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 very excited to welcome Umesh to our podcast. Umesh Rastogi serves as General Manager in Microsoft’s Healthcare and Life Sciences Dragon and Platform Group. In this role, he leads product and engineering teams developing innovative AI solutions for nurses, as well as broader data platform capabilities for the healthcare industry. Umesh is deeply passionate about building technology-driven innovation to help the healthcare industry achieve its quadruple aim, and he has held pivotal roles in engineering, technology, architecture, and product management. Welcome, Umesh, to our podcast. Really happy to have you here.

Umesh: Glad to be here, Ritu.

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

Umesh: My journey has been more than two decades in enterprise software. I’ve held a variety of different roles and worked across both the applications domain and platform. My foray into healthcare was about ten years ago, actually when I was helping my daughter with her biology coursework — that’s when I thought about how I could use technology to have an impact on healthcare. Over the years I’ve had the opportunity to build hospital emergency response solutions, and when COVID hit, various other healthcare capabilities along the way. And as you mentioned, I’m now leading our product and engineering teams building the Dragon Copilot solution for nurses, and I’m very passionate about the work we’re doing there.

Ritu: Thank you for sharing that story. We always love origin stories, and we find out the most interesting things about people. Let’s get started. Healthcare leaders often talk about AI as if the primary challenge is just selecting the right model or the right LLM. But much of your recent work has focused on building unified multimodal healthcare data foundations. Are we at risk of overestimating the AI problem and underestimating the data problem? What does AI-ready really mean in a healthcare organization?

Umesh: That’s an excellent question. If you take a step back and ask what outcomes these organizations are typically trying to achieve, it fits within the quadruple aim: improve healthcare outcomes, improve patient experiences, reduce cost, and improve the well-being of clinicians — physicians, nurses, and so on. Whatever technology we pick has to address one or more of those aims. Many organizations end up making it purely an LLM or core model problem, but that’s just a piece of the puzzle. The whole technology stack actually starts from making sure you have access to the right data — the data on which you’re applying AI models and agents and enabling workflows for physicians, nurses, radiologists, and others.

Ritu: Tell us more about how Microsoft is partnering with health systems to responsibly scale AI-enabled clinical solutions specifically for nursing workflows.

Umesh: If you look at what’s happening in the nursing workforce, recent data points to a shortage of about 80,000 nurses as of last year, and that gap is expected to increase to about half a million by 2030. What’s driving that is obviously the increasing demand from an aging population, but more importantly, high turnover within the nursing workforce — on average about seventeen percent across health systems in the US. The primary driver of that turnover is that forty percent of nurses report high stress and burnout. And the biggest contributor to that stress and burnout is that they spend forty percent of a typical twelve-hour shift in the system rather than taking care of patients.  The administrative work in EHR systems includes flow sheet documentation — rows and rows of data to capture as part of patient assessments, including vitals, head-to-toe assessments, patient safety documentation, and so on. That’s the biggest chunk of that forty percent. On top of that, nurses spend time looking for information — either in the EHR about the patient chart, or looking up drug adverse effects from external credible sources, or searching their own internal protocols and guidelines. And they handle various communication and coordination tasks, many of which could be delegated. All of this forty percent drives high cognitive load, turnover, overtime, and poor documentation quality. That’s the challenge nurses are facing today.  What Dragon Copilot is doing to address this is somewhat unique. If you look at the differences between physician and nursing workflows, nursing is typically much more fast-paced and mobile. In an ambulatory setting, a patient comes to visit the doctor — it’s a different environment. In nursing, it’s a very fast-paced environment: nurses are moving around, taking care of patients, attending to a given patient multiple times during a shift. The workflows had to be rethought. Over the last two years we’ve been co-innovating with nine different organizations — including Mercy Health, Advocate Health, and Stanford — learning, validating, and iterating on the solution to make sure we get it right. We knew from day one that for nurses to actually use and like the solution, it had to be as low-friction as possible and naturally fit the workflows they were already accustomed to.  We invested heavily in usability, in making sure AI accuracy was right, and in thinking through what organizations would need to scale the solution to thousands of nurses — because this is an entire workforce transformation. The result is what I would call the most comprehensive AI clinical assistant for nurses, which not only streamlines documentation but also surfaces information and automates tasks nurses currently handle manually. The nurse carries the app on a mobile phone, and as they see the patient at the bedside, they select the patient record and have a natural conversation with the patient. Or if they’re moving between patients and have an ad hoc observation — perhaps something they didn’t want to say in front of the patient — they record it and leave the rest to AI, which does the necessary extraction and automatically populates the flow sheet. Nurses can quickly review, edit if needed, and file. In addition, through Dragon Copilot’s chat functionality, nurses can ask about past patient interactions or access pending care activities, because Dragon Copilot provides a kind of cognitive memory of everything that has happened with the patient across the shift. The net result is a meaningful reduction in cognitive load.

Ritu: Thank you, Umesh — really detailed and well-explained. One of the great points you made is that the solution has to fit naturally within existing workflows and eliminate friction, because that’s what leads to so many technologies failing at the POC stage and never scaling. That leads into the next question: when nurses think about AI, there’s often resistance and skepticism. What have you actually seen resonate once nurses experience this AI firsthand? Are you seeing better adoption rates and a more open mindset toward the technology?

Umesh: There is a lot of hype and misinformation in the industry around what AI is and isn’t and how it will change things. From our standpoint, nurses who have actually experienced this technology firsthand tend to share feedback in two main categories. The first is around their own cognitive load. Nurses are saying the system is now doing a lot of things for them. Without this technology, when a nurse is doing documentation away from a workstation, they’re often scribbling observations on their hands to key in later, or trying to remember what they told a patient and whether they followed up. That adds to cognitive burden. Now, because the system ambiently listens to the conversation with the patient and records everything, nurses don’t have to carry that mental weight. The system has already extracted the information. And importantly, those conversations often contain more than just flow sheet data — for example, a promise to follow up on something. The system surfaces that as a pending care activity, and provides a running summary across all interactions with that patient during the shift. These capabilities significantly reduce cognitive load.  The second big category is presence. Nurses are saying they now feel present with the patient. Previously, nurses might have their back to the patient while keying things into a workstation, or be mentally focused on documentation while asking questions — not truly present. With this technology, nurses report being fully there with the patient, with a much freer flow of communication and needs, without worrying about explicit documentation. They leave that to the system, which does the extraction automatically, ready for review and filing. Those are the two main categories of feedback we hear from nurses who are using the solution.

Ritu: This is what we’ve heard from other C-suite leaders as well — an indirect benefit is that patients also feel more heard, because nurses are articulating more. Since the ambient system is capturing everything, nurses explain things more fully, and that benefits patients too. As generative AI becomes more embedded in healthcare workflows, there’s increasing discussion about responsible AI, governance, and trust. How should health systems balance the urgency to innovate with the need to ensure that AI-generated insights remain transparent, explainable, and clinically trustworthy — especially as the volume of information makes a human-in-the-loop approach harder to sustain?

Umesh: I would identify three aspects. First, before we ship any product or AI capability, responsible AI is front and center in Microsoft’s product development process. Nothing gets released without going through all the necessary checks — bias evaluation, review by nurses on our own teams who can assess whether an answer makes sense, and all the compliance requirements. That evaluation is done before anything is released.  Second, from a workflow standpoint, Dragon Copilot — whether for physicians, nurses, radiologists, or other clinicians — functions as a copilot. The user gets to review the AI output, edit if needed, and then file. Yes, AI can sometimes be wrong. And in these specific workflows, incorrect documentation could create patient safety risk, so clinicians are always expected to review the AI output before filing.  The third aspect: many organizations are saying they can’t treat AI as a black box, and they want transparency into how it’s behaving. We’ve built capabilities within our product to address this — we believe in establishing trust through transparency. Customers can configure their system to export raw data into Microsoft Fabric or their own data lake, giving them access to recordings, transcripts, and full visibility into how the AI actually performed. Organizations use this for audit purposes, for research, and for training. Based on ongoing customer feedback, we continue to develop additional capabilities to provide even more transparency — including visibility into usage patterns and what kinds of outputs end users are receiving.

Ritu: You mentioned co-innovating with nine large health systems and learning from them. Given all that experience, what are the key lessons for implementing at enterprise scale? What would you caution health system leaders about, and what would you want them to take away?

Umesh: There were two distinct categories of learning as we went through this experience. The first was iterating on the technology and the core product. Through working with nurses, nurse informatics leaders, and nurse leaders, we enhanced AI accuracy, improved the usability of the solution, and added functionality to continuously reduce friction in the workflow. We also built the organizational tools needed for a mass rollout. Pilots were manageable — a few hundred users. But a full-scale rollout means thousands or tens of thousands of nurses. You need visibility into actual usage across your workforce, across nursing units. So we invested in analytics tools giving nurse informatics leaders access to usage data and patterns. We also built AI tuning and customization tools, because each organization — and even each unit within an organization — has its own flow sheet schemas and charting practices. The AI solution has to align with those specific schemas and practices. We gave customers and their nursing informatics teams the ability to tune the system accordingly.  Almost equally important was the second learning: how these solutions actually get rolled out within organizations. At the end of the day, this is a workforce transformation and a change management challenge. It’s a different way of doing things — essentially building a new habit for nurses. Not every nurse is comfortable speaking aloud or charting aloud. We found demographic differences: more tenured nurses tend to be more comfortable with it than Gen Z nurses, which was somewhat counterintuitive. These non-product learnings about how the technology needs to be rolled out are critical to achieving maximum adoption. Change management is key — ensuring nursing leader sponsorship, nurse manager sponsorship, and dedicated champions for the technology in every nursing unit. We encapsulated these as change management best practices so that organizations can use the same technology in the best possible way for their specific context and actually achieve adoption.

Ritu: Digital literacy is so important — it helps remove the skepticism we talked about earlier. Once people understand the technology, they become much more open to adoption. We’re almost at the end of the podcast. Looking ahead one to three years — we used to say five years, but that horizon is now too far — do you believe the biggest impact of AI in healthcare will come from automating administrative work, enabling personalized patient engagement, or do you think an entirely new kind of care model will be unlocked?

Umesh: Restricting to the next three years and focusing on how nursing workflows and care delivery will change — I think we’ll see smart hospital rooms and wearable devices become far more integrated into workflows, creating a much more frictionless experience for both nurses and patients. Today we talk about audio; I think audio plus video will become integrated as well. I believe we’ll build a kind of AI-native UI that can pull together and surface information from far more data sources — delivering not just information but genuine insights. We already have early success there: bringing together data from the EHR with external credible sources and internal guidelines documents. Lastly, I think we’ll see a lot more agentic workflows rolled out. Today we talk primarily about streamlining documentation, but there is so much that nurses handle in interactions with supply departments, transportation, facilities, and so on — all of which takes time away from direct patient care. We’ll see more of those non-patient-facing activities delegated to agentic execution. I think those themes will come together very neatly over the next couple of years.

Ritu: Thank you, Umesh. Thank you for being on our podcast today and sharing all your learnings with us. I hope our listeners take away a lot of valuable insights. On behalf of the Big Unlock Podcast, thank you for joining us.

Umesh: Thank you, 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, everyone. A very warm welcome to all our listeners to The Big Unlock Podcast Season Seven. My name is Ritu Oberoi, and I’m the managing partner at Damo Consulting and your co-host today along with Rohit. We are really excited and honored to have Dr. Haider Warraich on our podcast today. Dr. Warraich is a cardiologist, researcher, and one of medicine’s most compelling contemporary writers, known for bringing humanity, history, and philosophy into conversations about health and illness. He joined ARPA-H as a program manager in December 2024. ARPA-H is the Advanced Research Projects Agency for Health, which supports the development of high-impact solutions to society’s most challenging health problems. He was previously a faculty member at Harvard Medical School and Brigham and Women’s Hospital and director of the Heart Failure Program. He has written three books and over 175 peer-reviewed papers. Really happy to have him here with us. I’ll ask Rohit for a short introduction, and then it’s all yours, Dr. Warraich.

Rohit: Hi everyone. I’m Rohit Mahajan, CEO of Damo Consulting and co-host of The Big Unlock Podcast. Very happy to be here. Over to you, Haider.

Haider: It’s my pleasure to be here, Ritu and Rohit. We first met at HIMSS and I know we’ve been meaning to speak since then. Lots of exciting things are happening in the health technology space, so I’m excited to dig in.

Ritu: Great. Let’s get started. ARPA-H was created to tackle ambitious, seemingly intractable challenges with a very different risk tolerance than traditional research funding. Based on your experience there, which healthcare problems do you believe are truly moonshot-worthy? And are we being bold enough in the questions we’re asking, or are we still being cautious and not fully exploring the potential of AI?

Haider: To create a thriving funding infrastructure, you need a variety of payers and funders who can tackle different bottlenecks in the market. You need an agency like NIH with broad scale where progress is realized over decades, if not longer. At the same time, private funders — especially in the AI space — will play a huge role in supporting breakthroughs and promising teams. ARPA-H fills a gap that existed in federal research funding: there was no health-focused agency that could take big swings, turn things around quickly, operate on a metrics-and-milestones basis, and have the risk tolerance to take on areas where incremental progress will never be enough — areas of genuine national interest that also create common good for the rest of the world. Our agency director, Dr. