Ritu: Hello, listeners. Welcome to Season Seven of the Big Unlock Podcast. My name is Ritu Oberoi, and I’m managing partner at Damo Consulting and your host today. We are welcoming Michael Archuleta to our podcast. Michael is the Chief Information Officer at Mount San Rafael Hospital, where he has helped transform a rural critical access hospital into a nationally recognized leader in digital innovation, cybersecurity, and healthcare IT. Michael is a perennial Becker’s Hospital CIO to Know, nationally recognized speaker, advisor to health technology companies, and someone whose work has spanned cloud transformation, cybersecurity, and AI adoption — and more recently, one of the most significant technology initiatives in the hospital’s history: the transition to Oracle Health. We are really excited to have Michael here with us today. Thank you for joining us on the podcast.

Michael: Thank you very much. It’s definitely a pleasure to be here.

Ritu: Michael, would you like to tell us a little more about Mount San Rafael Hospital so our listeners have some context?

Michael: Mount San Rafael Hospital is located in Trinidad, Colorado. Las Animas County is actually the largest county in the state of Colorado, with a population of about nine thousand in the city and around fourteen thousand across the county as a whole. I’ve always had a passion for rural healthcare, and I’ve always stated that your zip code should never determine your healthcare outcomes — that is such a critical element. Here at Mount San Rafael, we believe that whether a patient comes to us, to Denver Health, or to UC Health, they should receive the same level of care. We focus heavily on innovation and technology enhancements because in rural America, unfortunately, zip code does still determine outcomes. But we’ve been fortunate to bring a lot of innovation to what we do, and we’re proud to be a nationally recognized rural healthcare hospital within the state of Colorado.

Ritu: You’ve described your Oracle Health implementation as much more than an EHR replacement, and you recently shared the principle of “follow the patient journey, not the org chart” — a really powerful leadership philosophy. Tell us more about this project, and what advice would you give other CIOs embarking on similar modernization efforts?

Michael: I’ve always said that if you work in healthcare, your new CEO is the patient — that’s the bottom line. As a technology and business leader, the goal is to build tools that benefit our patients both inside and outside the organization. Patient care is number one. We have to look at initiatives that enhance patient care outcomes, improve the way we share information, and give clinicians a complete view of the patient’s health — along with the right toolsets to drive better outcomes. AI, used as an enhancement tool, can create better outcomes, better automation, better operations, and better clinical documentation. We put specific AI algorithms into our radiology practice, and I can literally say those have been life-saving. When an organization is considering replacing a system or investing in something new, you have to ask: what’s the why? Is it better operations? Better patient care? Every digital investment we’ve made has had a patient-centric strategy attached to it, because that’s how critical it is. Hospitals and clinics are digital companies that deliver healthcare services — we have to move away from the brick-and-mortar theology healthcare has been built on. Our strategic partnership with Oracle Health allows us to move into the next era, utilizing AI-native technology built directly into the process itself, from the EHR to the patient portal. That is our mission moving forward.

Ritu: We had Dr. Bharat Sutariya on the podcast from Oracle Health, and he also talked about being AI-native and how that’s going to be a real game changer — having everything built in rather than increasing clicks. I think you’ve leapfrogged a lot of health systems by moving ahead with this implementation.

Michael: A hundred percent. This move to Oracle Health was a strategic investment in the future of this organization. By integrating true clinical AI with a modern, unified EHR system, we’re giving our caregivers technology that removes barriers. How do we remove those consistent clicks? How do we let providers be providers again, instead of being attached to a workstation? Incorporating ambient AI removes those barriers, enhances clinical documentation, and reduces burnout. Providers are constantly seeing patients and sometimes fall behind on documentation — doing it after hours or at home. We have to show we’re investing in technology to create better automation, better efficiencies, and reduced administrative strain, while strengthening the connection between physicians and patients. This positions our facility to deliver a high-performing, more connected, and more sustainable model of care for the region.

Ritu: With AI becoming embedded across clinical workflows, you talk a lot about cybersecurity as fundamentally a patient safety issue rather than just an IT responsibility. How do CIOs need to approach cyber resilience as an essential component of clinical quality rather than just operational risk?

Michael: Cybersecurity has to be — and I consistently repeat this — a core component of the overall organizational strategy, period. I was looking at some data points from a national survey sent to many health systems, and only about 43% of healthcare organizations provide basic cybersecurity awareness training to their end users. We have a major gap where organizations aren’t focusing enough on security, and healthcare has gone from being in the top ten to the most attacked industry. When I go to my board of directors, I focus on three elements — what I call FOR: Financial, Operational, and Reputational. If a cybersecurity breach happens, what are the financial consequences, the operational consequences, and the reputational consequences? Framing it that way tells the story of why we continue to invest in security. Deep fake technology, especially with AI, is becoming more advanced than ever — voice clips, video clips designed to look legitimate. You’re on a Zoom call and you genuinely don’t know if you’re speaking with a real person or a deep fake created by a threat actor trying to compromise your organization. Social engineering is equally critical. We did something a bit out of the norm: beyond penetration testing, policies, and procedures, we ran a social engineering exercise where an individual with a badge similar to ours tried to access the facility to see how much access they could gain across different departments. It opened everyone’s eyes — attentiveness and vigilance are extremely important, and cybersecurity can be physical. Showing those results, the video clips of the individual trying to gain access to the garage, different units, flashing her badge as she moved through — those were powerful learning moments. We can have the best policies and procedures in place, but if we’re not actively testing them, we won’t be successful. Consistent testing and validation of your cybersecurity program is critical. We’ve focused heavily on deepfake and phishing awareness — phishing emails are getting more advanced and harder to detect because threat actors are using AI to create them. That’s why I’ve always said: we have to fight AI with AI. We focus on EDR, SIEM, SOC, identity management, two-factor authentication, and micro-segmentation. And if we do not invest in our human firewall, we will not have a successful outcome, because your end users are either letting threat actors in or keeping them out. Building that human firewall is one of the most important things we do.

Ritu: Thank you, Michael — that was really comprehensive. Rohit, would you like to ask a question?

Rohit: Thank you, Ritu. Michael, I was thinking about what you said in relation to the use of generative AI tools like ChatGPT by employees. We know everyone is embracing these tools in their personal lives and bringing that into the business environment, where they might inadvertently expose proprietary information. What are your thoughts on how generative AI is being embraced organization-wide, and what AI governance initiatives do you have in place?

Michael: Generative AI sites like Claude and ChatGPT are currently blocked within our entire environment. There have been breach issues with ChatGPT where individuals put in proprietary information — if an end user doesn’t configure the session correctly in OpenAI, the algorithm absorbs that information, and theoretically it could be leaked, creating a massive breach. So we’ve blocked all those tools. Right now we’re investing more in Microsoft Copilot, which is being rolled out across the organization, integrated into our Office 365 environment and customized to how we operate. Free-range use of general generative AI tools could create a major security issue. Copilot is the approved application with all the appropriate security processes in place — all other generative AI tools are completely blacklisted.

Rohit: So people have access to Microsoft Copilot for regular use?

Michael: That is correct — it’s a designated, approved application with all the security processes we’ve developed in place. All other generative AI tools remain blacklisted.

Rohit: How are you seeing adoption of Microsoft Copilot across departments?

Michael: There are a lot of great use cases. Departments are using it for finance validation, working directly in spreadsheets, developing reports and breaking them down into more readable formats, generating ideas, enhancing policies and procedures, and validating information. Departments are customizing how it fits their specific workflows, how to accelerate their work, and how to make things more efficient. It has been a really good tool for the organization.

Rohit: One more question — what are some of the innovation initiatives or processes you follow, and how do you foster innovation within the organization?

