could mean different things for different people.
In a nutshell, I would say it’s three things: one is the competencies required to collaborate responsibly with AI and interact with technologies such as large language models and generative AI.
The second is to be able to explain their outputs.
Third, it’s to be able to critically evaluate those outputs — and then do something meaningful with them. As you know, the worst thing would be to blindly trust those outputs and leverage them in your day-to-day work. So, critical evaluation is a very important part of AI literacy overall.

At GE Healthcare, there is an AI literacy program in place, which is part of a broader Responsible AI strategy. We have different ways to educate our teams — live sessions where they can dial in and learn from experts on how to use generative AI integrated into our tech stack, best practice sharing sessions, and self-paced learning offerings for employees at different levels — from a foundational course covering basic AI terminology to more use-case-specific, in-depth training for specific groups and roles.

Rohit: I was just wondering, Jan, about the key initiatives you’re taking, which are so valuable moving forward for the company and the employees themselves, because they’re basically increasing their skillset as well. We’ve been thinking about some success metrics for our own organization and for some of our clients who’ve been asking for similar services. Any thoughts or ideas, Jan, on what success metrics one could track for such initiatives in any organization setting out on this journey?

Jan: I think those success metrics and KPIs are critically important to measure traction and see where we’re heading and if the investment in these efforts makes sense. For instance, one area where we’ve seen early positive results is with our software developers. With AI capabilities, we’ve seen improvements in speed—getting code done, getting code reviewed, those kinds of things.
So this is maybe one area where, across industries, we already have several best practices and standard ways to measure performance and progress. But then there are other groups of employees where those measures are harder to obtain, or maybe it’s too early because we just started using these capabilities.

For instance, our field engineers—people who visit customer sites to check or repair MRI devices—now get AI support through tools we’ve developed where they can have a natural language conversation with the service manual of a specific device or get help with scheduling. Those are new use cases, and we’re still defining the right success measures or KPIs for them.
There’s a wide range of capabilities and use cases across different groups and business units. Over time, we’re getting better at measuring progress. As I mentioned, in software development we already have specific measures in place and are seeing the benefits of AI, but there are other areas where it’s still greenfield, and KPIs will evolve over time.

Ritu: Thank you, Jan. Our listeners—and most of us—always like to hear about success stories and success metrics, but it’s also important to learn from failure. It would be really interesting to hear from you about a couple of cases where things didn’t go so well, what you learned from that, and how you came back to do something even better.

Jan: I’ll give you an example of a tool we built in-house to support our marketing teams with approved external communication content. We’re feeding a retrieval-augmented generation model with external content so that when a communications specialist gets a request from the media, they don’t need to start from scratch—they can leverage this chatbot, get approved responses, tweak and refine them, and then use them.
It’s useful, but it’s also a lot of work—making sure the knowledge base of the chatbot is always up to date. Maintenance is a challenge and requires manpower and effort. So it’s not just a one-off where you build a cool AI tool and send it out for people to use. A lot of those tools require continuous focus, effort, and maintenance.

Rohit: While you do this at such a global scale, are you traveling a lot and meeting people in person to motivate them, or are you using online tools to make this happen? What are some of the key tools or methods you’re using for the advocacy you’re doing with such a large group of people?

Jan: That’s a great question. When I introduced myself, I should have mentioned that I focus most of my time on our customers—health systems, hospitals, and healthcare professionals—and focus my AI advocacy and literacy work mainly on clinicians.
To answer your question, I travel about 80% of my time. I’ll be in the US next week in Seattle and Atlanta. It’s important to meet medical and clinical experts in person. I always learn from them—I want to understand their concerns, fears, issues, what works, and what doesn’t. Even with great remote technology, face-to-face works best for me.

Of course, there are things that can be done online too. For example, for a few years we’ve been running an AI literacy program for healthcare professionals called Hello AI. When you go to helloai-professional.com, you’ll find more information. It’s a self-paced e-learning offering with two modules where clinicians, students, researchers, and executives can educate themselves about AI in healthcare.

We’ve received great feedback. So far, we’ve educated more than 5,000 healthcare professionals from over 70 countries. Later this year, we’re launching a new learning module built specifically for healthcare executives—because this population is becoming increasingly important in the overall transformation.
Over the last few years, healthcare systems have made progress adopting and piloting AI, but mostly through point solutions—like a decision-support tool in radiology or something with EHRs. Executives now need to think about AI strategically—how to plan, deploy, and measure ROI at a system level. That’s why we’re launching this new offering for healthcare executives on the Hello AI platform later this year.

Rohit: That is fantastic. I think there’s definitely a need for such a thing. Tell us a little more about Hello AI. How did it come into being? Did it precede your joining GE Healthcare, or is it a GE Healthcare initiative? We’d love to learn more about this venture.

Jan: Thank you so much. First of all, when we think about AI in healthcare, there’s often the impression that this is a domain led by big tech or the innovative healthcare AI startup ecosystem around the globe. But that’s only partially true. The reality is that when you look into AI and machine-learning-enabled medical devices, you’ll quickly realize that it’s also a huge play for traditional medtech.
Companies such as GE Healthcare and Siemens Healthineers are leading the pack. The FDA has authorized more than 1,000 AI-enabled medical devices so far, and about 100 of those come from GE Healthcare.

We feel a responsibility as a leader in this field not only to build and integrate great technologies into our devices but also to focus on the change management and education for those we expect to use these technologies. This is how it started—and what Hello AI is.

It’s a learning offering for healthcare professionals and executives built by a network of partners—GE Healthcare, universities, and technology companies—working together to spread the word. Our mission is to make AI literacy accessible and affordable for healthcare professionals worldwide.

We’re trying to provide healthcare AI–specific education, not just general AI education. There’s a lot of free AI content online from big tech, but we focus on healthcare-specific AI education at no or low cost. For instance, we currently have two modules: a free foundational course for everyone, and a more in-depth Professional course with 25 hours of content for just $99.

Rohit: That’s awesome. Would you be open to licensing this as well? In case a large enterprise is interested in your offerings, I’m sure you’re looking at some licensing deals too.

Jan: Our partnership model is threefold. First, we look for partner institutions to join Hello AI and co-develop new content. AI is a fast-paced field, and there’s a lot happening. For instance, earlier today we had a session on federated learning, which is part of new content we’re adding to our modules.
Second, we focus on co-marketing and co-promotion.
Third, when an institution joins us and contributes in these areas, we provide their employees or members free access to Hello AI content.

Rohit: That’s awesome. This is great—you’re offering such a robust platform to increase awareness and education in this space. As we come to the end of the podcast, Jan, any other thoughts or predictions in AI? There’s a lot of agentic AI coming our way—any thoughts for the future audience?

Ritu: And I think Jan wanted to show the device, which might relate to my question: do you have an example where AI has made a difference in patient outcomes—something you’ve put into a device that really made an impact?

Jan: Maybe just quickly, Rohit. I’ll share a use case that’s been very impactful, and then I’ll give a few takeaways. First, as I showed earlier, this is a handheld wireless ultrasound scanner for specific use cases. Imagine an emergency doctor carrying this wherever they go—a powerful imaging tool with no radiation. But one limitation is that it’s very operator-dependent. You need a certain level of education and experience to get high-quality medical images.

So a few years ago, we embedded AI into these machines. It tells you, while scanning the patient, how to move the probe to get high-quality images. This means even less-experienced clinicians can achieve excellent results. The idea is to democratize ultrasound so it’s accessible to more users. That’s just one of many examples of AI making an impact in healthcare today.

A few takeaways for your audience:
First, I strongly recommend everyone—whether in tech, corporate, or healthcare—start rethinking their job descriptions with AI in mind. Think about what you do every day, how AI could support you, and how it changes your role. When you start thinking this way, you’ll begin learning about AI, open your mind to opportunities, and adopt the right mindset to embrace this technology.

Second, we have so many great AI experts, data scientists, and developers worldwide working in industries like gaming or banking. If you know them, tell them about healthcare. If they truly want to make an impact on society, they should consider joining healthcare. It’s still early-stage and slower because of regulation, but if you have this expertise, the industry would truly value it. We can make a real difference here.

Ritu: Thank you, Jan. This was awesome. I’m sure listeners have a lot to absorb and reflect on. Your call to action is excellent—this is an industry where we can see the maximum impact and really help people. Thank you.

Jan: Thank you so much for inviting me.

————

Subscribe to our podcast series at www.thebigunlock.com and write us at [email protected]    

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

Ritu: Hi Thomas. We are delighted to have you as our guest today on The Big Unlock podcast. This is season six, and we are at about 180 episodes. Our listeners love to hear from people at the forefront of AI who are using it successfully in their organizations. My name is Ritu M. Uberoy, and I’m Managing Partner at BigRio and Damo Consulting, and co-host of The Big Unlock podcast with Rohit. With that, I’ll hand it over to Rohit, and then you can get started. Thank you.

Rohit: Hi Thomas, a very warm welcome to the podcast. We are looking forward to an exciting conversation. I’m Rohit Mahajan, CEO and Managing Partner at BigRio and Damo Consulting. Over to you, Thomas, for your introduction.

Thomas: Thank you so much for having me. It’s a pleasure to be with you. I’ve seen many of your episodes—it’s a great podcast. I’m looking forward to the conversation. I serve as Chief AI Officer at Eli Lilly and Company, a great place to develop AI, build medicines with AI, and help the pharmaceutical value chain deliver more medicines to patients.
My background is in AI. I have a PhD in machine learning from a time when it was not cool yet, and I’ve spent nearly my whole career in AI in healthcare at large systems like Memorial Sloan Kettering and Mount Sinai. I’ve had many students and founded three startups, some of which received FDA approval for AI in healthcare. I’m really excited to be here.

Ritu: Wow. Our listeners always love the origin story. So what pulled you to healthcare specifically, Thomas? You said you were in machine learning, but why did you choose healthcare? That always uncovers a very good journey, if I may say so.

Thomas: Very good question. As a kid, my father took me to a rescreening of 2001: A Space Odyssey, and then I wanted to build AI—like HAL 9000, but a good one. When I did my master’s thesis, I had a colleague who trained a neural network to differentiate cancer from non-cancer. I was completely surprised it was even possible. It was amazing that you can do that with code and help patients. It took over 20 years to get that into the clinic, but being able to help people by writing code is amazing. That got me hooked and that’s why I spend my career and energy in machine learning and healthcare.

Ritu: Great answer. Thank you so much. Rohit, would you like to ask the next question?

Rohit: Thomas, you have a unique perspective having worked in startups and having become familiar with AI before the ChatGPT movement, where AI has been democratized and is within reach of everyone. Please tell us more about your role at Lilly and the initiatives you’re focusing on. We’d love to learn more about what you’re setting out to do.

Thomas: Lilly is a fascinating place because, in contrast to many startups or other organizations, the breadth of AI application is enormous. We use AI in drug discovery for small molecules, large molecules, and genetic medicines. We use it in development, large language models to help draft documents for regulatory agencies or answer questions, and to improve workflows across the organization.

There’s also a huge space in manufacturing. There’s a lot you can do with AI and digital twins to produce medicines better and faster. We use time-based models in financial forecasting for profit centers and P&L, and on the commercial side, we use AI to help our sales force contact HCPs and plan routes.

It’s very diverse throughout Lilly where AI can be applied. We have a central registry tracking all AI projects, currently over a thousand across different areas, making it a very interesting and diverse job.

Ritu: Great answer, Thomas. You know, that leads us into the next question. Last year when we were at HIMSS or any of the other conferences, the main keyword was human in the loop. You know, AI is here to augment, not automate. There was a clear distinction that this is going to be a tool that helps you in your work or makes you do things in a better, easier, or faster way.
But now, with the rise of agentic AI and frameworks, we are seeing complete autonomy. The talk of human in the loop is kind of taking a backseat, and we’re seeing that these agents can autonomously accomplish a lot of work. But with that comes the trust factor. Since you said the range of applications is so broad across Lilly, how do you see that?
How are you evangelizing AI at Lilly to help pathologists, physicians, data scientists, and others trust AI and make sure it’s there to help them in their work and augment human capabilities?

Thomas: Very good question, thank you. And of course, a very broad one. First of all, I think it helps to ground the conversation a little bit. These AI models are all fabulous machine learning models and help us tremendously, yes, but there’s no magic behind them. These models don’t have will or volition, and they’re not going to run off and destroy civilization.

Regarding agentic AI, we use agentic approaches in many areas. We use agentic orchestration, for example, in lab-in-the-loop scenarios or in complicated submission systems. But often, the heavy lifting is not done by the language models used to orchestrate everything, but by dedicated models—for example, diffusion models for molecules or temporal models that we use on the financial side.

There’s a lot of AI outside of language that’s very important, but the orchestration is often done in an agentic way, and the LLMs allow us to have an interface to all of these.

Regarding trust, you are spot on. In organizations like ours, and actually very successful ones, it can be more difficult to have that organizational change management. You have to convince colleagues to use new tools and change what they did in the past. That only works if you are deep in the trenches with the various areas where you apply it. You co-develop with them from the beginning, solve real business or scientific problems, and make sure you can actually integrate them into their workflow.

What never works—didn’t work in healthcare or in startups, and doesn’t work at Lilly—is developing something very cool outside and then trying to throw it over the fence into the application area. That always fails. You have to really do it together, and that builds trust. You also have to have complete transparency and be very clear about the goals—what these things can and cannot do.

Some people think AI is magic and will take over everything, while others fall into the pessimistic camp and think it doesn’t work at all. If you are very clear that these large and powerful statistical models can help many processes and accelerate what we do to get medicines out faster for patients, then you can build that trust and integrate AI systems into daily practice.

Ritu: That’s a great answer. I really agree with you because that’s what we heard at other conferences as well—that it can never be a top-down dictate. It has to be a buy-in from everybody in the company. People have to feel involved, and that makes them more open to being part of the change management.
With that being said, Thomas, if you can give us one or two real-world examples of success stories at Lilly where you’ve had great success with your approach to AI, or demonstrated projects that brought people on board and showed the power of AI, I think our listeners would love to hear more about that.

Thomas: Of course, yes. There are many in the language space. For example, our software product engineering team built something called Cortex, and on top of that, there’s Chat in the Box. These are systems any employee can use. If you have hundreds or thousands of PDFs in your area, you can index them, load them there, and build your own chatbot. We have hundreds of very specialized chatbots in the language areas that people use constantly.
We also have larger, advanced systems with guardrails for communications with regulatory agencies, drafting documents, and so forth.

Outside of language, we have a large initiative in discovery—that’s large molecules, small molecules, nucleotide language models for genetic medicines. For some of our targets, some of the leading molecules were already co-designed with AI. We build our own foundation models at Lilly. We have the luxury of an enormous wealth of data over many decades—Lilly is 150 years old—and reaction data from two decades alone provides a huge resource to build property prediction models and de novo design models. They compare very well with other approaches, which is very motivating for the team.

In the computer vision space, we use vision transformer models or other image-based models for quality control in manufacturing. That has allowed us to increase throughput on production lines for millions of injectors. There you have immediate impact with AI and computer vision. We also use AI to build more biomarkers—where I come from, in pathology—to find and classify cancer, and in the preclinical space to assess disease or reactions to medication in preclinical models. Later, in radiology, we use it to track disease. These are very exciting areas with concrete implications at Lilly.

Ritu: Thank you for sharing some of those examples with us. It’s very interesting, and when you say you have data from 150 years, that’s where most companies get stuck because that data is either isolated or not in a shape or form to be used. So that data scrubbing or data labeling itself becomes a huge project. Did you at Lilly feel the same way, or was the data already in a good enough shape or form to be used or input into your AI systems?

Thomas: Of course, that’s always a moving target for every large organization, but I was surprised how well in shape the data infrastructure actually was when I joined Lilly.

It’s quite amazing. But it is, of course, a moving target. Lilly brings on dozens of new devices every single day, every lab that goes online and so forth. We constantly have new data coming in, new data streaming, and new old data sources being opened up.

So, AI allows us to take advantage of data we couldn’t take advantage of some years back, be it unstructured text data or image data and so forth. It is a constant moving target, and as you know, in machine learning, 80% of your work is always data work, and then 20% is machine learning on top. That does not change at Lilly; it’s like at a startup or in academia as well.

What is very unique here is that we can also produce a lot of data. We have various initiatives where we build lab-in-the-loop setups where we can produce huge amounts of data we can then use to train models at a scale I haven’t seen before. That is very exciting, but it’s also necessary, so you can really compare it to the self-driving car industry where you need your models to produce much more data than you could properly collect in the real world. And that combination of synthetic data and real-world data really gives us the amount of data that facilitates the training of these frontier models internally, which we then can use to design molecules or for all kinds of predictions.

Ritu: That’s a very good insight because I remember hearing about Wabi, who are doing self-driving trucks, and they said there’s just not enough training data in the world to train those trucks, so they had to simulate an entire universe and use synthetic training data to train the self-driving trucks. Those trucks are now driving on the roads of Houston, and it’s really interesting to see how they’ve been trained on synthetic data, but they actually proved that the synthetic data is equivalent. There was no difference between the real-world data and the synthetic data. So that’s a very interesting insight. Thank you for sharing that, Thomas.

Rohit: I was thinking about that, such a large organization with a very large number of employees, Thomas, possibly approaching 50,000 employees globally and many different cultures and languages. Now we have this new thing which is AI and very fast-moving generative AI. How do you win hearts and minds, or how do you influence or turn around such a big ship that you are now suddenly the captain of or steering towards AI? Any initiatives or early steps?

Ritu: With AI literacy, I think Rohit wants to ask how generally?

Thomas: I have to say, first of all, it’s actually easier these days than just a few years back. That’s the change of mind of the whole society. If you think about AI five, ten years back, you had to push ideas very hard to be adopted. For example, in a medical setting, these days, sometimes you have the opposite problem. Again, there’s so much demand that you can hardly fulfill it. In our AI group throughout Lilly, you mentioned the global reach, and I think that’s something very important at an organization like ours. For example, I have great teams in Bangalore, and now we also have teams in Hyderabad, in Ireland, all over the U.S., and many places in the world. That also gives you very diverse ideas about AI, the use of AI across the world from very different societies. That certainly makes our work much stronger, much more robust to all the changes that are happening in our society across the globe. So first of all, that helps a lot.

Second, we do a lot of internal education, of course. At Tech at Lilly, everybody has to be certified in foundation courses in AI. We do advocacy and education for everyone at Lilly. There are a lot of resources we bring to bear. And then we have change champions for AI in all different parts of the organization who not only disseminate knowledge but also get ideas back. With our central AI registry, we are collecting this enormous wealth of ideas throughout the organization. Then we can pick and choose the ones which really drive us forward most and which will help the patients most at the end of the day.

But, of course, since we are in a regulated setting globally, there are a lot of things you cannot easily change. You can run fast and break things and fail a lot, as we do in research, but you cannot do that when you are patient-facing. There, you need quality systems at the highest possible standard in AI as well as in all other spaces. And then, of course, there are regulatory requirements not only from the FDA but also the EMA in Europe, or in South America and Asia, and they all have different AI regulations we have to take into account. So we also have a large initiative on the legal, compliance, and ethics side on how we handle AI throughout Lilly and across these applications.

Ritu: So, for some of the projects that you see, what would be the typical time horizon? Because you said there’s an AI registry and people bring you these ideas. Can you give us a little insight into that? Because you said you like to fail fast and try out ideas. So what are some of the typical timelines for these projects?

Thomas: Of course, it depends, but that’s a very good question. We have a risk-based approach. For example, if you do an internal proof of concept on data that’s not embargoed or doesn’t include PHI, you can be very fast. We often have POCs that take only two weeks—really amazingly fast. I was very surprised when I came in how fast Lilly is moving compared to other areas. But then, of course, if you think about discovery—the development of molecules—these are very difficult things. That takes years, and you need the breadth to develop that and the AI for these models over many years. That also needs expertise of computational biologists or computational chemists who’ve used AI in that field for decades.

So, we set up dedicated teams that dive deeply into these areas. These are not things you can just try out. You can’t throw all your electron microscope data into ChatGPT and hope something comes out. These language models will never solve science on that level. You really need dedicated models, dedicated expertise, and years of work in these different areas. But it’s worthwhile doing that difficult part. And it’s also something I always tell startups. We get so many startups who think they’ll just try something with an API to a language model, make a mock-up in two weeks, and then sell that to pharma. But every startup has to be aware: if it only took them two weeks, then our internal software AI team also only needs two weeks. It’s much more worthwhile to go after the really difficult things—the problems not everybody can solve. And of course, those are the most exciting areas.

Rohit: To that point, Ritu, I’m chiming in – it just sparked a thought around the startup world and innovation in large business enterprises. You’ve seen both, Thomas. You’ve bridged the gap. Would love to hear more about your entrepreneurial journey. What kind of startups you ventured into, how you feel now in a large corporate environment doing innovation, and whether the approaches are different or similar.

