Ritu: Hello, listeners. A very warm welcome to the Big Unlock Podcast Season Seven. Today we’re very excited to have Weston Blakeslee and Dr. Ben Long with us. My name is Ritu Oberoi, and I’ll be your host today. Today’s episode brings together two leaders tackling healthcare transformation from complementary angles — clinical care and digital innovation. Dr. Ben Long is on the front lines at Magnolia Regional Health Center, delivering care while navigating the realities of modern clinical workflows. Weston Blakeslee from DrFirst is rethinking how medication management and patient engagement can be streamlined through smarter technology. Together they offer a grounded and forward-looking perspective on what it really takes to move from fragmented digital tools to truly connected, patient-centered care. With that introduction, I’ll hand it over to them for a short intro and then we can get started. Thank you both for joining us today.

Ben: Thank you so much for having us. We’re very excited to talk about digital health transformation and improving clinical care. As you mentioned, my name is Ben Long. I’m a hospitalist — internal medicine by training. I serve as the Associate Chief Medical Officer at Magnolia Regional Health Center, and I’m really motivated by innovations in care, quality improvement, and patient safety. That’s what led me to partner with my good friend Wes here. Wes, do you want to tell them who you are?

Wes: Thanks, Ben, and it’s a pleasure to be here. I really hope our listeners get some positive anecdotes and stories out of what we’re able to share today. I’m Wes Blakeslee, VP of Commercial Enablement at DrFirst. I’m a pharmacologist by training and spent my first five years developing heart failure therapeutics, so this particular population has always been near and dear to my passion for medication management. We were fortunate to partner with Dr. Long and his team and achieve some really positive outcomes from digital interventions in this population. I’ve been with DrFirst for about five and a half years, and my team partners with all of our clients to make sure that any digital interventions we launch are actually achieving the right clinical outcomes — which is part of the peer-reviewed study that Dr. Long and I collaborated on and published in January of this year in PLOS Digital Health.

Ritu: Welcome to the podcast once again. I’m glad you mentioned that study — we were really excited to read it and understand more about what you were doing. Let’s start there. Dr. Long, from the front lines at Magnolia Regional, take us back to the moment when you realized that heart failure readmissions wasn’t just a discharge planning problem but actually a medication follow-through problem. What surprised you enough to change the care model, and how did it all begin?

Ben: Thanks for that question. Before diving into any data, it’s really important to understand the setting. Magnolia Regional Health Center is a 200-bed acute care hospital in rural northeast Mississippi — a medically underserved population with high disease prevalence, particularly cardiovascular disease. Our patients face significant barriers that influence their outcomes: socioeconomic challenges including transportation and cost, limited access to specialty care, and more. That context really matters when we think about medication adherence. We know medications improve outcomes — that’s well established in the literature — but less attention has been given to how we help people not just comply, but become genuinely engaged in their care. For conditions like congestive heart failure, that’s critical, because all of these advances in medicine are helping people live longer, but they’re not living longer free from disease. As a system, we have to adapt to being better chronic care managers. And non-adherence is a real issue for both patients and providers in heart failure — it has a meaningful impact on outcomes and on the overall cost to our healthcare system. About one in five new prescriptions are never filled — what we call prescription abandonment — and about half of refills are eventually abandoned as well. DrFirst has been instrumental in helping us gain visibility into what is happening with patients and their medications over time. Wanting to improve that was our starting point.

Ritu: That’s a really good introduction to the problem and your study. But tell us more — given that this is an older, underserved population that may not engage with technology, how did you find that text reminders alone weren’t enough? And how did you arrive at the idea of nurse navigators and handing off to humans? I’d love to hear a story or anecdote if you have one.

Ben: It really came from familiarizing ourselves with our population. That took intentional work — understanding the individual problems over time that could be attributed as root causes of these patients’ outcomes. It took time, experience, and familiarity with these individuals. Too often we’re tempted to make a sweeping generalization about a population and then generalize a solution for them — and what happens is the change winds up being a change, not an improvement. It’s one part implementation science, as quality improvement often is, and one part really knowing the population you’re serving. Our population is older, largely Medicare-covered, rural, facing the socioeconomic barriers I mentioned — and understanding their patterns of how they access care and where our care planning along the continuum falls through was essential. One of the things DrFirst really emphasizes is eliminating care silos. For me, working in the hospital, that resonated deeply, because that is how our system functions. Throughout much of the country we have episodic care that is very good at being reactive — when the patient has an acute need, we stand ready to meet it. I’ve heard it described this way: the US healthcare system allows people to travel toward a cliff, even allows them to fall off, but has a really expensive ambulance waiting at the bottom. That’s obviously an incomplete and perhaps unfair characterization, but the point is that we have to adapt to being more proactive in meeting patients’ needs — especially as we drive down mortality rates with improvements in therapeutics, because then we have to manage the morbidity associated with more chronic diseases. We’ve seen a similar shift in oncology, where cancer has largely become a chronic disease and that has dramatically changed how we approach those patients. I think about heart failure the same way. How do we engage with patients more proactively? How do we help them with education and eliminate barriers? We’re looking for solutions that are widely accessible, lower cost, and far-reaching — because that equals higher value. That’s how we began thinking about what better looks like for this population.

Ritu: Thank you, Dr. Long. My next question is for Wes. DrFirst sits at the intersection of prescribing, adherence, and patient engagement. What are some of the friction points in the medication journey that you’ve solved, and which ones do you think AI will help address?

Wes: Our goal is always to build solutions that help providers and patients — giving clinicians like Dr. Long technology that extends their care to their patients. We’ve had a long strategy of ensuring we have a solution that can intersect every step of the prescription journey: from the initial prescribing decision, to real-time prescription benefit and medication cost, to prior authorization avoidance, to making sure patients don’t simply forget their medications by giving them education on why adherence matters and nudging them to fill their prescriptions. That was really the genesis of this study. Dr. Long and I have been collaborating for about five years on many different initiatives — this was simply the most recent one we published.  To his point, we need to be intentional about the technology we build. It can’t add steps to an already cumbersome system. Every new technological innovation we bring to the table has to be thoughtful. On AI — it’s the hottest topic in healthcare tech right now. We’ve employed versions of AI for a long time in the form of machine learning, but generative AI is a different matter. The machine learning aspects of our work go through extensive quality control and are very narrowly trained, which allows for much greater accuracy in the high-throughput recommendations we surface. Generative AI is still a nascent field and does produce hallucinations — not because it’s not useful, but because it pulls from such a wide dataset. In the context of patient care, any generative AI solution must have very rigorous quality control to ensure the data and recommendations surfaced are clinically accurate. That is one of the next frontiers we’re exploring, and having long-term partners like Dr. Long and his team at Magnolia gives us the opportunity to refine our technology while helping them solve real-world problems at the same time.

Ritu: Rohit, would you like to ask a question?

Rohit: Hello, Dr. Long and Weston — good to meet you both. This has been a fascinating conversation. As you mentioned, generative AI will add new dimensions to products and services, including voice agents, which we’re seeing deployed widely in the provider space. Dr. Long, being in a rural setting, what are some of the other challenges you’re seeing at your health system beyond medication adherence and cardiac care? And what other initiatives do you have in the pipeline around innovation?

Ben: Thank you for asking that, because rural healthcare is underrepresented in studies, in conversation, and in policymaking — and I suspect the majority of the country looks a lot like Magnolia Regional Health Center. When we innovate and design systems to improve quality and safety, it’s very important that work as perceived matches well with work as done — those frontline realities matter.  What comes to mind is the lack of interoperability, which connects directly to the care silos we talked about earlier. And I want to unpack that beyond just the obvious silo of hospital versus non-hospital. Think about the state of primary care in this country — we know it’s necessary, and we know it has eroded due to provider shortages and the increasing complexity of what’s being asked of those providers. We have a sicker, more complex population and people are being asked to do more with less.  A very real everyday example for us: a patient is seen in the hospital, and we have a capable electronic system that communicates well within our own walls — but not necessarily with the independent primary care office that might be in northeast Mississippi, northwest Alabama, or southern Tennessee. We can send prescriptions to an independent pharmacy and they receive them fine, but medication dose changes or discontinuations, when multiple streams are flowing into the same pharmacy or occasionally multiple pharmacies, represent a real hazard for the patient.  Automation will only get you so far. At some point, good care — especially for population management like heart failure — is hand-to-hand combat. You have to actually engage with that patient. That’s why being proactive matters so much: reaching the patient outside of the acute moments, outside of the brick and mortar, before they’re clinically declining. That’s what changes outcomes. We’ve tried to combine digital solutions with well-proven efforts like nursing-led care navigation — figuring out how to easily reach patients, and then what to do when we reach them. A lot of that looks like: “Can we go through your medicine cabinet together?” You can’t do that with a patient in the hospital. The pharmacy can tell you a prescription was filled, but we really have no idea whether the patient is taking their medications unless we connect directly. DrFirst’s technology helps us triangulate across insurance claims data, pharmacy fill data, and electronic prescriptions — but that picture isn’t complete until you connect the dots with the patient directly. That gives us the opportunity to educate, catch adverse events and side effects, and identify any unintended consequences of care. Our question is always: can we do this in a way that is replicable? If we can do it in an under-resourced place like rural northeast Mississippi, with an older population that has largely been characterized as unlikely to engage with digital tools — and prove that wrong — then I believe it can be done anywhere.

Wes: A couple of things to add, Ben. On the interoperability challenge — what makes the DrFirst and Magnolia collaboration so powerful is that on the healthcare technology side, we have all the data points to follow prescriptions from the writing event all the way to the fill event. But what really matters is how that affects patient outcomes. And as Ben mentioned, for a rural system, Magnolia has one of the most impressive informatics teams I’ve seen in my entire healthcare career. Being in a rural setting does not mean you can’t connect the dots on important outcomes and tie them to technological interventions. They’ve proven that.

Rohit: That’s great to know. Thank you.

Ritu: That leads directly into my next question, Wes. Healthcare technology often optimizes for workflow efficiency but not always for trust. What does trust-first design look like in clinical workflows and patient-facing tools, and what has been your focus there?

Wes: It starts with a simple concept: many healthcare providers measure their day in clicks. The way electronic health records were designed, the number of clicks and keystrokes is enormous, and the cognitive load and administrative burden become unsustainable. And connecting the dots all the way to the patient — we all have our smartphones with us nearly every waking hour. Patients are constantly inundated with text messages, emails, and app notifications. At the end of the day, we’re all patients too.  So the simple form of trust-first design is to intentionally make every step of the process easier. In the intervention Dr. Long and I recently published, we wanted to test how SMS nudges — with education, fill reminders, and cost information — would actually improve medication adherence and reduce readmission rates. In both areas, we achieved impressive results. Over a five-year study timeframe — two and a half years pre-intervention and two and a half years post — we gained a lot of very positive learnings from relatively simple concepts. The backend technology is certainly not simple, but the ideas we’re testing are. Sometimes it doesn’t take an incredibly sophisticated intervention to get really positive outcomes.  Starting simple and intentional with provider and patient workflows, rigorously testing and adjusting them — and then the final step that most healthcare tech companies don’t do, but that we pride ourselves on with partners like Magnolia, is clinical validation. That’s the most important part. With Ben’s team having such a sophisticated reporting capability specifically around this congestive heart failure population, we were able to demonstrate real impact.

Ritu: Time has flown by and we’re almost at the end of the podcast. Any closing thoughts, forward-looking statements, or crystal ball predictions you’d like to share with the listeners?

Ben: As healthcare leaders, we’re uniquely positioned to identify the gaps — between departments, between discharge and pharmacy, between prescribing and filling. Basically, the gaps that exist between our intentions to care for people and what actually happens. Digital tools are not the solution by themselves, but when aligned with clinical insight and operational execution, they become force multipliers. Our goal moving forward is not just to send more text messages — it’s to build systems that ensure patients actually receive the care that we prescribe. I think that’s the kind of leadership and innovation our healthcare system needs. The Big Unlock Podcast shines a spotlight on a lot of the successes happening around the country. We are often inundated with what we get wrong, but focusing on and celebrating what we get right is valuable and helps us learn as a system. I hope to keep doing that throughout my career.

Wes: To summarize Ben’s point, which I completely agree with — internally we call it the tech-plus-touch approach. You’re not going to have a technological solution for everything, but you need to empower both clinicians and patients to make their lives easier so they can do the right thing, and then celebrate those wins. Clinical validation at the end of that process is the key step we’ve been focused on for a very long time. As new innovations come to the forefront, we want to be intentional about those interventions and connect the dots from an interoperability standpoint to how providers are actually using them. Our Chief Medical Officer, Dr. Colin Banas, has a saying I always carry with me: “You can’t manage what you can’t measure.” We want to know what we’re measuring well before we embark on any intervention. And we need to make sure the people using the technology are using it properly and efficiently, and that it’s actually driving toward measurable outcomes — so we can celebrate those wins and iterate from there.

Ritu: Tech plus touch — I love that. Thank you both so much for being on our podcast today. It’s been a pleasure having you.

Rohit: Thank you, Dr. Long. Thank you, Weston. Great to be here.

Ben: Thank you all so much. We really appreciate it.

Rohit: Hi, Omkar. Welcome to The Big Unlock podcast. It is, great to have you here with us today.

Omkar: Thanks, Rohit. Nice to be here.

Rohit: Likewise. How are you doing?

Omkar: I’m good. It’s getting into springtime and lots of, uh, opportunities everywhere, so it’s nice.

Rohit: Great. So happy to share that we’ve crossed the 200 mark on the podcast Omkar, so it’s fantastic. So I’m Rohit Mahajan. I’m the CEO and, Managing Partner for BigRio and also the co-host of the Big Unlock podcast. The credit of starting this goes to Paddy Padmanabhan, who was the founder of Damo and he was very deeply loved by the audience and the podcast guests as well. So with that, I will ask you and request you for your intro, Omkar. So Omkar, tell us a little bit more first about KidX and the Children’s Hospital at LA And then would love to learn also about how you got attracted into the healthcare industry segment and what was your journey so far.

Omkar: That’s amazing. Sure. So listeners love to hear that. Yeah. So there are about one hundred children’s hospitals in the country. CHLA, Children’s Hospital Los Angeles, is one of the largest ones. And as the name suggests, it’s in Los Angeles, but specifically it is an academic medical center taking care of all sorts of very complex and sick kids, honestly, who require procedures, treatment. We have a very busy emergency department, very impressive and historic research organization, largely collaborating with the University of Southern California, USC. Our faculty are all faculty physicians at USC. Great teaching tradition there as well, so we have residents and fellows, both from the physician standpoint, but also from a nursing standpoint. Incredible clinical care. We have been ranked in the US News top ten honor roll for many, many, many years and continue to be in that ranking. And then what’s really unique is we are proudly a safety net hospital, so we actually take care of a very large proportion of kids on Medicaid, which I think a lot of people don’t realize this, and we may talk about this soon, but a lot of kids in this country, more than half of the kids who are hospitalized in this country, are covered by Medicaid. It’s a really important part of the equation. Actually about forty percent of Medicaid, I believe, is children. So it’s, it’s a big chunk of the puzzle. Yeah. And we take care about, of about seventy to seventy, seventy to seventy-five percent of our inpatient discharges are typically kids covered with Medicaid. And so we’re really proud of the safety net element of our hospital, the great clinical care, the great research tradition, and the great teaching academic tradition as well. So we just celebrated our hundred and twenty-fifth birthday as a hospital a few weeks ago, actually.

Rohit: Congratulations.

Omkar: Yeah. It is. Yeah. That’s amazing. It’s a big deal. It’s, it’s hard to do anything for a hundred and twenty-five years and let alone do it so well, so this place is really successful. So that’s that. KidsX, really briefly, so you know when I came to CHLA in twenty eighteen, I quickly realized that there was an opportunity for children’s hospitals to come together to work together to drive digital innovation. And large part of that is because children’s hospitals don’t compete nearly as fiercely as adult health systems do. Most of them have geographic regions that they cover that don’t necessarily overlap. Pediatricians are also highly collaborative in nature, and there’s a need for collaboration. Uh, the populations are heterogeneous, they’re small, and innovation requires large populations and scale at some point. And so the opportunity for collaboration was great. I was lucky enough to get to know and collaborate with many children’s hospital innovation leaders in my first few years at CHLA, and in 2019, we built this consortium called KidsX, which we run here in Los Angeles out of CHLA, and it’s driven around driving pediatric innovation. So we’ve been live for six years. That’s amazing. We’ve helped launch over 85 innovation projects in children’s hospitals across the country in that time period and are still going on very strong, so very proud of that work.

Rohit: Amazing, amazing journey, Omkar, and please share with us that what motivated you to join into the healthcare industry segment and basically your origin story so far.