Michael: One of the biggest initiatives we’re focused on right now is the RHTP program — the Rural Hospital Transformational Program. It’s about $50 billion over five years, each state applies separately, and the State of Colorado received approximately $160 million. Critical access organizations are going to apply for this, and it’s really going to transform how rural health delivers healthcare services. This is where you’ll see a lot more innovation coming out of rural America — healthcare has gotten behind the curve on digital transformation, and in rural America the pace has been even slower. These funds will allow organizations to move in the right direction if they strategically align their projects with the application process. For us, a big driver is focusing more on remote patient monitoring and in-home hospital initiatives. We have many remote patients who have difficulty coming in and tend to wait until things get worse before seeing a provider, rather than having their health consistently monitored. As I said from the start, your zip code should never determine your healthcare outcomes — but in rural America, people statistically live shorter lives than those in metro areas because it’s very hard to access the same level of services. Our model has been: how do we focus, strategize, innovate, and bring technology that actually matters to the organization? We’re a CMS five-star hospital — one of only two rural facilities in Colorado to receive that designation. We’ve been a beta site and an innovator, doing more with less. We’re incorporating AI to help providers select appropriate CPT codes based on their completed documentation — before AI, providers were essentially acting as billers and coders, which took them away from being doctors. Reducing those barriers, automating the right things, and getting to a place where your zip code truly does not determine your outcomes is the philosophy and the direction we are moving in. The acceleration of digital transformation is definitely here.

Ritu: Totally agree. One final question: CIOs are now expected to oversee not just traditional IT, but AI, cybersecurity, cloud strategy, and digital experience. How do you see the role of the healthcare CIO evolving over the next couple of years, and which leadership capabilities will become more important than technical expertise?

Michael: I am a technology executive, but we have to be business leaders first. We have to understand the organization as a whole — not just from a technology standpoint, but as a complete operational entity. The CIO is increasingly focused on creating automation that enhances revenue cycle initiatives, and on moving away from the old stereotype of IT as purely a cost center versus a strategic revenue contributor to the organization. The evolution is about focusing on better patient care outcomes, recruitment, reducing burnout, business continuity, and operational excellence. Everything we do now has a technology piece associated with it. But the CIOs who will really make a difference are those using technology for better outcomes — technology that truly matters, technology with a patient-centric initiative attached. Those are the game changers and the innovators moving forward as the CIO role evolves.

Ritu: Thank you for that answer, Michael. That brings us to the end of the podcast. Thank you so much for being our guest today.

Michael: Thank you very much. You both do an amazing job.

Rohit: Thank you, Michael.

—

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

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

Rohit: Hi, Alex. I’m Rohit Mahajan, co-host of The Big Unlock Podcast and CEO and managing partner at Damo Consulting. It is great to have you as our guest, especially at this fantastic event, AI4. I would love you to give your introduction to our listeners.

Alex: Thank you very much for hosting me — I’m a big fan of the podcast and when we connected I immediately decided we had to do this. I’m Alex Zhavoronkov, founder and co-CEO of Insilico Medicine. We are a generative AI company that is clinical stage, meaning we actually have real drugs in clinical trials. We are publicly traded — we went public in Hong Kong last year under the ticker symbol 3696, so I need to be careful about forward-looking statements. My background is in computer science, and I started my career in GPU computing. I made some money in my early twenties and decided to spend the rest of my life on aging research, because I believe that’s the most important problem everyone has — it is the major cause and source of suffering in the world. I did my graduate work at Johns Hopkins, was a professor here and there, and in 2014 came back to my roots in GPU computing and started Insilico at the NVIDIA GTC conference. We had a presentation called “Can NVIDIA Solve Aging?” Fast-forward twelve years, we’ve managed to get a few drugs into human clinical trials, and I’ve raised a considerable amount of funding — though it wasn’t easy in the beginning. There were times when I had to sell everything and put it into the company. When you’re rich you invest everything, then you’re poor, then you get it back — and then you don’t want to be rich, so you invest even more. It’s an interesting journey. I’m still far from solving aging, but we’ve made some good progress.

Rohit: That is a very interesting area. Everyone talks about the silver tsunami — 10,000 Americans aging every day, going into senior care or rehabilitation facilities. When you say you’re working on longevity, there is health span and there is lifespan. Tell us how you think about that, which drugs are in the pipeline, and how you chose them.

Alex: I actually focus on lifespan — for me that is more important than health span, and I’ll explain why. Health span is a very vogue definition of longevity, basically used by people who don’t want to push the boundaries of actual lifespan. I don’t know of any drug that can give you ten years of life without also giving you additional years of healthy life. If your goal is to push that boundary, you’re also going to increase health span no matter what — the question is just the size of the delta. If that delta is six months of suffering, that’s not something we’d want to focus our entire company on. That said, we still develop drugs that may help in certain conditions, because very often diseases are diagnosed too late. I would still give six months to a mother of two suffering from terminal cancer to have more time with her children — especially if it doesn’t cost much, and it has to be her choice. But we need to provide people with the freedom to live even six months longer. People who say they don’t want lifespan, only health span, are actually doing damage to the field, because we want lifespan too. And if you want to be truly ambitious, think about peak span — how long you spend within ten percent of your maximum performance. All of those spans matter and there shouldn’t be a debate about which one to extend. Life is a fundamental human right. If we can give one quality-adjusted life year to everybody on the planet, that is 8.3 billion life years — 110 million lifetimes. A couple of years would be 220 million lifetimes, which is more than were lost in all the wars fought in the history of humanity. The way to get there is to work within the traditional pharmaceutical drug discovery and development paradigm — going after a disease, getting a drug approved — but if the root of that drug was actually aging research, you’re developing a longevity therapeutic with AI. The AI tells you this is an anti-aging drug but it should work on this cancer, so you approve it for the cancer. The patient gets additional months or years. And then once the drug is approved and safety and efficacy are established, you can experiment with it, gather real-world data, and see if it also improves aging biomarkers — potentially beneficial for others at a different dose or mechanism of administration. That’s the idea.

Rohit: I heard you say longevity and cancer. Are most of the drugs in your pipeline targeting different cancers, or something else as well? What’s the breadth and depth of what you’re going after?

Alex: We are very different in the context of AI drug discovery. Most companies go after cancer or neuroscience. We are the MMA fighter — we decided to go very broad spectrum. Our core ideology is that we go after aging. Many of the protein targets that drive disease are implicated in both aging and cancer at the same time, just used differently. For aging, for example, you want to eliminate senescent cells — cells that have stopped functioning and are just sitting there excreting toxins into the microenvironment. Some cancer drugs do exactly that. About half of my pipeline is related to cancer and half to chronic disease. My lead program, currently in phase three, targets idiopathic pulmonary fibrosis — a chronic lung disease with no good treatment at this point, where everything available just slows the decline rather than reversing it. In our phase two trial we demonstrated very promising reversal of the loss of forced vital capacity, the essential measure of lung function. We also go after IBD, inflammatory bowel disease, neuroinflammation including Parkinson’s and potentially Alzheimer’s, ocular diseases including dry AMD and uveitis, and the most exciting recent breakthrough is in pain. Pain is very difficult — identifying a new mechanism that isn’t opioid-based or an ion channel or anti-inflammatory is extremely hard. Most pharmaceutical companies historically started as painkiller companies, and that’s how we got heroin, morphine, and fentanyl. We identified a new target originally purposed for aging, and through sheer scale of experimentation — while testing a pain drug on animals, we had the ability to put more compounds in the assay. In one experiment we rescued an animal in terrible pain with something that might not have worked, and it worked better than morphine and epidural. We tested it against every standard of care, even against NAV 1.8 orally, and it performed significantly better. We took it all the way to a development candidate — one step before human clinical trials — and it went great. We now have 32 development candidates, eight phase ones, three phase twos, and one phase three. The pain drug alone is extremely exciting, because imagine if at high dose it kills pain and at low dose it addresses aging.