Thomas: I had my first startup at 16 in Austria in the nineties. That was a very different world than Lilly is now. Back then, I coded a whole hypertext system without the internet—just from books and magazines. That’s very different from the AI-supported coding we can do today. But it teaches you a lot. One thing that stays constant is that you need, at the end, the customer who’s actually interested and buys what you make, and it has to fit into the workflows you’re developing for.

One of the most interesting startups I founded was called Paige—Pathology Artificial Intelligence Guidance Engine. We spun it out in 2017 from Sloan Kettering. There, we built very large models in pathology. We digitized millions of slides—billions of images, 10 times more pixels than the whole Netflix catalog. With that, we built large frontier models in pathology that led to models for cancer classification in prostate cancer, breast cancer, and so forth. That led to the first breakthrough designation of any AI in oncology from the FDA and the first approval of an AI in pathology. These models work surprisingly well across the globe, so they can generalize.

But in practice, of course, you have hurdles in terms of reimbursement and digitization. Even in the U.S., only 10% of hospitals are digital in pathology. In the other 90%, diagnostics are done like 150 years ago—pathologists looking through a microscope and making a judgment on a slide instead of having a quantitative readout. There’s a huge gap between what we can do today in AI and diagnostics and what patients actually get access to. As William Gibson said, “The future is already here—it’s just not evenly distributed.” You see that constantly.

One reason for joining Lilly was that we have the enormous opportunity to reach millions of patients. You can have an impact at a much larger scale than before. Many of the drugs we’re developing are for terrible diseases like obesity that affect over a billion people globally. To do something against that and help with AI is, of course, fabulous. The sheer scale is very different than back in the day, but of course, a large organization is also very different from a startup.

Rohit: Tough job. I would say Thomas.

Ritu: But extremely rewarding. We’d like to ask Thomas for some closing comments—where do you think the future is headed? What do you think the next three top trends are going to be, for Lilly or for AI in general? Anything else you’d like to share with our listeners? Thank you, Thomas.

Thomas: That’s a difficult question—predicting the future is always challenging. A big area is de novo design of molecules. This is enormously challenging, but the speed of change there is dramatic. Although some are skeptical because we can’t pump out new medicines with AI the way we can generate poems in language, the sheer scale at which we can now propose new molecules that are synthesizable and have attractive properties is a huge phase change compared to one or two years ago. That will impact how fast we can develop drugs, but more importantly, whether we can find the right drugs for the right targets.

When you start solving that problem, the next challenge becomes development—how to prove these drugs work efficiently. That requires rethinking how we run trials. Some of our submissions have 100,000 pages of documents to regulatory agencies—nobody’s going to read that. The agencies will use AI to read it. So wouldn’t it be better to exchange data and do that faster, to get drugs to patients more quickly, given they’re safe, effective, and equitable? If we can prove that, we can hopefully increase the number of medicines delivered globally.

That’s why there’s also a push for small molecules—it’s easier to ship a pill than an injector, which democratizes access to medicines across the world. To do that effectively, we’re again in manufacturing—you have to do it at scale very efficiently. That’s a huge challenge and includes not only digital twins but also robotics, not always humanoid, but industrial robotics, autonomous mobility on manufacturing floors, and across the world. It’s a very exciting area.

Then, of course, a lot of education—not only for healthcare providers but also for patients globally. Hopefully, we’ll have better mechanisms to get drugs to patients. We should never forget that of the increase in life expectancy over the last 100 years, 20 years of that came from better medicines. It’s medicines that make our lives better, and that’s a worthwhile goal to pursue. Doing that by writing code is just amazing.

Ritu: Thank you, Thomas. Great closing thoughts, and I’m sure that leaves a lot for our listeners to think about.

Rohit: Thank you, Thomas, for such an engaging discussion. We really appreciate it, Thomas. Hopefully, we’ll be back again sometime soon to discuss another topic.

Thomas: Thank you for the invitation and for a very engaging conversation. It was fabulous.

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Subscribe to our podcast series at www.thebigunlock.com and write us at [email protected]    

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

Rohit: Hi Girish. Welcome to The Big Unlock podcast. Really wonderful to have you here.

Girish: Hi Rohit, hi Ritu. Thanks for having me. It’s a pleasure.

Ritu: Welcome, Dr. Girish. We have a lot of episodes—175 and counting. Listeners are always in for a treat, and they enjoy hearing new perspectives on AI. Every physician has such different thoughts about AI, how they got here, and what they think future trends will be. So, I’m looking forward to an engaging and interesting discussion. Welcome once again.

Rohit: Thank you. I’m Rohit Mahajan, Managing Partner and CEO at Damo Consulting and BigRio.

Ritu: I’m Ritu M. Uberoy, currently based in Gurugram, India. Also, a Managing Partner at BigRio and Damo Consulting, and co-host of The Big Unlock podcast with Rohit. Looking forward to our discussion today. We’d love to start with your introduction.

Girish: Thanks, Ritu and Rohit. My name is Girish Nadkarni. I am the Chair of the Windreich Department of Artificial Intelligence at the Mount Sinai Health System. The department is the first of its kind in the country, established within a health system and medical school.

I’m also the Director of the Hasso Plattner Institute for Digital Health at Mount Sinai. Those are my academic roles. In addition, I serve as the Chief AI Officer for the health system. Clinically, I’m an internist and nephrologist, and I still actively see patients about 25% of my time—both in outpatient settings and on the clinical floors.

Most of my time is spent doing research, but more importantly, translating, deploying, and scaling that research into clinical care and operations.

I grew up in Bombay, India, which some of you may know is one of the largest cities in the country. I live in New York City now, but the scale of Bombay—or Mumbai—can put New York City to shame, just in terms of the number of people.

I went to medical school in India. From a young age, I was interested in tech and computer science. As you both know, in India you typically either go into engineering or medicine. My parents made it clear: you’re either a doctor or an engineer—or you’re a failure. Both of my parents were doctors, and although I cleared the engineering entrance exam, I was drawn to medicine.

Medicine is a noble profession. What’s more noble than helping and healing people when they’re most vulnerable? More importantly, I was interested in prevention—helping people before they became that sick.

During medical school in India, I always wanted to further my training in the United States because it has led the world in innovation at the intersection of medicine and technology. I came to the U.S., completed a master’s degree and postdoctoral fellowship at Johns Hopkins, and since 2009 I’ve been in New York City.

I did my residency, fellowship, research, operational training, and faculty roles all at Mount Sinai. The health system is unique because it combines a medical school and a health system, which creates a free-flowing exchange between academic discoveries and clinical operations. Research can be translated and scaled across the system, while clinical practice inspires new basic research questions.

I feel fortunate to be in this role. Healthcare is a field where change is often imposed externally—by policies or new technology—but meaningful change must also come from within. If clinicians broadly—physicians, nurses, and other health professionals—become true evangelists for responsible tech, we can create transformative change in healthcare like never before.

Ritu: That’s a great introduction. Thank you so much, Girish. I’m really curious—right now with AI and the rapid pace of change, especially with LLMs, we’re hearing about a lot of physicians and clinicians getting into tech. But your interest in tech predated all of this. Tell us a little bit about that—how you felt back then versus now.