Omkar: So I’ll share that growing up, my dad worked in the pharmaceutical industry. I had lots of family and friends who went into medicine, nursing, pharmaceuticals, something in healthcare. So I feel like I was always surrounded by it, and for me, though, I was not a science guy. Like, I love science, but I didn’t excel perhaps in biology or science or any of those things. Instead, I was more of a business leader. I went to school for business. I went to school for management, et cetera, and at some point after my undergraduate studies, I was in a job in a hospital in New Jersey, and my boss at the time told me, “You know, you can actually make a profession out of this. You can actually go be a hospital administrator,” which is really a role I never thought about, somebody who’s non-clinical who can still do work in healthcare. And at the same time, I learned about this whole field of public health, and I really fell in love with public health. I thought it was a really interesting concept about keeping populations healthy and how can we create systems as simple as fluoride in the water, or seat belts, or making sure kids are safe, people are safe where they go. All these different things to keep communities safe and prevent things from happening. So that’s what got me into this space, and then over time, I think a big part of my story is being lucky to have opportunities offered to me, and then being discerning about which opportunities to take. And so I’ve been able to build skills in Lean and Six Sigma. I’ve been able to work in health systems from New York to Los Angeles and have been able to see front hand how decisions are made in healthcare. And what I’ve really come to realize is that a lot of healthcare, innovation, whatever you wanna call it, is really about relationships. It’s about trust that you can build with doctors and nurses and executives and administrators. ‘Cause at the end of the day, this work that we do where we’re trying to change healthcare, whether it’s through technology or process change or anything, it involves trust. You have to get people who are taking care of patients every day to trust that this new thing that you want to bring to them is going to work, that it’s going to make their lives easier, and that it’s going to ultimately create a benefit. And that’s the core of what I’ve learned, and I’ve been lucky to kind of go through this journey over the past few decades that’s gotten me to where I am today.

Rohit: That’s great, Omkar. So please tell us a little bit about, you said children’s hospitals are different from the general hospitals, even in terms of the location like you were describing it, and it is great that, you know, you have this consortium which is KidsX. So what is so special about pediatrics patients? Could you tell us a bit more about this aspect of it?

Omkar: Sure. Thank you. So I didn’t know a lot of this when I came into pediatrics, to be fair. So I don’t think, I think there’s even a lot of executives and leaders in innovation don’t realize the differences that exist. Some things that may seem obvious once I describe them, but it’s important, right? So the end user, if it’s a consumer-facing technology, typically for an adult, is the patient themselves. Maybe it’s, you know, a loved one, but typically it’s the patient. For pediatrics, it’s not. Typically pediatric patients are under the age of 7 or under the age of 12. If you look at the cohort of people who are in children’s hospitals, they’re typically young children. There are teenagers and adolescents, but the bulk are under the age of 12. And so typically you’re dealing with the end user being the parent, and that’s one big thing to think about. So you’ve gotta design a solution for a caregiver, not necessarily for the person experiencing the healthcare. That’s interesting. Children’s hospitals are also not taking care of the parents, right? Those parents don’t have MRIs. They don’t have accounts with that hospital. Those hospitals are actually taking care of the kids. That’s challenging. One thing I like to remind people, if you look at the demographic spread of the United States, the youngest Americans or people who live in this country are the most ethnically and linguistically and culturally diverse. So if you look at people age zero to 18 in this country, which is the pediatric population, and even if you include their parents, which would go up to whatever age you wanna think about, that’s a much more diverse population than 70 to 80-year-olds. And so when you think about language, when you think about culture, when you think about ethnicity and considerations, you’ve gotta build that in. You need a solution that’s gonna be English, Spanish, a lot of things that are culturally relevant, so that’s another thing. But from a privacy standpoint, another thing that’s important, I use the number 12, the age 12 deliberately. So children, once they’re over the age of 12, but before they’re 18, they have rights in this country to be able to request that their health information be kept private from their parents. Right? So the teenage girl who doesn’t want parents to know that she’s on birth control, that’s an example, but there’s many examples of that. And so as you develop a digital solution, whether it’s parent-facing or child-facing, you’ve gotta create different privacy provisions for that, you know, 12 cutoff. This impacts things like patient portals, appointment scheduling, text message reminders. You don’t wanna send a 13-year-old’s parent a reminder about their upcoming visit if that patient has chosen for that parent to be kept out of the loop. I mean, little things like that you’ve gotta think about because those, those children have rights. And then the other big one, if you’re developing anything that has any physical size to it, right? Like a device or, or anything that, like a, a sensor. Such a diverse population, right? So if you look at adults, generally adults are all the same size, generally. I mean, yes, there’s different size variations, but generally the same size. You look at a little baby that’s born in the neonatology NICU area and you compare that to a three-year-old, and you compare that to an elementary school-aged kid to a teenager and then a 17-year-old. These are all pediatric patients, right? Yes. But the size of these people, their physiology, it’s very different. Uh, so lots of things to consider. And then, uh, and then, you know, engagement with this audience is constantly changing. When I started CHLA, social media meant one thing. Today it means something different. And how to engage audiences using different technology means different things. And there’s so many, there’s so many more things we can talk about as well, different regulatory considerations as well. The other thing that’s really important to me, and to lots of us, is information security. Yeah. You do not want the identity of these patients to be compromised. Yes. Because imagine, like um, I’ve got young children. I would hate for any of their identities to be compromised. Yeah. Somebody opens a credit card in their account. All of a sudden now you know, they’re 18 and their credit is ruined. As a health system, we have to be extremely careful about the identity of these patients and making sure that it’s very protected. So lots of things, but you know, people don’t necessarily think about that. Now despite that, one thing I’ll say, pediatric patients make up 20% of the population, right? If you think about children 0 to 18, it’s about 20% of the US population. Yet, when you look at investments made in digital health, innovation, it’s less than 1% goes to pediatrics. So there’s a huge discrepancy in terms of how much money we invest into pediatric innovation, and that’s part of the motivation of KidsX, is we need to really create a place where these innovations can excel and scale.

Rohit: Yes. Great, great thoughts and insights here, Omkar. Let me ask you the next question, which is coming to my mind, is that since you mentioned this whole innovation ecosystem, what are some of the innovations that you are seeing in the pipeline or the problems that needs to be solving from your vantage point?

Omkar: Rohit, there’s so many. Yeah. Thank you for that question. So one of the biggest crises that’s happening to our children right now is around mental and behavioral health. I think there’s always been a challenge with kids as they’re growing up having to figure out how to navigate and manage their emotions and their mental health. But with the pandemic and post-pandemic period, it’s been exacerbated. So I think there is so much unmet need in this country as it relates to pediatric mental health. And what I’m seeing more recently is data showing that elementary school-aged kids, there are suicides being attempted amongst these children. There are mental health crises that are happening with third graders, fourth graders, fifth graders. And sometimes those things, if they’re unchecked, lead to much bigger problems when they’re teenagers. So how do we screen and diagnose and treat mental health conditions for anyone, maybe even in kindergarten and above? How can we do that? That’s an unmet need. And it’s unmet because we don’t have enough providers to do that work. Yes. We cannot rely on old care models where you have one-on-one synchronous face-to-face meetings. The wait lists for these providers across the country are very long, especially if you’re on Medicaid, which as we talked about, is about half the population. So we need new ways to screen these patients. We need new ways to engage with them and then treat them. So that’s one big area. Another one that’s growing, and it’s a great thing, many children, so if you look 50 years ago, 60 years ago, children diagnosed with some chronic conditions, those conditions were terminal back then. So take diabetes. I was actually talking to my father-in-law, and he said when he started practice 50 years ago as an endocrinologist, that when a child was diagnosed with diabetes, often that meant that we didn’t expect that child to live into adulthood. But today, people live well into adulthood, maybe even a full lifespan, managing diabetes that’s diagnosed, Type 1 diabetes that’s diagnosed in childhood. So how do you develop chronic care management tools for children that translate into adulthood, so things that the kids can manage and translate? I was talking to this innovator in Vancouver who is doing some really cool things because the Canadian health system, they care quite a bit about longitudinal care. So she’s thinking about how to build a digital solution that you introduce to a child when they’re in elementary school or middle school. They keep using it, and then when they’re an adult, they keep using it. So now this is something that goes with them. So longitudinal chronic care management is another really big area that I think is really interesting. And then the last one that I would just point out that I think is an unmet opportunity is around access to… I think there’s an opportunity for us to leverage a lot of these artificial intelligence gen AI tools to equip parents with getting answers to questions that they have that they otherwise just don’t ask or they look online and don’t get the right answer to, right? As we sometimes say, when you bring home a child from the hospital when they’re born, no one gives you an instruction manual, right? No one tells you how to raise that child. You ask your parents, you ask your grandparents, your neighbors, your friends. That’s how we rely on this. But we’re living in twenty twenty-six, we have so many validated clinical… so much information around how best to raise a child, what things to look for, what symptoms, what signs. And right now, the only tools that patients have are Google or their social connections, or they’ve gotta wait to see a doctor, which often comes with a co-pay and often comes with the struggle of accessing that person. Can we use consumer-facing AI tools to enable parents to get access to information that they need at the moment they need it, that we can believe is validated and accurate? We’re already seeing, and I’m sure you’ve seen the studies, that so many consumers, adults and pediatrics, are going to ChatGPT, and they’re going to Gemini to ask these questions. Let’s figure out how we can get the right answers that’ll reduce your urgent care visits, reduce your same-day appointments, and it’ll hopefully allow for better health outcomes.

Rohit: Absolutely. That actually leads us nicely into the next question, Omkar, which I’m curious about, is that would you like to describe any gen AI initiatives or AI initiatives that you are seeing being implemented either at CHLA or other places that you know about, you know, which are kind of in this domain of children’s hospitals? And then we were talking earlier about secrets for startups. So kind of in that direction, what are your thoughts?

Omkar: So I do think there’s some early work happening around how we can equip patients with information either about a chronic condition, so we’re thinking about diabetes and asthma and some of these chronic conditions, so that we can provide the knowledge base with really clinically validated content so that they’re only getting information from sources we trust. That they’re able to engage with it as a chatbot or as you would with a ChatGPT. So that’s something that I’m seeing and we’re working on and others are working on as well. I really hope that becomes a solution that is available to people, because I think that what we’re seeing on the flip end is, you know, what does a parent do? They pick up the phone and they call, and they call somebody, and let’s give them answers twenty four/seven that we feel good about. The other thing I’m seeing, I think, in the gen AI space that I think is very interesting is around synthesis of information. So pediatrics more than anything else, a pediatric patient at a children’s hospital is likely to have been seen by or will be seen by many different specialists and doctors, right? Because they’re typically very sick. So they’re seen by, they’re being seen by lots of consultants, and many times they’re in the hospital multiple times. And so they’ve had previous consultants, hospitals, so many different people who’ve engaged in their care. And so what does a doctor do who’s taking care of them, or a resident do who’s taking care of them? They’ve got to read all these notes, all these historical documents to make sure they don’t miss anything so they can be well informed about this, about this patient in their hospitalization now. So what I’m excited about is seeing some of these synthesis tools, including some that the EHRs are working on, that are going to be able to synthesize information from past notes to be able to generate summaries so that not only is it a time savings, but you can really make sure that you capture the information so that the provider can really take care of the patient that is there today. Now again, lots of questions around accuracy, validity of that summary and synthesis, but again, as we think innovation, that’s a huge opportunity for pediatrics because it is such a specialty oriented profession and space, children’s hospital, that having that synthesis from multiple sources is going to be crucial as we think about efficiency, scaling, and things like that. Which I guess dovetails nicely into your question about kind of secrets of working with startups. So I’ve had the amazing luxury of being able to work with lots and lots of startups over the last decade, and I think as we are in this new AI space, I think a couple things come to mind. One, be really specific and careful about what you’re going to promise in terms of the efficacy of your product and the outcomes that it’ll deliver, right? Hospitals are very discerning, and they should be, particularly as you think about AI, about when you say your product can do X, what does that actually mean? Is that in one pilot study that you did with one unique place, or is this across many different clients you had? And particularly as we think about tools that are part of clinical decision-making, that’s gonna be really valuable. Having not only evidence, which has always been a goal, but deep evidence, evidence that has large sample sizes and perhaps heterogeneous populations, right? You didn’t just work with one hospital in Pittsburgh. You worked with hospitals all over the country with lots of different populations and care models, and through that you were able to reduce errors in clinical decision-making by 10%. Great. That’s what you need. So getting there is going to be important. Now, the challenge of that is it takes a long time to get to that point. So making sure we have the right funding vehicles to support companies in early stage that are particularly building these AI tools so that we can get to a place where these tools have validity before they are scaled across the country is gonna be really crucial. Another really important one is around business model. I cannot describe how many times I’ve heard of a great solution that is solving a real problem but has no viable business model that’s going to, you know, scale across all the different types of healthcare settings that are out there, meaning I can’t think of who’s gonna pay for it and why they would pay for it. So that’s another one. The other just general thing I worry about, when you’re coming to pitch something, your best bet as a startup is not to get some sort of grant-funded pilot. Your best bet is to get a commercially funded engagement because that tells me, that tells you as the startup, hey, there’s some real value that people are imagining here. And the final thing I would say is humility. I think nobody expects that your solution is going to completely change decades of healthcare practice. Admit what you can do, talk about what you’re focused on, but be humble about it. And I think, I’m sure, you know, if you listen to the hundred and ninety-nine other podcast episodes that you’ve had on this show, you’re gonna see the same thing, right? Ultimately, this is going to be about partnership between people. And humility is such a key part of developing trust.

Rohit: That is amazing you say that. I think it goes such a long way, Omkar. Yeah. So now that we are coming towards the end of our conversation for this time, what are some of your predictions for the future or any other parting thoughts that you would like to share with the audience?

Omkar: Predictions for the future, that’s very interesting. So I think that there is still a tremendous amount of work to do as we think about tech-enabled services. There’s a lot of work we have to do around not only making healthcare more efficient and more cost-effective and faster, but also about how we can reach more patients. I think one of the best ways of doing that is by enabling those who are providing services with technology that’s gonna help make them create better outcomes and see more patients in a timely manner. I firmly believe, and I may be in the minority here, but AI replacing humans in healthcare is not the way I like to think about it. I think healthcare is a very human-to-human process. I think when people are sick, they wanna talk to somebody, even if it’s somebody on the phone. Because they care about… they want someone to be able to comfort them and talk to them, et cetera. I don’t think people want an end-to-end transaction for many of their, you know, more complex needs that are entirely technology oriented. Perhaps, sure, if you need eye drops, you can do that through a chatbot. But if you’re sick and you need an MRI, and you want someone to talk to you about your MRI results, a lot of people are gonna wanna talk to a human. Now, how can we enable that person who you’re going to talk to so that they can talk to 10 times as many people, or five times as many people, whatever the case may be. Let’s help them become more efficient and better at their job. Let’s not talk about replacing humans with all these bots and whatever. Again, some instances it may make sense, but I do think that, and lots of consumer behavior shows this, people still want to talk to somebody. And my thinking and prediction is that we will still have humans in healthcare five to 10 years from now, lots of them. They will just be doing work that is much more top of license. They will be empathizing and talking more to patients, and letting the AI and the technology do a lot of the routine, manual administrative stuff. That’s my hope for where we go, because I think that if it’s my kid or my parents that need healthcare, or myself that needs healthcare, I’d like to talk to somebody and engage with a human. So that’s my prediction.

Rohit: That’s awesome. So with that note, Omkar, thank you so much. This was a wonderful conversation, and I’m hoping that we will have you back on the podcast sometime soon for a follow-on meeting.

Omkar: Thanks, Rohit. Thanks for having me, and always happy to come on the show.

Ritu: Hello, listeners. Welcome to Season Seven of the Big Unlock Podcast. My name is Ritu Uberoy, and I’m managing partner at Damo Consulting and your host today. Really excited to have with us Dr. Nele Jessel, Chief Medical Officer at athenahealth. Dr. Jessel has been a practicing pediatrician for over two decades and is a board-certified clinical informaticist. She brings a rare perspective that bridges frontline medicine with digital transformation, and she has been a leading voice on how AI and health IT can reduce clinician burden while improving patient outcomes. Looking forward to our conversation today. It’s all yours, Dr. Jessel. Thank you so much for being with us.

Nele: Thanks for having me. Pleasure to be here.

Ritu: We’ve read your athenahealth report and I was also looking at your blogs. One key observation was that you noted a rapid shift from skepticism to enthusiasm among physicians using AI — that so much has changed in just the last 12 to 18 months that clinicians finally believe this technology can actually help them. We would love to hear your perspective on that shift, what you think the contributing factors were, and where you think it’s headed in the next year or so.

Nele: I’m actually still amazed every day at how quickly the sentiment has shifted, because medicine isn’t typically known to be fast-moving or change-friendly technically. So watching the rapid evolution of AI and the shift in physician sentiment has been really remarkable. As you mentioned, 12 to 18 months ago when we did our physician sentiment survey and asked about AI for the first time, the sentiment was essentially: “Okay, maybe this could potentially be helpful, but we’re actually quite worried it’s going to be yet another thing on our plate.” And that’s not surprising when you think about how EHR technology has developed over the last two decades and how it has always been viewed as a burden by physicians. For the longest time, EHRs have been cited as the number one contributor to physician burnout. Physicians have, I’ll say it, hated EHRs for adding to their administrative burden and turning them from actual physicians into data entry clerks by interrupting the physician-patient relationship. So EHRs have been no one’s friend.  I believe that is one reason why, when generative AI and large language models arrived on the scene, physicians were very skeptical it would actually be helpful — given their previous technology experiences. The first attempts to implement AI in healthcare technology weren’t very successful. Vendors repeated some of the mistakes of the past and jumped straight to their solutions without asking clinicians what the right use cases actually were. And I think the main pivot I’ve seen since then is this: we’ve moved to letting clinicians guide development rather than vendors retrofitting technology to perceived use cases. Let physicians tell us where the need is.  After the initial lessons learned — everyone jumped to having AI answer patient messages, which wasn’t quite the success we thought it would be — we found a use case that was extremely well-received: leveraging large language models to generate physician note documentation. Note documentation has been one of the greatest burdens physicians face. When EHRs arrived, notes became far more complex than old paper notes because the appetite for data increased dramatically. Physicians were asked to capture more and more data with 100% accuracy for legal and billing reasons, and notes turned from one-liners into giant complex documents. It became a major burden.  Introducing ambient note generation — where AI records the conversation between the physician and patient and then summarizes and transcribes the note — was a pretty immediate win, even though initially we didn’t actually see this translate into time savings. Our initial data showed that physicians sometimes spent more time on the note because they had to make extensive edits. But the perception of time-saving was immediate, because physicians were able to concentrate on the patient again during the visit rather than on the computer. That relief from cognitive burden was significant — having to write a note while actively engaged in a conversation adds enormous cognitive load. When you and I are having this conversation, the last thing either of us wants to do is also be the note-taker. Yet we ask that of physicians every single day, not just once but 30 times a day depending on how many patients they see. Introducing ambient note generation immediately removed that cognitive burden and gave physicians the sense of having time and quality of life back — even though, at first, the actual minutes spent on documentation weren’t much less, and sometimes more.  That has since changed quite dramatically, but that initial sense of relief was the turning point. Because of it, physicians became much more inclined to view AI as a benefit, word of mouth spread quickly, and ambient notes were adopted so rapidly that physician sentiment shifted from “Oh no, not another thing in my EHR” to “Give me more — what else can you do to reduce my administrative burden and cognitive load?” That, I believe, is where that rapid change in sentiment originated.

Ritu: Amazing answer — lots to unpack. A couple of follow-up questions come right away. You mentioned data, and you said there is so much of it now, but clinicians still struggle with fragmented workflows. What’s missing between having data and actually making it usable at the point of care? Where do you still see gaps that technology hasn’t solved, even with all the data being generated?

Nele: That is still one of the struggles across the industry, and it came through loud and clear in our last physician sentiment survey as well — interoperability is still perceived as a major challenge by clinicians everywhere. Not so much because of a lack of interoperability per se. We have made good progress over the last decade in getting systems to talk to each other and exchange data. However, that has led to a new problem: access to too much data. While we have gotten good at exchanging data, we are still not great at exchanging it in a way that curates it for the clinician on the receiving end and helps them make sense of it. We basically overload clinicians with data — we bring in volumes from other systems and dump it in the EHR, then leave it to them to hunt and peck through to find what they need. Our chief product officer calls it “slunking in the EHR,” which I think is pretty apt, because that’s exactly what clinicians complain about: “Great that you’ve given me access, but help me find it.” With 10 to 15 minutes per patient and hundreds of incoming documents and data points, the concern about missing something — and the liability that comes with that — is something I hear from physicians constantly.  What can we do to help clinicians make sense of all this data? By and large, physicians don’t object to having external data brought to them — they understand it’s important to good patient care. But they want help making sense of it. And that is one of the use cases where generative AI in the form of large language models can add tremendous value. As we all know, LLMs are extremely efficient at quickly digesting large amounts of data and synthesizing it into something more concise. Those of us who have used large language models for research know they can sift through thousands of pages and quickly surface the relevant take-home points. That’s how we’ve started applying them: quick summaries like “show me what has happened since I last saw this patient” — the Reader’s Digest version. Which specialists have they seen? What medications were changed? What lab results have come back? Is there external data I need to review? That is the next frontier, though we’re not fully there yet — because when you’re summarizing clinical data, it’s critical that the models return both relevant and accurate information. Large language models are probabilistic, not deterministic, so just like a person they pick and choose what seems important.  Now, perfect is the enemy of the good. Are clinicians 100% accurate and always focused on the most relevant information? No — there’s simply too much. What we’re finding is that the models are often equally good, sometimes better, than clinicians at surfacing what matters. So we’re quite optimistic that this is what will finally turn the EHR from a giant data repository into an actual helpful tool — one that helps clinicians improve patient outcomes and truly acts as an assistant rather than a burden.

Ritu: That goes right back to the cognitive load physicians carry beyond documentation. You raised a really interesting tension between accuracy and relevancy, and how you trust what an LLM brings back to you. Until last year we were firmly in the “human in the loop” era — AI is here to augment, not automate. But in the last six months we’ve really seen a shift, with AI becoming more and more independent. Just recently a leading health system CEO announced that he sees AI taking over clinical and diagnostic work as well. How do you draw the line between AI as a helper and AI going beyond summarization into actually guiding care?

Nele: Personally, I’m not quite there yet on AI replacing clinicians, and I see plenty of examples in daily life where AI gets it completely wrong. Clinicians get things wrong too, of course. But part of what makes medicine a little different from other industries is that it is both a science and an art. A lot of what happens in an exam room is not on the surface — it’s in the interaction between the patient and the physician: the body language, the facial expressions, the things that aren’t said, how family members in the room react, how you interpret the data in context. Granted, AI is really good at catching things that can get lost in the shuffle. We’re all busy, things become routine, and in the heat of the moment — when all the exam rooms are loaded and people are already waiting in the doorway — the tendency can be to take things at face value rather than dig a layer deeper. That’s often how mistakes get made. That’s exactly where AI can be really helpful: sounding the alarm and saying “Did you see that this lab value came back and it doesn’t quite match the diagnosis you selected?” — more as a check on yourself than as a guide for care. Because I’ve also seen AI make pretty dramatic mistakes, saying an X-ray looks fine when there was clearly a fracture. So it goes both ways.  The human and AI should ideally tag-team and double-check each other. Medicine is one of those fields where you would rather have too many checks and balances than too few. The error of inclusion is better than the error of exclusion. Now, that said, are there many use cases in medicine where AI can absolutely take the lead and probably do it better than a human? One hundred percent. Take administrative use cases: prior authorization. Prior authorization has very quickly risen to the top of most-hated tasks for clinicians — I think it actually surpassed the EHR as the number one driver of provider burnout last year. Can agentic AI handle prior authorizations? Absolutely — and then kick the edge cases or denials back to the clinician and clinical staff for review. Insurance verification, scheduling, check-in — do you really need a human for those, or can you free up your human staff for the high-value, face-to-face interactions that genuinely require their presence? The front desk person doesn’t have to check patients in and collect insurance information — AI can do that, often better. The patient uploads their insurance card on their phone, AI selects the right insurance package, and now the front desk person can spend two minutes actually talking with the patient, forming the human connection that makes people want to come back for in-person care rather than turning to a chatbot or WebMD. That’s how I view it. Would I want to remove the clinician from the clinical loop? No — that would be the last thing on my list. The way we develop AI functionality currently, especially on the clinical side, is to keep that human in the loop at minimum until the trust level is high enough — for both clinicians and technology vendors — that it feels appropriate to consider reducing that oversight. That is not where I would start.

Ritu: I think fully removing the clinician is still a ways out. And as you rightly noted, a lot of the big wins right now are in that AI front door space — patient check-in, voice agents handling prescription management and appointment scheduling. Those are exactly where C-suite leaders are seeing easy, meaningful implementations. From athenahealth’s perspective, Dr. Jessel, how do you balance the “move fast and learn” mindset with healthcare’s requirement to get it right every single time? Those seem like two very conflicting viewpoints.

Nele: That’s a great question, and it’s something we’ve learned a lot about over the last year and a half since we really kicked off our clinician-facing AI efforts. We’ve always used AI on the back end — machine learning and the like — to reduce administrative tasks, so that part isn’t new. What was genuinely new was the clinician-facing use cases: leveraging AI, and especially large language models, which are probabilistic, in clinical medicine. That’s been the new frontier for the past year and a half, and we learned very quickly that it required a different development approach.  What we do now — with AI in the clinical realm, the revenue cycle realm, and patient experience — is develop prototypes rapidly and test them with real live users in a small-scale production environment very quickly, to get real-time feedback right away. We’ve formed special user groups of highly motivated early adopters who understand this is experimental, that there are risks, and that we depend on their candid feedback. And there are genuinely many motivated users who are enthusiastic about trying AI in real practice and telling us what works. We’ve coined the term “pre-alpha” for this stage — it’s different from our standard development process, which normally goes through extensive user experience design, UX sessions, alpha testing with a select group, then beta, then general availability. In pre-alpha, we put small groups of highly motivated users in a live environment to give us the most basic signal: is this even useful, or are we completely missing the mark? We’ve learned to fail fast. We now have hundreds of these experiments in this pre-alpha stage with real live users, and not all of them make it to alpha — but the ones that do move pretty rapidly through to beta and then general availability.  That’s allowed us to shift the mindset on the customer side as well. We went from “No, please don’t put that in my EHR” to “What else can we develop together that would be useful for all of us?” It’s been exciting to watch, and I think all of us are really excited to be living through a transformation where healthcare may finally change in the way we’ve been hoping for decades. It’s an exciting time to be in healthcare technology.

Ritu: Thank you for sharing that — the pre-alpha approach sounds really interesting and something our listeners will love to hear about.

Nele: I’ll add one more thing: we have an in-house patient safety team comprised of clinicians who are embedded on our development side. Everything we do — even in this early pre-alpha stage — gets vetted by our patient safety clinicians to ensure there is no risk to patient safety. We test all the output and have patient safety eyes on it before anything goes into production, even with just a few select users. That’s an important addition, because at the end of the day it’s all about patient care. Yes, we want to reduce clinician burden and administrative burden for practices and ensure they get paid appropriately — all of that absolutely matters. But first and foremost, we want to ensure patients receive optimal care and have great outcomes.

Ritu: Time has flown by, Dr. Jessel — we’re almost at the end. Would love to hear a personal anecdote or story about how you came into healthcare and specifically into this role bridging technology and medicine, and any closing thoughts you’d like to share with our listeners.

Nele: As I mentioned at the start, I’m a pediatrician by training. I had my own practice, and I founded it because I had previously been part of a large group practice that made the transition from paper to EHR. I was actually one of the super users who helped implement the EHR system — back when EHRs had first become a thing and were anything but user-friendly. I tried very hard to improve the implementation for my practice, offering my input and help, but this was before clinical informatics was a recognized discipline and physicians’ voices were largely ignored in technology decisions. So I thought: there has to be a better way. This technology has amazing potential — how can I actually leverage it to be a better physician and spend more time with my patients? Since I couldn’t make that happen in my group practice, I went out on my own and founded my own practice with exactly that goal.  Around that time I went to a technology conference, evaluated different EHR vendors, and saw a mock-up of the “office of the future” where everything was fully integrated and automated. I thought, that’s what I want to build. And I set about building it for my own practice. Gradually I became deeply interested in clinical informatics as it was emerging as a discipline, went back to school, became board-certified in clinical informatics, and joined a large medical group where I eventually led the clinical informatics team and optimized the EHR system. I then did the same for a second large enterprise medical group, and ultimately ended up at athenahealth, where I have a similar role working directly with the product development team to translate clinician needs into technology — because I speak both languages: the practicing physician’s language and the clinical informatics language.  I really love this role because it lets me do at scale what I was trying to do in my own practice: optimize technology to the point where it does what I’ve always felt it should — enable clinicians to spend more time with their patients rather than less, and genuinely improve care rather than detract from it. That, in a nutshell, is my story and how I ended up here. I’m so grateful for the opportunity to make a difference at this scale.

Ritu: Thank you, Dr. Jessel, for sharing that. It’s a really wonderful story. And I think we really are reaching that point where AI will allow technology to become invisible in the background and let clinicians do what they do best — spend time with their patients. Looking forward to that future.

Nele: Thank you so much for having me. It’s an exciting time and I can’t wait to see where we’ll be in a year or two. I believe EHRs will function very differently from the way they do today.

Ritu: Thank you, Dr. Jessel.

Nele: Thank you.