Rohit: That’s a great combination. We’re at an AI conference, so tell us more about how you’ve combined the different AI approaches. AI was once predictive analytics, now it’s generative — and you’ve been finding targets and small molecules for drug discovery for a long time. What’s novel about how you’re approaching AI?

Alex: Insilico originally started as a deep learning company and closely followed DeepMind — when we hired people, we tried to do hackathons where we’d take one of their papers and hire people who could perform similarly. We started as a biology company with an AI biologist focused mostly on understanding how you live from birth to death using different biological data types. Those were originally predictive systems. In 2016 we started publishing on generative adversarial networks — early days of generative AI — and we also purposed those algorithms for chemistry for the first time. My first paper on GANs for multi-parameter optimization and molecular generation came out in 2016: instead of searching for a needle in a haystack, you generate perfect needles — molecules with desired properties. In 2017 we published papers showing experimental validation of the technology: we actually synthesized and tested the molecules. In 2018 we published in Nature Biotechnology on generative tensorial reinforcement learning — right before ChatGPT — showing we could synthesize and test molecules all the way into mice in 46 days. That was a big deal. From the early 2020s we started working on transformers and diffusion models, but essentially built a Lego system of different models that can do biology and chemistry. Some do generation, some do synthetic data generation — because in biology you often don’t have much public data for specific problems, so you can actually generate high-quality data using generative approaches. Some models do predictive analytics, some predict clinical trial outcomes. We orchestrate all of them using frontier models. We now have over 1,200 tasks in drug discovery — think of 1,200 experts you can clone and spawn into many different agents to achieve the grand task of reasoning across an entire program from target identification to approval, working backwards. Essentially: from prompt to drug.

Rohit: “Prompt to drug” — that’s a great phrase. I’ve been following this space and I think our listeners would also ask: how do you position yourself relative to Flagship Pioneering, Moderna, or other companies doing pioneering work in this space?

Alex: Flagship Pioneering is a great platform for company creation. What they do is essentially have capital, a pool of experts, and they identify trends — for each trend they build a company, offer it to investors, raise funding, and utilize the same people incubating many companies. In generative AI they have Memong Mini, incubated by the same team for different trends and investor bases. I’m not entirely sure how to establish the success of those companies. One called Generate Biomedicines is listed publicly and has a drug in phase three, though it would be nice to see peer-reviewed publication of how much the generative approach actually contributed. Others I’m less certain about, because when you create a company for a purpose it’s very difficult to get people to work together toward a specific goal. Companies that tend to be more successful in our field are those where people came together, worked on a problem, solved it, and only then raised money to scale. Forced invention is very difficult — genuine invention works better. That said, Flagship is a great platform, and one win like Moderna pays for the entire party. Moderna is actually a Flagship company, which is why they’re famous. And if it hadn’t been for COVID, it would be an open question where Moderna would be now. But the companies I’m actually more concerned about are Anthropic, OpenAI, Google, and many Chinese players like Tencent and Alibaba. They are developing foundation models that can reason really well in the context of biology. Some of the more primitive tasks — like target discovery — are already completely demonetized. More complex tasks are being demonetized as we speak. All of those companies are also putting resources into biology. My hope is they don’t make the mistake of buying low-quality companies just for the data or for kudos — those are computer scientists who don’t know what works in biology. But once they get proficient in our field, they will be real competitors. What I’ve started doing is developing tools that actually accelerate this transition — tools like MMI Gym that help frontier models train on what we do. If in some tasks they are advancing and can be better than me, I’d rather help them do that, because they can help us back. I’m going to be always at the frontier with a new algorithm or approach. In the future, the real moat in my field are the drugs — they are like diamonds, they’re forever. AI comes and goes every six months. The future moats will be capital — which you can convert into energy or compute — infrastructure including labs, robotics, and networks of contract research organizations, and third: good intent, good ideas, and being in the right place at the right time. We have more ideas than we have capital or infrastructure, but with increased intelligence we can more rapidly convert ideas into real products that save lives.

Rohit: That’s a great insight. What are some of your biggest challenges at the point in the journey where you are now?

Alex: One fundamental challenge that everybody faces is the complexity of biology. You need to try a lot of things to see what works, even with perfect AI. I can now go from prompt to drug for a given target — if it’s low or moderate novelty, I’ll win. But if I’m going into truly new terrain, there’s a very good chance I’ll fail. You need a very sustainable business model to allow yourself to fail. The real great challenge I see is geopolitics. Right now it’s absolute nonsense. I don’t understand it and don’t want to understand it, but I have to. The US is fighting with China, countries have become very nationalistic. To do really good work in our field, you need international reach. If you want to synthesize molecules at scale, there are only two places you can do that — India and China. You can’t do it in the US; the infrastructure simply doesn’t exist, and it’s also a low-value task. Some animal experiments, like primate studies, can’t be done at scale in India either. Most of the hardcore competition is in China, so if you want to compete you actually want to compete there. The US is making it difficult for American companies to do that, and there are regulatory complications everywhere. My job isn’t to pick any side — I don’t care where you live, as long as you can live longer. If a mother of two is dying somewhere in Africa or in China, you need to help her. We’ve had to establish infrastructure that allows us to be global — we’re in Montreal, Abu Dhabi, Hong Kong, Taipei, Shanghai, and Yixing. Right now it’s just very difficult to operate globally, and that difficulty is a real challenge for what we’re trying to do.

Rohit: Any upcoming announcements or plans you’d like to share, including any plans to list in the US?

Alex: We would of course love to explore additional capital markets and are constantly on the lookout, timing the markets carefully. The US biotech industry is going back up but it’s still in a winter — it hasn’t fully processed the excess from companies that listed and raised a lot of capital in the early pandemic days, didn’t deliver, and lost investor trust. The AI hype also needs to settle somewhat, because right now people are chasing trillion-dollar companies and forgetting about smaller biotech, even though it’s very important. What we have on the horizon are massive scientific breakthroughs we’re constantly working on — but as a publicly traded company I can’t talk about them specifically, so watch for peer-reviewed publications. We usually don’t make big claims until we publish. What excites me most are the clinical trials. Once you’re in the clinic, you’re worried all the time — with many programs running, you must fail statistically at some point. So far we haven’t, but given our current rate of success I think in many cases we should succeed, and when we do it pays for everything. On the AI front, our most important initiative is benchmarking — we just released a set of benchmarks where we can test frontier models and specialist models across over 1,000 drug discovery tasks. Many of them perform poorly; some are reasonable. The large foundation model developers don’t even know drug discovery yet. I’m very happy to see that Anthropic is actually going into their own drug discovery — you need to discover a drug to know how to discover a drug; it’s the chicken and the egg. The most exciting thing for me remains aging research. I don’t think there are greater enemies that humans have other than aging — it will kill you with one hundred percent certainty and takes everything away. There’s a good chance we can give everyone an additional ten or even twenty years. We have drugs in development that hopefully will get us there. That’s what will consume a large part of my life, and I’m willing to fight for it.

Rohit: That’s beautiful — pushing the envelope on longevity. Thank you so much, Alex. Really appreciate you being our guest on The Big Unlock Podcast.

Alex: Great to be on the podcast. Let’s unlock longevity.

Rohit: Yes. Thank you.

—

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

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

Ritu: Hello, listeners. Welcome to Season Seven of The Big Unlock Podcast. My name is Ritu Oberoi, and I’m managing partner at Damo Consulting and your host today along with Rohit. Today we are really excited to welcome Zaw Thet to our podcast. Zaw is the co-founder and CEO of Exer AI, a digital health company using computer vision and AI to assess, coach, and improve human movement. Zaw brings an unusual combination of experience as a serial entrepreneur, investor, and operator, having previously been a founding partner at Signia Venture and CEO and co-founder of ForInfo. His current work with Exer sits at the intersection of musculoskeletal health, physical therapy, sports performance, and AI-powered care delivery. Welcome, Zaw — really excited to have you here today.