Today, people feel that AI is so enabling. It has democratized access to technology and made it easier for non-tech people, especially clinicians and physicians, to prototype and build things they couldn’t before. Do you feel the same way? Do you think the pace of change has been like light years’ worth?

Girish: I’ve been in this field for the last 15 years, and my interest in tech has always been for medicine’s sake. Technology is one way you can scale impact in ways a single physician—or even a single healthcare system—cannot. Tech can touch millions of lives at scale, which is what drew me to it.

The idea is to take what we know from medicine and codify it into a knowledge base that can then scale across demographics, populations, countries—even globally. That’s the promise of tech.

Yes, things have gotten easier in terms of building and prototyping because of large language models. If you think about what LLMs are, they’re essentially encoded human knowledge at scale. They’ve read all of human knowledge, compressed it, and made it possible to converse with and ask the right questions to get back knowledge at scale.

This democratizes access. Now, if a physician has an idea, they don’t need to rely on a lengthy process to prototype it. They can bring their idea to life as an app or a product and test how it works.

But this also introduces unique challenges. Let me start with a couple of assumptions I hope we all agree on. First, healthcare is an industry where trust is paramount—trust between provider and patient, and trust between the system and the broader social and cultural environment it operates in. We must safeguard that trust.

Second, while scale is useful, it has a flip side. If goodness can scale, badness can scale too. A useful, effective product can scale—but so can mistakes or bias. That’s why it’s essential to be rigorous, responsible, and trustworthy in healthcare. This is a trust-sensitive, patient-centric industry, and we cannot allow mistakes or bias to scale unchecked.

So yes, prototyping and scaling make sense—but they must be backed by rigor, good governance, and solid evidence.

Ritu: Great. That’s what we’ve been hearing across the board—it comes with its own baggage, and you have to be really careful to, as you said, have rigor, responsibility, and trust, which are paramount. Great answer, thank you. Rohit, would you like to ask a question?

Rohit: Yeah, Girish. Could you tell us a little more about your AI journey, or share examples you’ve seen that have positively impacted patients—something we could talk about today?

Girish: Oh, absolutely. I can talk about the broader AI landscape in healthcare right now. I think healthcare is at a transition point. The U.S. healthcare system costs a lot of money and is focused predominantly on sick care rather than healthcare. We wait for people to get sick, then we treat the sickness. The financial incentives are also aligned that way—and that needs to change.

The promise of AI, in my view, is that it’s an “arbitrage of knowledge for time.” What I mean is that because knowledge is compressed and encoded, you can use it to buy time for making decisions.

A great example in healthcare is ambient scribes. As a physician, suppose I have a 20-minute patient visit. In practice, I spend 10 minutes talking to and examining the patient, then another 10 minutes typing up my note. Alternatively, I could spend the full 20 minutes with the patient—but then I’d have to go home after putting my kids to bed and finish my notes.

Ambient AI scribes flip that paradigm. During a natural clinical conversation with a patient, you don’t have to type into the computer. The entire interaction is transcribed into a structured note. All you have to do is review it for accuracy and sign off. What used to take 10 minutes per patient now takes 30 seconds. That’s what I mean by arbitraging knowledge for time—you gain time back with every patient and avoid doing data entry after hours.

Another example: imagine if you knew a patient was going to deteriorate in the hospital one or two hours before it happened. That time would allow you to make a life-saving intervention. That’s another way AI arbitrages knowledge for time—by shifting care from reactive to proactive.

We developed a deterioration model that predicts if a patient is going to get sick an hour in advance. It allows clinicians to intervene, maybe give IV fluids or switch antibiotics, before the patient worsens. That model is already in production.

A second example, very close to my heart, is in the neonatal intensive care unit, where premature babies are often the sickest patients in the hospital. Using vision AI—similar to the technology in self-driving cars—we can continuously monitor babies and predict an hour in advance if one is going to get sick. That gives care teams time to act and potentially prevent deterioration.

So again, I like to say AI in healthcare is an arbitrage of knowledge for time. It gives providers time to make critical decisions and also gives them back time in their day that would otherwise be lost to data entry and processing tasks.

Ritu: Amazing. This neonatal ICU example is incredible. I can only imagine how many babies’ lives have been positively impacted. That’s a wonderful story—thank you for sharing, Girish.

Rohit: Girish, I think we were talking the other day about traditional AI and the new AI—generative AI and large language models. How do you distinguish between the two in your team? And how do you build teams for AI work, since these tasks often require cross-functional expertise—problem definition, technical skills, and domain knowledge? How do you structure that at your organization?

Girish: That’s a big question, but I’ll try to answer it in parts. AI has been used in medicine for a while now, but most of it has been predictive AI. It’s been, “Given this set of data, what is the probability that something good or bad will happen to the patient in a certain amount of time?”

The difference between that and generative AI is that predictive AI is usually deterministic. If you enter the same set of features, you’ll get the same answer every time—it’s very repeatable. New AI, on the other hand, is much more flexible and non-deterministic. You can get different answers to the same question depending on context.

It’s almost like the left and right sides of the brain. Predictive AI is extremely logical, follows a process, and arrives at an answer. Generative AI is more creative and non-linear. Both have to be governed differently, and the way you evaluate them is also different.

With predictive AI, you focus on making sure it isn’t biased against certain populations, and that when you deploy and monitor it over time there’s no drift—meaning the model doesn’t degrade as the underlying data changes. Generative AI requires a more holistic evaluation. You have to ensure it aligns with a broader set of ethics and values, and put guardrails in place so it doesn’t deviate from them.

We’re setting up what we call an “assurance lab” to monitor both predictive and generative AI in different ways, but with the same underlying goal: AI must be safe, effective, responsible, and ethical.

And for that, you need cross-functional teams. You need someone who understands the clinical problem—that could be a doctor, nurse, medical assistant, or allied professional. Then you need someone who can build AI models at scale, usually an MLOps or AIOps engineer. You also need researchers, because sometimes you need people to think outside the box and explore different solutions. Finally, you need a layer of ethics—people who understand the risks and ensure the work is responsible.

We’ve set up a governance structure that combines all digital and AI applications, divided by domain. There’s an AI care domain focused on clinical care; an AI operations domain for back-office operations, finance, and regulatory; an AI workforce domain for workforce applications; a research domain; and a student domain. All of these are underpinned by the assurance lab and by the REP—Risk, Ethics, and Policy—committee.

Why is this important? Because even if an algorithm is pristine, its application can have issues. For example, you could have an algorithm to predict which patients won’t show up for clinic appointments. But if applied incorrectly, you might cancel the appointments of the very patients who most need care. Often, patients who miss appointments have unmet social needs. The right solution isn’t to cancel on them—it’s to address those needs, like providing transportation or financial support.

So yes, you need cross-functional and multidimensional teams to tackle clinical, operational, or workforce problems. But everything must run through governance to ensure the work is safe, effective, responsible, and ethical.

Rohit: That’s great to know. And when you look at these applications, do you have some kind of framework or success metrics—what people often call return on investment? How do you go about prioritizing projects, especially now that so many physicians are becoming interested and more ideas are coming your way as Chief AI Officer?

Girish: Yes. We have a process where anyone in the health system can submit an AI or digital idea. I work closely with the Chief Digital Transformation Officer, Robbie Freeman, and our Chief Digital Officer, Lisa Stump, on this.

First, as a health system and academic institution, we define a list of clinical and operational priorities for the next 12–24 months. Part of the process is aligning ideas to those priorities. If an idea aligns with strategic priorities, it gets higher prioritization and faster execution.

Second, every idea must go through governance, as I described earlier, to ensure it is safe, effective, ethical, and responsible—not only in development, but also in execution.

Third, we calculate ROI for all priority projects. But this is a multidimensional ROI—it’s not just financial. We also consider workforce impact and patient experience impact. It’s a holistic, 360-degree evaluation.

We also monitor projects over time to ensure they meet predefined milestones. For example, with an ambient AI project, one milestone might be provider satisfaction. How many providers are satisfied with it? We track KPIs, and if a milestone isn’t met, we require an explanation and a remediation plan.

This way, we ensure ROI is not just about finances—it’s about employee experience, broadly defined, and patient experience as well.

Rohit: And one tough problem I’ve seen before—and something the C-suite often struggles with—is where the money comes from. If you’re focused on cost savings, you might say, “We’re going to save costs by becoming more efficient.” That’s one way to look at it. But how do you think about budgeting for AI? Is it longer-term or shorter-term? Any high-level perspective you can share?

Girish: If you align your roadmap—your operational map—with the larger strategic priorities, then you automatically get C-suite buy-in. These are priorities already set by leadership, and we’re just enabling and accelerating them. That also makes it easier because budgets have already been allocated for those priorities.

And honestly, the cost of generating software has gone down significantly. You know this better than me—the cost of coding agents and developing software is far lower than it used to be. So if incentives, strategic priorities, and broader vision are aligned, the more tactical, lower-scale items start to align as well.

Rohit: That’s great to know.

Ritu: So Girish, you mentioned “ambient” a couple of times, and we all know that’s been one of the big success stories across hospitals and health systems. Our listeners always want to hear what’s coming next—straight from our guests. In terms of trends, what do you think will be the next big win for AI? Where is it headed in the next 6–12 months?

Girish: In the next 12 months, I think we’ll see much more multimodal integration, which is especially relevant in medicine. Right now, most AI is text-based. But that’s not how clinical decisions are made. Clinical decisions involve talking to the patient, observing them, noting how they look, how they speak, and even their frame of mind in that moment.

The technology isn’t fully there yet, but I think it will be in the next few years. And I still believe that in the short term—say, the next 10 years—it’s about augmentation rather than replacement. These systems, while powerful, are fragile, and trust is a huge factor. People aren’t ready for a fully autonomous AI doctor.

So I see multimodal integration—combining text, voice, images—becoming more common. We’ll see a push beyond ambient recording, perhaps toward video recording with patient consent, sensors, and more. Broadly, though, I think the shift will be from reactive medicine to proactive medicine, and AI in all its forms will be an enabler of that.

Ritu: Great point. We’ve also been hearing a lot about voice agents and conversational AI, so I think you’ve hit the nail on the head there. Thank you.

Rohit: Thank you, Girish. As we come to the close of the podcast, are there any final thoughts you’d like to share with our listeners?

Girish: I’d just say this: AI is a very impressive technology. For the first time in human history, it’s approaching cognition, which until now was the preserve of humans. We should think of AI as a collaborator, not a replacement—something that helps us become the best version of ourselves.

At the same time, we shouldn’t wear blinders and assume it’s flawless. We need honest assessments, constant monitoring, and recognition of its flaws. There’s hope, and yes, there’s hype, but it should always be tempered by reality and rigor.

Rohit: That’s a great closing thought. Thank you so much, Girish. We really appreciate your time.

Ritu: It was wonderful having you on the show. Thank you for joining us.

Girish: Thank you both. It was a pleasure.

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Subscribe to our podcast series at www.thebigunlock.com and write us at [email protected]  

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

Q: Hi, Dr. Michael Docktor, welcome to our Big Unlock podcast. We are really happy to have you as a guest on our show. Uh, it’s on our sixth season now, and, uh, we are looking forward to this conversation. I’m sure we’ll have a very engaging and interesting conversation about dog health, about healthcare in general, and the new trends about AI and all the buzzwords that people want to hear. Welcome to our show — we’d like to start with introductions. Happy to have you give your introduction. Thank you.

Michael: Yeah, thank you so much for having me. Yeah, I’m Michael Docktor, co-founder and CEO of Dock Health. I’m an ologist by trade. I am now on this, uh, healthcare venture outside of Boston Children’s Hospital where I started my GI career.

I was a busy clinician working on the clinical side as well as in innovation and informatics, and found that we had wonderful tools in the world outside of healthcare and struggled with my own challenges around managing all the tasks that are part of patient care. I found that I was using task management and project management tools that were not built for healthcare.

I scratched my head and wondered why we don’t have these things for healthcare. That was a journey. We started just over five years ago. We spun out Dock Health from Boston Children’s, and we’ve been on a very exciting journey in the world outside the academic walls since then.

It’s been fun. I’m excited to talk to you more about, uh, what we’re doing.

Q Great. That’s a great introduction. Thank you for letting our listeners know. We usually like to start with how you came into healthcare because everyone has an interesting story about what inspired them to become a physician.

For you, it’s particularly interesting because you’ve created this interface between design, technology, and healthcare — and you went beyond just visualizing what you wanted to do or asking why somebody doesn’t fix it, to actually building it yourself. I’d love to hear more about how you came into medicine and then into technology.

Michael: I could probably spend the entire podcast on this question. Obviously, my last name is Doctor, so there was some planting occurring there, and there’s something to be said for that.

One of my earliest memories was wanting to grow up to be a doctor. My younger sister had some GI issues when we were young, and I became very close with her gastroenterologist. I followed her on her journey and, unbeknownst to me, ended up following one of my mentors and was inspired to do the work for other children the way my sister was cared for. That was the initial inspiration.

You mentioned design. That’s interesting because my mother is an interior designer, my father is a dentist who specialized in aesthetics, and design — how things look, feel, and work — has always been part of my DNA. It was an amazing journey to go from being a traditional doctor in pediatric gastroenterology to discovering the importance of user experience and user interface.

Then the iPhone came out, then the iPad, and that really started me on my journey of exploring app development and software, and wondering why we didn’t use these wonderful devices in our pockets the way we should. That journey started more than 10 years ago and has led me to this perfect intersection of healthcare, design, and informatics, creating better experiences for patients and providers.

There’s a lot to unpack there, but people ask me if I would do it differently if I had the chance to do it all over — and absolutely not. I’m doing exactly what I want to be doing right now.

Q: Thank you for sharing that. That gives us so much perspective into how you came into this field, the influence of your parents, and how everyone around you inspired you to do this. That’s really interesting.

That leads us into how you built Dock Health. Tell us some examples of how you saw this becoming transformational in actual implementations with clients. When you built the tool, did you visualize exactly what it would do? Did it go beyond what you thought it would achieve in a healthcare setting? We’d love to hear more about that.

Michael: We’re far beyond where I ever thought we’d be when we first started this journey. That’s the exciting part. Fundamentally, we have such an administrative and operational challenge in healthcare. I saw it firsthand as a busy clinician struggling to manage all the clinically adjacent work that had to be done for my patients.

For example, I might have a patient with Crohn’s disease who needs to go to an infusion center. There are all these downstream tasks and administrative steps that need to be done by a team of people. We didn’t have assistants to do that, and the electronic health record wasn’t built for it.

A quarter of the $5 trillion spent in healthcare goes to administrative and operational challenges, and there really are no tools built for that. We rely on paper checklists, email, faxes, and spreadsheets to manage all the work happening outside the electronic health record.

For me, the realization was that I was using wonderful project management and task management tools to run software projects in my innovation and informatics roles—and yet nothing like that existed in healthcare. Patients, in many ways, are projects. There are tasks that form the essence of care, and they need to be tackled, managed, and collaborated on, with visibility and accountability across teams. That didn’t exist.

What we are now doing is serving as that productivity platform for healthcare, one that sits alongside the electronic health record in synergy, helping manage all the administrative and operational tasks of patient care in ways the EHR wasn’t designed for.

Some examples would be managing patient intake and all the processes associated with it. Referrals, inbound and outbound, with all the paperwork, eligibility checks, and faxes—those workflows are still incredibly manual. Referrals are a great example: 80% of referrals that come into major academic medical centers are still arriving by fax, despite the fact that they’re using Epic, a wonderful electronic health record.

These processes are still incredibly laborious, take too long, and are inefficient. Patient care suffers along the way. There’s a gigantic bottleneck around the administrative and operational work that moves patients through their journeys, and that’s what we’re focused on fixing.

Q: This is what we hear from a lot of our clients and other CIOs and C-suite leaders who come to our podcast—that this is one major problem with healthcare. It’s siloed, and the processes are really stuck in the dark ages.

We were talking to somebody from Athena, and they said they had an 800-person offshore center in India just to screen faxes. When a fax came in, one person looked at it to see if it was a pizza order or a referral. It sounds unbelievable in this day and age, but that’s how it works. I’m amazed to hear this from you as well. It’s remarkable.

Michael: Yeah. In fact, Athena is a partner of ours, and we’re working on referral management. It’s an incredibly antiquated process where everyone loses visibility. The amount of manual labor required to process these things is shocking in this day and age, when they can be handled through process automation, AI, and agents.

Q: Exactly. You’re talking about agentic, we’re talking about voice agents, and yet we’re still dealing with faxes. That’s really interesting to know.

Michael: There is reason to be hopeful. Healthcare is always many decades behind, but the adoption of AI is progressing more rapidly than I’ve seen any technology adopted in healthcare. There’s reason for hope.

Q: That leads into my next question, but before we get there, you mentioned Dock Health as a productivity platform. That’s what it does for doctors, clinicians, and patients. But when we talk to our doctors, clients, and health systems, the one word that always comes up is complexity. They deal with so many different systems and have to learn something new each time.

Where does Dock Health fit in? Is it another system they have to manage, or does it help reduce that burden?

Michael: Yeah, it’s all about reducing the burden and improving the user experience. That’s been fundamental to our belief system since the founding of the company.

First and foremost, we’re trying to become the productivity platform and the hub that handles the administrative and operational work. Instead of being “another system,” we’re often replacing spreadsheets, paper checklists, emails, and faxes—the ways we’ve traditionally managed the 50% or more of administrative and operational tasks required for patient care.

The EHR is great for clinical work, billing, prescribing, and scheduling, but so much happens downstream that isn’t covered. I’d argue we are creating the hub for that. Just as the EHR is the clinical hub, Dock Health serves as the operational hub. Together, it’s really one plus one equals three.

We’re also aiming to be much more consumer-like in our approach. Our tool can onboard a customer in 30 minutes. We offer a free trial, and for small organizations that want to get up and running, they can start using it right away—just like downloading an app. It’s lightweight, highly configurable for end users, and designed with a strong focus on user experience.

The real challenge is that clinicians don’t want another tool. They’re busy and need to spend their time in the EHR. Our core users are often the super users: front office staff, back office teams, operational leaders, and administrative staff who leverage our tool every day.

The important piece is that we connect to the electronic health record and provide visibility for clinicians. If they’re interested, they can see where a patient is in their journey or where a process stands, but they don’t have to be in our system all day, checking tasks or learning another workflow.

The intent is to provide that operational hub for administrative and support staff—the essential members of the care team—while clinicians continue their work in the EHR and often drive tasks into our system through their orders and actions there.

Q: Is the integration with the EHRs native, like with Epic, Cerner, and the big ones?

Michael: Yeah. We’re on the marketplace with a number of the top EHRs, including Epic and Cerner. We’re also working with Mayo Clinic, their Mayo Clinic Platform, and their Deploy platform.

The other thing I’d mention is that many organizations use our tools without being integrated with the electronic health record. That’s often because they’re smaller practices—mental health, concierge care, or primary care—that simply need a hub to handle administrative and operational work and collaborate as a team.

We spend a lot of time helping organizations define their processes, and those processes can then live in Dock, where they can be collaborated on and turned into operational best practices. That’s something that often doesn’t happen in traditional healthcare organizations, and our tool is valuable even when it’s not connected to an EHR.

Of course, the real value comes when we integrate with systems—EHRs, CRMs, patient engagement tools—and serve as the hub while automating as many tasks as possible. That’s when the flywheel starts turning, and organizations begin to feel the benefits of automation and work reduction. It ultimately moves the needle in a measurable way.

Q: So, you’re saying if there’s an integration with the EHR, then it’s end-to-end and people can see a complete view of what’s happening. But even without the EHR, because it helps with workflow management, it’s valuable for practices?

Michael: It’s just like every other industry. They have Asana, JIRA, Trello—all these tools where a lot of the real work gets done. That provides value in itself by giving a clear view into where the patient is in their journey and what process they’re following.

When we integrate with an EHR or other systems, we can then automate those workflows. For example, when a fax comes in, we can launch a workflow for a new patient referral. When a lab order is placed, we can launch a workflow for follow-up. Or when a patient has a procedure, we can launch a process for their downstream follow-up.

Q: That leads into the next question, which is your learning journey with AI. What advice do you have for our listeners about where AI is headed? And with voice agents and agentic tech being buzzwords, where do you see Dock Health going? Are you also looking at implementing voice agents?

Michael: AI is incredibly exciting. I’ve put it out there that we’re an AI-first company. We’ve been thinking about this for a while, and we already have what we call Dock Intelligence in production with customers. We started with the basics, using foundational models and generative AI to process large troves of data.

Now, we can summarize all the work being done for a patient and create a snapshot for the end user—showing all tasks, workflows, patient context, and even clinical context if available. That gives a quick overview of what’s happening. Users can edit that snapshot if needed, or generate a note that writes back to the EHR to keep the operational and clinical record in sync.

That’s the first step in our AI journey. From there, we realized tasks bundle into workflows, workflows can be automated, and now we’re starting to farm out those tasks to agents and AI. For example, a voice agent could follow up with a patient to confirm a referral or review lab results. We’re actively pursuing that, as well as leveraging AI to extract data from faxes to automate referral processes.

AI is the big unlock. In the next three to five years, I believe most administrative and operational tasks will be handled by automation and AI agents orchestrating together. These agents need rails to run on and structure to guide them, and end users must remain in the loop to provide oversight and visibility. But the lion’s share of this work will be handled by agents—and we’re building that future right now.

Q: Are you aligning specifically with any particular LLM or provider, or are you going to be completely agnostic?

Michael: I’d like to think we’ll be agnostic. We’re doing plenty of building on some of the foundational models ourselves, and we’re partnering where we can with great tools. For example, with voice agents, we’re not necessarily building them ourselves—there are excellent options out there. What we are doing is building our own homegrown AI agents that can manage processes and facilitate handoffs from task to task.

Our approach has always been to recognize that there’s a wonderful ecosystem out there. We want to be the hub that connects to the best-of-breed systems—whether that’s Epic as an EHR, Salesforce as a CRM, or other tools we can leverage to create a healthy ecosystem. That’s what we’re pursuing.

Q: I wanted to ask a question about that because with the LLMs, one of the main problems is the hallucinations or the repeatability or the trust factor there. So I saw that Dock is HITRUST certified. So, I just wanted to ask, what are your views on that? Because in terms of using generative AI when we talk to our clients, uh, for implementations on LLMs, or they’re more comfortable certainly with the administrative tasks, but even with the administrative tasks there’s always the question of what are the guardrails you put around that and how do you make sure that the results that you are seeing or you are displaying on Doc Health, you know, stay within that, those bounds of accuracy. Whathat are your thoughts around that?

Michael: AI is incredibly exciting—it’s moving at the speed of light, and everyone wants to benefit from it. I do think administrative and operational areas of healthcare are lower-hanging fruit and far less risky. We’re not yet in a place where we can reliably use AI for absolute clinical decision support and autonomy, though I believe we’ll get there in the not-too-distant future.

For now, the clunky and manual administrative work is ripe for AI. Still, we need to be mindful of potential hallucinations. In our experience so far, we haven’t seen issues, but we’ve put safeguards in place. That means keeping a human in the loop to ensure outputs are appropriate and relevant. We also give users the ability to edit results before they’re sent—whether it’s a summary being emailed to a colleague or written back into the EHR as a clinical note.

It’s essential to show your work. If we’re pulling data from various sources, we show those sources. We keep users in the loop, give them control to review and edit, and maintain tight feedback loops to catch and address anything off.

So far, even foundational LLMs have been surprisingly strong in our relatively narrow use cases. The real challenges will come as we move toward higher-risk clinical decision support—but I’m confident we’ll get there quickly.

Q: It’s interesting you mentioned humans in the loop and the fact that people can review or edit what’s being generated. But with agents, one of the key defining characteristics is autonomy—they can make API calls, send emails, even browse the web to find and act on information. How do you think about that level of control being handed over?

Michael: I think it’s all about balance. The value of task automation and workflow automation is clear. For us, one of our strengths is providing a user interface where the end user can actually see where a process is in its journey.

We’re working on features like showing confidence levels or confidence intervals for data extracted—for example, from a fax. That way, we not only show our work but also give transparency into how accurate that extraction might be. Using our interface, which breaks workflows into tasks, we can show exactly where a process stands. If we believe human involvement is needed, we assign a task and call someone in to review.

Even in the agentic future—which is already upon us and actively being built and tested—we see ways to keep humans in the loop. Operating entirely in a vacuum is risky. Part of my mantra, coming from clinical practice, is that I lacked visibility and accountability in the work being done on behalf of my patients, lost in a black hole of faxes and emails. It’s important to show where the patient is in their journey, where the process stands, whether an agent is handling it or automation is moving data between systems. Giving people visibility and the ability to drill down is critical.

Q: That’s an interesting viewpoint. You mentioned the front and back office and clinicians, but I didn’t realize patients might also have visibility into the system.

Michael: That’s certainly the vision for the future. Right now, our solution focuses on creating visibility and accountability for the care team—clinical and operational staff. But ultimately, the patient is part of the care team too. I would love to see a future where patients are assigned tasks and the rest of the care team has visibility into their progress. I think that’s inevitable. For now, we’re focused on giving the traditional care team the tools they need, but over time, I see patients and their home-based care team being part of that ecosystem.

Q: So I think we are almost at the end of the podcast. We would like to conclude by asking you what you think, maybe give a horizon of like a year and then maybe a five-year horizon. What do you think the top trends that are going to be in AI, and particularly for you at Dock Health? What are the three things that you are really keeping close tabs on and you feel that it’s going to really impact your product and be very transformational.

Michael: It’s tough to predict because AI is moving so fast. Moore’s Law doesn’t apply anymore—the doubling is happening every few months. Everything right now is AI, and “agentic” is the word of the day. I’ve even heard “ambient agents.” We see our role as orchestrating workflows where agents will carry out the majority of tasks.

When we started Dock Health, I imagined a day when it would be smart enough to handle processes automatically. AI has accelerated that roadmap by five to ten years, and the pace keeps shrinking. I truly believe that in the next five years, when healthcare spending in the U.S. reaches $10 trillion, the $2.2 trillion spent on administrative and operational costs may actually shrink because of operational efficiencies unlocked by AI.

We want to give care teams superpowers. Administrative and operational staff deserve better tools to do their work. Just as ambient scribes are now relieving clinicians from documentation, I believe AI will do the same for the administrative and operational burdens in healthcare. Within three to five years, I expect most of that work will be automated. Humans will still play a role, but much of it will feel like it just happens—almost magically.

If you zoom out five years from now, it will look like magic. But by then it will feel obvious. And that excites me, because at the end of the day, patients, our economy, and the world need more efficient care. The fact that we waste a trillion dollars a year on administrative tasks is shameful. Patients are asked to take on too much, like reminding us about referrals or tracking down lab results. These things happen because we’re human and don’t scale well. But now we have incredible tools at our fingertips, and I believe the next few years will be the most exciting time healthcare has ever experienced.

If we zoom out five years from now, it’s going to look like magic—but by then it will feel obvious. I’m excited for that future because, at the end of the day, patients, our economy, and our world need more efficient care. The fact that we waste a trillion dollars a year on administrative and operational tasks is shameful.

Too much of the burden falls on patients. Physicians and care teams often say, “Call me if you don’t get your referral in a couple of days,” or, as I saw with my own mom recently, patients are left chasing down lab results. These things happen because we’re human and we don’t scale well.

Now, we have incredible tools at our fingertips. That’s why I believe the coming years will be the most exciting time in healthcare that I’ve ever experienced—and likely the most exciting time the world has ever seen. Healthcare is primed for transformation and opportunity.

Q: Thank you for being with us on our podcast. It was a wonderful conversation, and we look forward to sharing your vision for the future with our listeners.

Michael: Thanks so much.

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Subscribe to our podcast series at www.thebigunlock.com and write us at [email protected]  

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