Zaw: Thank you so much for having me. When you say all of those things out loud, it makes me blush that I’ve been doing this now for twenty-five to twenty-seven-plus years. It’s a pleasure to be here.

Rohit: Thank you, Ritu. I’m Rohit Mahajan, CEO and co-host of the Big Unlock Podcast. Welcome, Zaw, to the podcast. We really appreciate the friendship and are looking forward to an engaging discussion.

Ritu: Great. Zaw, tell us about Exer, which is built on the idea that movement itself can become measurable through ordinary cameras — without wearables or any extra hardware. What does that unlock in healthcare that we previously just couldn’t do when movement assessment depended mainly on in-person observation or wearing devices?

Zaw: Thank you again for having me. To explain why human motion matters, I need to rewind to about eight years ago when we decided to start the company. As an investor looking across multiple digital health disciplines and companies, I was considering the different potential applications for AI — this was before ChatGPT, Claude, or any of the current LLMs and large models were out there. What we were really looking at was discrete sets of data, because I believe all AI is based on the moat around the data it has been trained on. What became very evident as we talked to people across multiple disciplines was that there was a ton of data in the chart. All the companies looking at EKGs or blood panels were essentially training on the same types of data. There was a lot of early AI starting to emerge around anything related to the back office of health system operations — revenue cycle management, administration, prior authorization. There was also a lot around digital scribing, whether ambient scribes listening from the room or simple dictation tools like Nuance. But what became clear was that in all the imaging being done — X-rays, CT scans, MRIs — actually seeing dynamic human motion was something that really didn’t exist. It existed in gait labs with multi-million-dollar cameras and sensor rigs, but deploying that on the edge or in clinical settings for a mass amount of patients wasn’t possible. What we heard over and over again was that even though the data didn’t exist yet and people didn’t fully know what it would mean, it would be really helpful to capture human motion in a clinical environment, at a patient’s home, or over telehealth, and understand what that means for different diseases. That’s the basis on which Exer was formed — with the understanding that a lot of research would be needed to validate all the different algorithms for assessments and phenotyping across different disease types. That’s how we got started, and it has obviously evolved a lot since then.

Ritu: You’re not just building another healthcare app — you actually want to turn the camera into a scalable clinical and coaching tool for movement health. How does real-time AI coaching change patient engagement and outcomes compared with traditional home exercise programs or simply telling patients what to do?

Zaw: A lot of people in the early days mistook us for a PT company because many of the assessments are around fundamental human motions — a hinge, a squat, a pull, a gait walk. But no one had really thought about how to apply that to clinical settings as a whole. When we first started, the feedback we got from people was: if you want to make this work inside of very large health systems — and we’re now inside twenty-five of the top fifty health systems in the US — how do you deliver a platform, not a single-point solution? A platform meaning it doesn’t just apply to one department but applies to orthopedics, neurology and neurosurgery, primary care, PM&R, pain and spine, and so on. The second was: how do you actually deliver on the three legs of the stool? How do you deliver demonstrably better patient outcomes, better clinical efficiency — actually taking time that would normally be spent by a clinician, nurse, PA, or MA and making that more effective so they can scale across more patients — and potentially increase new revenue streams for a hospital where they may not currently be capturing that care in-house? Those were the two primary drivers. The other requirements were par for the course: regulatory approval as a Class 2 medical device, SOC 2 and HIPAA compliance, and clinical validation through peer-reviewed publications. The way we approached it was to build two different parts of AI. One is the neural net that allows us to see the human body without a gait lab — just using a phone or iPad. The other is delivery across multiple points of care, primarily in-clinic in outpatient settings, with telehealth and at-home options as well. Then we built a research arm across multiple disciplines. I’ll give you a quick example. Total knee surgery is probably the most common orthopedic surgery in the US, with over a million done every year. The current standard of care: you visit your orthopedic surgeon, they assess your knee, determine you’re a candidate, schedule surgery for three months out, and send you for a long leg X-ray — a static image taken while you’re standing straight with your kneecaps together. That image is used to measure the angle from hip to knee to ankle, but it doesn’t capture how you actually walk — whether you have a bit of varus or valgus. None of that is captured by an X-ray today, even though research has shown it’s not a great predictor of surgical outcomes. Instead, imagine having an iPad in the clinic room. When the patient agrees to total knee surgery, a medical assistant can assess their gait in under twenty seconds and understand their circumduction patterns and how the knee loads at maximum weight-bearing — all of that populated directly into Epic or whatever the EMR is before the doctor even comes into the room. That assessment is repeated pre-surgery, checked immediately post-operatively to confirm alignment with the angles the surgeon intended, and tracked again three months later as ligaments relax. That’s just one example in orthopedics of how this is already being deployed today.

Ritu: Wow, that’s an amazing example. A static picture really can’t tell you that much compared to seeing movement on an iPad. How does the telehealth part work — if the patient is at home and you’re doing a remote appointment, are you just recording them on a normal browser and still getting all the information?

Zaw: Telehealth and at-home work typically over a web browser — the easiest approach. It’s done inside any modern browser, Chrome, Safari, and so on, using the same basic tools. It’s a little different because the clinician is on the telehealth call with the patient, and it can be a bit harder for the patient to set up in their home environment. But especially for anything upper body, we can capture it well. One of the big telehealth use cases is in neurology. We can track 22 discrete points on the hand, which makes this very powerful for orthopedic and post-operative visits — especially for destination hospitals like Mayo Clinic where patients have flown in and don’t want to travel back just for a follow-up. We can track where they are on their recovery curve: whether the hand is achieving full range of motion is very difficult to assess even on physical exam using a goniometer for each finger joint. Our platform allows that to be done instantaneously. Another great example is tremors on the neurology side. Because we can see the full body, we can track how a medication is affecting a tremor as a patient is titrating different drugs, as well as other balance and ataxia measures. That’s how the telehealth piece works, and there’s also an at-home option that works over a browser or as an app on the patient’s phone.

Rohit: How did you get started in this space, and are there any prior ventures in healthcare you’d like to share with the audience?

Zaw: I don’t come from a traditional healthcare background — I come from a software startup and tech background, though I do come from a family of physicians; both of my parents are physicians. Like every good first-generation immigrant, I was supposed to become a doctor, an engineer, or maybe a lawyer as a third option — and I chose a different path. When I went out to Stanford in the late ’90s during the first dot-com boom, in the early days of eBay and before Google even existed, I saw an opportunity. I’d always been interested in technology, and what I found was my true calling: solving big problems in society using technology, where you can scale the impact to millions of people. When I first considered medicine, I loved the mission and purpose and the ability to help people — that has always driven me. But I wanted to deliver that at scale. The problem with a one-to-one ratio between time spent with someone and help given is that you can only help that one person. My superpower, I guess, was the ability to build big platforms where instead of helping one person I could hopefully help a thousand. That’s the big vision we had when we got into healthcare — I saw a lot of inefficiencies and maybe I was a little too naive, thinking this is a big problem that needs fixing. I didn’t realize how complicated it was eight years ago. I joke that I still don’t have a full healthcare license — more like a green card — and I’m learning every single day. But what I found was that the ability to deliver precision medicine at scale is especially critical given the severe shortages of doctors, nurses, and PAs alongside an ever-aging population that only increases demand on the healthcare system. Those two things are fundamentally broken and won’t fix each other. Yet somehow healthcare still works — survival rates for cancer and cardiac events in the US keep going up. The medicine is getting better and we are helping people, but it’s a broken system. My hope was that Exer could be one component of fixing it — allowing clinicians and providers to deliver better medicine at scale, working with them and not against them. That was a core tenet from early on.

Ritu: Exer has so many applications across physical therapy, senior care, orthopedics, and sports. So far, which market has been the strongest, and which do you think becomes the biggest long-term opportunity?

Zaw: People always asked us this, especially in the early days. When we had our first demo running in 2018, no one had ever seen anything like it, and everyone wanted a better golf swing — or tennis serve, baseball or lacrosse. We looked at all of those early on and said we could build a consumer company doing very specific sports applications, or we could start where the science is really hard. If we can prove we can do this for the top doctors in the US, and that’s where the research originates, then we have the opportunity at a later point to go anywhere we want. Think of Amazon starting by just selling books — that’s sort of the first inning we think we’re still in today. We focused on healthcare and on delivering a platform to health systems and providers rather than selling directly to consumers. Maybe that was the harder route, but it also meant we didn’t have to spend enormous amounts on marketing or raise hundreds of millions of dollars, and it let us keep the team lean — because healthcare just takes a long time regardless of how much money you throw at it. Our secret advantage is that we now have four-plus years and hundreds of thousands of patients who have run through IRBs and clinical studies, giving us a very proprietary data set that no one else has — where clinicians and researchers are using Exer to record not the video but just the body points as they move, tied back to all the clinical data from the chart. We understand what a gait shuffle looks like: does it mean arthritis, stenosis, or Parkinson’s? To do all of that and also do golf would have been very difficult. We’ve stayed in our lane, and we’re going to be there for a while because there’s still a lot to do in healthcare before we get to the, quote-unquote, fun stuff.

Ritu: This leads nicely into the next question. We’re talking about continuous functional monitoring, and wearables are having a big moment. We’ve talked to clinicians and doctors about how outdated the annual physical is. How do you see this tying into more continuous monitoring of patients and catching problems earlier?

Zaw: I think it ties in really well. We’ve been fans of working and partnering with companies that are more device-specific — consumer companies like Whoop or Oura Ring, medical-grade companies like SensorBio, and data coming off a watch like a Garmin or Apple Watch. There is a lot of room in this space for continuous monitoring. It’s already happening at a certain level for patients at home managing diabetes, for example. The challenge on the continuous monitoring side is filtering through all the noise — there are a lot of signals potentially coming from these devices, and knowing what matters most is the hard problem. That will get solved, but probably not by a company like us at Exer. Where Exer comes in is that a big component of that annual physical is a movement screen — especially for a senior population 65 and over, where you’re looking for issues and particularly fall risk, which is related to gait and balance. Falls are the number one cause of death and the number one cost to our healthcare system for people over 65. If we can prevent that, if we can get ahead of that curve — instead of checking someone every 12 months, doing a movement screen once a month — all of their wearable data is being pumped in at the same time. Low blood pressure that may cause fainting comes from the wearable; arthritis in the right knee that could cause a fall comes from us. That is definitely the future of medicine, and it will get there over time.

Rohit: The AI landscape is changing so fast. Could you give us some thoughts on how to keep up, and how you’re thinking about incorporating new developments into your offering for clients?

Zaw: It’s moving at a phenomenal pace. When we first started, we were really one of the first true AI companies in healthcare. Because of the research and time it takes to be clinical-grade and deployed inside health systems, we’ve had to be very patient. Now you’re seeing algorithms coming out left and right — some just based on large LLM models, some based on real data. You have companies like Open Evidence helping physicians find information faster. It is a brave new world and very hard to keep track of. I get the email digests from Silicon Valley and look at the top developments. For patients, most of these things are happening without them seeing it. There are starting to be some patient-facing applications that are genuinely interesting — like scribing your own doctor visits, then feeding that recording into an LLM to better understand what was discussed. It’s probably better than playing Google Doctor. But for the most part, most of the AI patients see in healthcare is behind the scenes. Exer is actually one of the exceptions — when you come in for a visit, you’re being assessed, and there’s a playback feature that’s one of the most-used things after an assessment: the clinician pulls it up, goes through it with the patient. It looks like a skeleton moving, so the patient can see what they’re doing without any actual video. Some patients even like that — it removes concerns about how their hair looked or what they were wearing. As a business owner using these tools all day, they’re phenomenal for making a business punch above its weight. A ton of our customer support is now handled by AI agents. Our developers are at least two to three times more productive because a lot of junior developer tasks — QA, code checks — are all being done by AI. There are countless ways to incorporate AI into your business, and it will only keep evolving. We tend to find one we like, stick with it, and try not to switch as often as possible.

Ritu: We’re almost at the end, Zaw. Any closing thoughts or crystal ball predictions for the next year?

Zaw: I’m a big optimist — I wouldn’t be doing this if I didn’t believe we can change the world for the better. That said, I do think there’s some potential backlash coming that we need to be aware of, especially in healthcare around the use of AI. I’m already hearing early pushback from systems, particularly around generative AI where the AI is generating content and having a patient interaction without any human in the loop at all. The capability is really good, but a small percentage of the time it halluccinates or sends inappropriate information, and we’re starting to see the first lawsuits around that. I don’t think that means it will go away, but for anything without a human in the loop there’s going to be a period of pushback about whether this can scale directly to consumers and who bears liability when it sends something incorrect. A lot of that will get sorted out in the next couple of years. At Exer, thankfully, there’s always a human in the loop because we provide support to clinicians, not replace them. In the end, I think we’re obviously going to end up with a much better and more efficient system. That’s my crystal ball.

Ritu: You raised very good points. The ability of generative AI to hallucinate leads to real ethical questions — the moment you remove the human from the loop and cede that control, it needs to be fail-proof, which it isn’t yet.

Zaw: We’re going to have agents watching agents watching agents, and then maybe a human to check at the end. Who will guard the guardians — that’s right. Well, thank you both for having me. I really appreciate it.

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

—

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

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

Ritu: Hello, listeners. Welcome to Season Seven of the Big Unlock Podcast. My name is Ritu Oberoi, and I’m managing partner at Damo Consulting and your host today. We are looking forward to a conversation with Dr. Angela Shippy, and we warmly welcome her to the podcast. Dr. Shippy has been a director and senior physician executive and clinical innovation lead at Amazon Web Services for over five years. She leads executive engagement for healthcare and life sciences. Her work focuses on bridging operations and technology to enable and accelerate transformation for providers, payers, pharma, biotech, health tech, and device organizations. We’re really looking forward to a very engaging conversation with Dr. Shippy, so welcome to the podcast once again.

Angela: Thank you, Ritu. It’s great to be here, and thank you for having me.

Ritu: Dr. Shippy, would you like to add anything to that introduction?

Angela: I think the one thing I would add is that people often ask how I ended up at Amazon Web Services. Truly, it was because I have been an end user of technology my entire career. I started my residency a few days early because we were all mandated to use a very early version of CPOE — computerized physician order entry. All along the way I’ve continued to use technology, whether it was being a physician champion for PalmPilot, electronic documentation, or putting together templates with the informatics team. It’s always been part of my career. And like so many of my colleagues, the COVID pandemic was really an opportunity to bring data together. For the first time ever in my previous organization, we brought together clinical and operational data in a dashboard that we were using on a regular basis. Seeing in real time how having all the right data in the right place allowed us to treat a patient population that was in need — and know exactly what they needed — really spurred me to join this team and help my colleagues across the country, and across the world, be able to do this.

Ritu: Thank you for sharing that. We would love to hear your origin story — how did you decide to become a physician?

Angela: It’s a funny story if you think about a nine-year-old girl who had a teacher who told her she was really good at science. My fourth grade teacher said, “You’re really smart and you’re really good at science.” I translated that to mean that if you’re really good at science and you’re smart, you should be a doctor. And then I decided that if I was going to be a doctor, I should be a neurosurgeon because the brain is the hardest thing and the most important part of the body. For many years through elementary school, I was focused on becoming a doctor and a neurosurgeon — I used the term brain surgeon at the time. In high school and college I shifted to thinking I would love to be an OB-GYN and deliver babies. When I got to medical school, it was my very first rotation, and I loved it — a six-week rotation starting in labor and delivery, so much fun. Then I got to the surgical part: bladder repairs, hysterectomies, and oncologic patients, and I realized that wasn’t the part I was excited about. My next rotation was internal medicine, and I loved it. Internal medicine is the gateway to so many specialties, and I ended up as an internal medicine physician, practicing as a hospitalist for many years.

Ritu: Thank you for sharing that story, and a big shout-out to any teachers out there who are listening — those words set you on this path. So Dr. Shippy, you bring together the perspective of a practicing physician, a former health system executive, and a technology leader to help organizations think about what’s possible in this new era of cloud computing and generative AI. In your experience working with so many health systems, what separates the organizations that are truly transforming with AI from those that are just trying pilots and deploying isolated use cases?

Angela: Right now so many of our customers are thinking about their biggest pain points — what their clinical operations look like, what throughput looks like, whether they have enough nurses, enough allied health professionals, and what they need across physician specialties. They’re looking at the entire picture, and that’s leading them to ask: what do I want to do with generative AI and agentic AI to take things to the next step? The organizations doing the best work are those asking two things. First, which particular problem am I working backwards from to solve? Second, what am I doing to scale beyond just that initial pilot? I’ll give you an example. UCSD is using one of our purpose-built solutions for healthcare — Amazon Connect Health — allowing them to engage with patients in a meaningfully different way. Being able to verify the patient when they first call the contact center, direct the call to the right part of the organization, use all the data they have related to that patient to direct them appropriately or provide a quick, accurate answer. For them, this has meant 3.2 million contacts with patients, a reduction in hours that call center staff spend on those calls, and a thirty to sixty percent reduction in abandoned calls depending on the department. That translates to patients getting to preventative care, managed care, prescriptions — whatever they need — faster and more accurately, which produces a better patient experience. And we know that patients who have a better experience have better health outcomes. When you see that level of engagement with patients and their ability to access the parts of the healthcare continuum they need, you also know those are patients who continue with that same health system — which addresses the operational efficiency goals as well. The systems really advancing beyond pilots are those focused on what matters most.

Ritu: Thank you for telling us about Amazon Connect Health and how you’re using autonomous agents for patient navigation, appointments, rescheduling, and ambient capabilities. That leads into the next question: AWS has invested heavily in helping organizations build scalable data foundations before deploying AI. But so many executives want to jump straight to the next shiny new AI application. How do you help healthcare leaders resist that temptation and understand they first need to build foundational capabilities?

Angela: One of the things I really enjoy about my work is the opportunity to talk to colleagues across the country about what they’re doing. In those discussions, we talk about the end goal — but keeping in mind that to meet those goals, they first have to think about their data. Do they have clean, available data as their foundation? We know that right now 97% of the data in healthcare does not get used, largely because it’s unstructured and from so many disparate sources. Part of that initial conversation is about how to bring all that together — how to take that disparate data and turn it into real information that becomes insights. Then being able to use agentic AI to take that data and information and turn it into actionable insights, whether that’s for the patient experience, the clinician’s experience, or for research. For a payer organization it can help with member and population management. For a pharma company it can help get to drug discovery faster. That’s what we talk about — the foundation of clean data in the format that allows you to use these agentic tools.

Ritu: You mentioned that you started with technology training even before your residency. Today’s CIOs, CMIOs, and digital leaders need to understand cloud architecture, AI, and cybersecurity — all changing at a rapid pace, sometimes weekly. What is your advice to CIOs and CMIOs on how they stay abreast and relevant when the pace of change is so rapid?

Angela: Our customers share a curiosity about what’s happening from a technology standpoint — they’re standing side by side with us wanting to learn. We have thousands of customers running workloads on AWS, and we’ve earned their trust over time, so they see us as a source of information. We feel a commitment to help them continue to learn in this rapidly changing environment. We do that a few ways. One is through summits — I’m at the DC Summit right now, which gives customers the opportunity to come and learn, hear from their peers, and see what others are doing. We also have training and certification available for leaders and their teams to use — much of it free. We have a fellowship program where up-and-coming leaders from health systems can come through and learn more about technology and, as they advance in their careers, how they’ll think about working with technology and specifically with AWS. We’re also dedicated to upskilling, because every worker within healthcare and life sciences will see their day-to-day job change because of the tools that are available. We want to make sure they have an understanding of that. The reason this is so important for CIOs and CTOs is that we know there’s significant administrative burden across the workforce they’re responsible for, and these tools can help decrease that. When you’re at a summit like this, the energy is remarkable — especially across public-sector customers who are mission-driven, highly regulated, and are really looking to AWS to be the partner that helps them in secure environments, with managed AI layers that allow them to deploy quickly without having to dig into every single aspect of the technology, and with purpose-built services that have guardrails in place for another level of security. All of that is what we’re doing to ensure CIOs and CTOs have knowledgeable teams, that we’re supporting those teams, and that we’re supporting their organizations and goals.

Ritu: So you’re saying this technology can really be an enabler and help them do their job better.

Angela: Absolutely — it’s an enabler and an accelerator. It allows them to start with a proof of concept and then truly scale it across the entire organization. Right now we’re working side by side with customers to help them meet these goals, and we want them to look to AWS and know we have solutions ready for them. I’ll give you another example — Amazon Bio Discovery. This is a new technology that allows our pharma customers to move so much faster. The time it takes to research, take all the data from that research, and get to the discovery of a new drug takes years. With Amazon Bio Discovery, being able to put all the components together and help them get to the answers and insights they need so much faster is going to decrease the time it takes to develop the next drug. That might mean the next drug we need in the oncology space is developed much faster — more lives saved. It’s exciting to think about, and we really look forward to continuing to take this drug discovery work and ask: what can we do next, what can we learn, and what can we iterate and innovate on in this space?

Ritu: Healthcare leaders often talk about moving from data to insights, but increasingly the conversation is shifting toward moving from insights to autonomous action. As cloud platforms evolve to support AI agents and intelligent workflows like Amazon Connect, how do you envision the role of health systems changing over the next few years? Will we still be thinking in terms of software applications, or will patients see seamless, almost invisible technology that just helps them — without passwords, thousands of clicks, and getting stuck at every point?

Angela: I think right now we will see an evolution — technology will become easier to access, and more and more of the processes that are currently very manual and require multiple people and steps will become automated. Our customers will be right there with us as we help make those changes and see what that looks like. That can really change where health systems spend their time and do their work. I’ll give you an example. We announced this week what we’re doing with the Fleming Initiative, specifically around antimicrobial resistance. Every provider organization understands they have infections in their community, some of which are resistant to antimicrobial therapy, and they have whole departments of infection preventionists following surveillance and reporting. We’re now partnering with this initiative — antimicrobial resistance is extremely important. Right now, every eleven seconds someone is being diagnosed with a resistant infection, and every fifteen seconds someone dies from it. Being able to have a global platform connecting 150 countries, bringing together all of this information to minimize resistance — that’s going to change how they practice, how patients receive antimicrobial therapy, and those mortality numbers. When you automate practices that infection preventionists are currently doing perhaps on a spreadsheet, you free up that expertise to be deployed elsewhere, take away administrative burden, and help patients have better outcomes — not being treated with an antimicrobial therapy that isn’t working, or having to have a second course because the first didn’t cure the infection. Those are really significant leaps that are all about technology, automation, and using agentic tools to get to insights faster and change clinical care.

Ritu: That truly scales and multiplies impact to save lives. Thank you for sharing that, Dr. Shippy. I also wanted to ask about the Children’s Health Innovation Awards — I was reading about that and it sounded remarkable. Tell us more about those awards and what you’ve seen with the health systems you’ve worked with.

Angela: The CHIA — Children’s Health Innovation Awards — was an opportunity to say: here we have a population of children with rare diseases and genetic diseases, and children’s hospitals are standalone entities, often associated with academic institutions but smaller in scale than adult hospitals. Putting these awards in place was about helping them bring their information together — accelerating the genomic testing and genomic research they’re doing, accelerating work in rare diseases, and helping them come together on platforms where data from different parts of the children’s disease ecosystem — research institutions, children’s hospitals — could be aggregated. When you take all these smaller populations and aggregate them, you have more data that can get you to insights faster. We wanted to help them get to those insights faster, make an impact on providers taking care of children right now, and also shed light on the importance of rare disease research and genomics and what that can lead to in the future. We have a purpose-built service, HealthOmics, that helps get to insights faster from gene therapy and research.  I’ll share another example. Over five years our health equity initiative helped organizations look at underserved communities, access to care, and ensuring that patients — no matter where they lived, whether in a rural or underserved community — had access to technology. Over five years that was a $90 million commitment helping 600 organizations. Babyscripts, for instance, was able to diagnose pre-eclampsia faster — eclampsia is one of those true medical emergencies in a pregnant patient, so getting to diagnosis faster is huge. We were able to help a company in Uganda take care of at-risk babies for the six weeks after delivery, ensuring they were able to attend their postnatal visit — a 275% increase in postnatal visit completion, and a decrease in mortality as well. These are really significant impacts from funds we’re excited to give and support, either with technology embedded to improve the application or with technical support for applications being built on AWS.

Ritu: I hope listeners learn about the CHIA program and it reaches a bigger audience to help other health systems apply. Thank you for sharing that. As usual, the time has flown by, Dr. Shippy. Any closing thoughts you’d like to share? Where do you think technology is heading? We used to ask about the next three years, then the next year — now it’s almost the next three months because it’s moving so fast.

Angela: One of the most exciting things in our work with customers is watching them start small but think big, never losing sight of where they’re trying to get to. They’re trying to get to better patient experiences, better experiences for their workers, and ensuring that administrative burden is taken away so folks can work at the top of their license. Researchers really able to go fast, accelerate, and get to insights that move them from bench to bedside faster. Nurses and physicians at the bedside really able to interact with patients face to face, eye to eye — not looking down at a keyboard — because of ambient listening. Getting to the discovery of new drugs and new applications of devices faster. It’s really exciting to work side by side with customers as they do that. And the other thing becoming very clear — and our customers are starting to see it too — is that in healthcare, you want to be at the forefront of utilizing this technology. In being at the forefront, understanding that the next big discoveries are most likely going to happen with someone — whether an individual researcher, clinician, or an organization — that either has an agent by their side or agentic technology deployed across the organization. Whether it’s coming from the bottom up from frontline staff or top down from leadership, really embracing this technology and putting it to use to improve the experiences of everyone across the continuum of care is going to make a difference — both in the experience and in the overall health of all the populations being served. That’s really exciting as we look forward.

Ritu: Thank you so much for sharing all those insights with us, Dr. Shippy, and thank you for bringing this transformational change to so many people. It’s exciting to see you doing that. Thank you for being on our podcast today.

Angela: Thank you so much for having me, Ritu. It was great to be here.

—

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

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

Ritu: Hello, listeners. Welcome to Season Seven of the Big Unlock Podcast. My name is Ritu Oberoi, and I’m the managing partner at Damo Consulting and your host today along with Rohit, my co-host. We are really excited to welcome Greg O’Neill to our podcast. Greg is the Director of Patient and Family Health Education at ChristianaCare, one of the nation’s most innovative health systems, based in Wilmington. In this role, he leads the strategic plan for patient education and health literacy initiatives. I’ll ask Rohit for a brief introduction, and then it’s all yours, Greg.

Rohit: Hi everyone. I’m Rohit Mahajan, CEO and managing partner at Damo Consulting, and very excited to have this conversation with you, Greg. Over to you.

Greg: Thank you for that introduction. Just to clarify further — I’m an advanced practice nurse, so everything I do comes through that nursing lens. Nurses really try to connect with patients’ needs, as do other healthcare providers. I’m excited to be here. Thanks for having me, and let’s get into it.

Ritu: Why don’t we start with you telling us a little more about ChristianaCare, where your digital health program stands today, what the enterprise priorities are, and how patient education and health literacy fit into that broader strategy?

Greg: I’m very lucky to work at ChristianaCare. We’re a very busy health system — one of the busiest in our region, the largest healthcare system in Delaware, and we provide care across four states. A lot of people are depending on us to provide safe, effective care. We like to say that we serve our patients, families, and communities in their care. One of the ways we do that is by becoming more effective at communication and more efficient in care delivery. Patient education and health literacy are tools in our toolbox to meet people where they are and make sure they’re getting the care they need and can care for themselves as needed.  As for system priorities — we do like to be innovative and forward-thinking, and that’s one reason health literacy is so important to our organization. Technology is equally valued. While I don’t oversee all of the technology platforms, I can tell you there is serious interest in making things easier for patients, families, and caregivers alike. Everyone wants healthcare to be easier — for those providing care, they want everything to be seamless and connected; for those receiving care, they want to understand what’s in front of them, what their choices are, and what they need to do to have a better quality of life. That’s where we’re trying to meet them.

Ritu: Thank you, Greg. That’s a perfect lead-in to my next question. We’re seeing a lot of focus on digital access and the consumer experience because patient expectations are rising alongside access to tools like ChatGPT, where patients feel much more prepared and empowered when they come in for care. What are you seeing on the front lines, and how does that shift the mindset around care delivery?

Greg: Our entire world is undergoing this relationship with AI and information. The attention economy is very important — where are people spending their time and attention? One thing we can’t forget in all of this is that people spend time with information and sources they trust. So while we continue to evolve and make care more efficient using advanced systems like AI, we really need to make sure the human connection is still there. No care can happen with digital resources alone. You need care providers, a team, and a system as the backbone to support all of it. That human connection is really where, from a communication standpoint, it all starts and ends.  From my perspective, if a person leaves a healthcare encounter confused — not knowing what’s happening or what to do next — that will not help build trust between the two people in that interaction. The tools we use, like AI and patient education resources, need to be wrapped in the context of the human relationship to be trusted. Otherwise, when folks don’t fully understand what’s happening from their care provider, that’s when they seek other sources online. We really want to make sure they trust us first, so that the tools we’re using are what they turn to when they need additional information and want to follow up on their care.

Ritu: I have a follow-on question. What do you think patients and families are actually asking for when it comes to the digital health experience, and how has that shaped the way you approach medication communication and education?

Greg: There are two things I think about on that front. Everyone wants this to be easier — that is the main thing. And it has to meet people where they are. Those are the two pillars. If a person is interacting with an information source and the information is too complicated, too voluminous, or too complex, they’re not going to be able to access it. How we communicate is actually an access-to-care issue. If people don’t feel they’re getting information in the way they need it, they may disengage and disregard that information entirely. Going back to ease — we have all become accustomed to everything being fairly easy from a consumer standpoint. At the touch of a button you can have things delivered to your home or get the service you need, and I think that’s what people are looking for from healthcare: to evolve and meet the same lifestyle standard they experience in the rest of their lives. The goal is to make it that easy, but at a level that people can genuinely interact with.

Ritu: Absolutely right — they want instant access, but they also want to be able to trust the information they’re getting. Rohit, would you like to ask a question?

Rohit: Greg, the practice of medicine is so wide and specialized, with so many different subspecialties. How do you build trust across all of them? Is your group focused on one aspect, or how does a health system overall build trust with patients across every specialty?

Greg: That is an amazing question, because it’s extremely difficult. The care we provide is so complex, the needs so varied. At ChristianaCare, we take care of people from all stages of life — we’re with people the whole way. Real trust and connection happen at the local level. Whatever your interaction with a given specialty is, that’s where the actual connection to care takes place. But from a system strategy standpoint, my team oversees the strategic plan for how we conduct patient education across the organization. We do need broad-reaching standards and practices to ensure the baseline for everyone is at a good operating level. That’s why we promote health literacy best practices. We need to make sure the tools we’re using are wrapped in the care delivery practices of each local provider, so that local connection really sticks. And going back to ease of use — if we’re making system-level decisions that set a relatively simple baseline for everyone, that makes it easier for local providers to pick up those tools and use them in their individual patient interactions. It’s the combination of those two things — system-level standards and local connection — that really drives success in healthcare.

Rohit: That’s great. And Greg, tell us more about how you started your nursing career and what some of your plans for the future look like.

Greg: I started as a student nurse extern — a common way many nurses begin their careers, finding your way into a health system and getting to work right away. From those roots I moved through surgical ICU, where I was a nurse for a number of years, and then continued to advance within the system because I really wanted to impact as many people as possible. I try to bring systems thinking to my nursing practice. After the ICU, I went on to get my master’s and became an advanced practice nurse, because from that position you have access to roles where you can influence with broad strokes how we provide care — and that’s where my personality met my profession.

Rohit: What do you think the future holds?

Greg: I just want to keep providing value — that’s my main goal. If I can influence the conversation and the attention given to patient education and health literacy, I’ll keep doing that. There are many competing priorities in healthcare, and one of my roles is to be an advocate for patients through communication and health literacy best practices. I hope to keep elevating that conversation, because I think it may be underappreciated in the history of healthcare just how important communication is in helping people reach their health goals. Whenever there’s an opportunity for improvement, it often lives in the communication realm, and we need to make sure we’re connecting with people.

Rohit: I’m curious about your approach — communication is so diverse, with so many channels available. How do you measure success at the end of a quarter or a year? What are your success criteria?

Greg: In the realm of patient education, those connections are still being developed. When it comes to quality of care, we have a number of metrics that health systems everywhere use to monitor things — like readmission rates and hearing directly from patients and families about their experience of care. We try to align ourselves with those system-level quality goals. The belief is that by improving patient education and communication, we contribute to improving those standard metrics: safe and effective care, and hearing back from patients and families that it’s working. Specifically for patient education, we’re working to connect our tools more directly to the outcomes we’re seeing. That’s still evolving work.

Ritu: It’s time to talk about AI. We speak with a lot of C-suite leaders, but given your experience on the front lines of nursing and working directly with patients, what do you think about technologies like ambient documentation, AI scribing, or voice agents? How are they going to affect nursing, and where do you see the most transformational potential? Where are you seeing the best adoption and the most meaningful difference?

Greg: I don’t directly oversee systems like ambient listening, but I pay close attention to what’s happening in those spaces. To me, the potential is huge, and I think it’s really going to break down silos. It’s still a little early to see exactly how it will all come to fruition, but one of the most frustrating things for patients and families is when care feels disconnected. We do our best here to make sure those dots are connected for people. AI has so much potential to take what has evolved as disconnected systems and bring them together — to take the cognitive load off the patient, to consolidate all of this information into a clear understanding of what’s happening, what the plan is, and what they need to do next. If we can do that, we’re really transforming healthcare, and I do see a lot of attention moving in that direction.  Healthcare is not easy — I can say that directly from having been at the bedside as a nurse. Anything we can do to make the process easier for everyone involved, both caregivers and patients, is going to be a game changer. If we can make all the systems work together to support that, it will be a genuine lifestyle change for everyone involved.

Ritu: Like a personal companion that guides you through the whole journey and keeps telling you what’s next — that would be really remarkable. While looking at ChristianaCare, we read about your work with FDB and their Meducation solution to bring personalized multilingual medication calendars inside the EHR. For listeners or health systems that haven’t explored that kind of tool yet, what does it actually look like in practice?

Greg: When I started this journey over twelve years ago, really focusing on patient education and trying to bring health literacy concepts and practices to care delivery, I wanted to find a strategic partner. I knew this process was going to take time to evolve, technology would keep changing, and I needed a thought partner and strategic partner for the long haul. That’s what I found with FDB and Meducation. From the very first time I saw their solution, I felt they had health literacy at the forefront, and that really came through in the innovation of the tool. The whole thing was created to meet the need of people understanding their healthcare, specifically around medications.  For those who haven’t considered this yet, I would say: add a health literacy lens to any decision-making process you undertake. Think about plain language, ease of use, and how the tool will actually be used — and that will help you determine what’s best for your organization. I saw from the beginning a real prioritization of patient needs first. And my team, being nurses, we don’t want to make life harder for the nurses and providers going through their daily activities, so we focus heavily on integrating the tool into their natural workflow. As much as possible, we try to keep it within a step-by-step process that doesn’t pull them completely away from what they’re already doing. FDB has been a great partner in working back and forth to make the tool as easy as possible for end users, which in turn makes the end product for the patient feel meaningful rather than cumbersome — something worth doing for the patient’s benefit. The main commitment is to ensure that the process of using the tool remains easy enough for our caregivers.

Ritu: Healthcare leaders are increasingly being asked to do more with fewer resources. Beyond the financial ROI of digital transformation initiatives, how do you measure the impact of health literacy programs in terms of clinician satisfaction, patient outcomes, or system resilience? How do you justify the investment?

Greg: We all need to be making sure we’re adding value, and we strive to do that every day. While we’re still developing the data tools on the back end to connect those outcomes, what really resonates for me personally is the individual stories. Nothing makes a healthcare provider — nurse, physician, physician assistant, whoever’s delivering that care — feel more validated than when they genuinely feel the care they’re providing is understood and that the person is going to be able to meet their goals.  When we do training sessions, my team doesn’t just manage technology — we also manage best practices. When we work with nurses and physicians on how to use the tools and how to communicate clearly using plain language, you see the light bulbs go on. When a provider says, “It’s really important to me to communicate clearly, so I use teach-back in my practice — and when I hear it back from the patient, I’m validated,” that to me is the most meaningful metric. Everyone is trying to build trust and make the relationship the driver of care, and that’s the best way to do it.  So it’s both ends: we’re trying to build a system-level view on the back end, making those ROI data connections to quality and safety — but providers don’t necessarily come to work every day with data metrics in mind. They’re trying to provide excellent care, connect with people, and make sure care is valued and that people can meet their goals. The sweet spot is connecting the tools with the practices that hit home, so that when you leave at the end of the day, you know you really connected with people and made a difference in their journey.

Rohit: Greg, any closing thoughts for our listeners?

Greg: I appreciate the opportunity to keep sharing this message. I just hope people continue to prioritize communication and health literacy best practices. One thing we really want to avoid in healthcare is waste — wasting time or resources. The cost of care is on everyone’s mind, so we need to be more effective and more efficient. If we’re not communicating well, we might be spinning our wheels. All of our caregivers are very well-intentioned, doing the best they can. But if we’re not confirming that it’s working — if we’re not making sure people understand what’s happening before they leave our care — we might be missing the mission. I encourage everyone, whether you’re a systems thinker at the leadership level or a caregiver working directly with patients every day, to keep the health literacy lens on. If you do, you’re much less likely to miss that opportunity to truly connect with the people you serve.

Ritu: Thank you, Greg. It’s been a pleasure having you on the podcast.

Greg: It’s been a great time. Thank you so much.

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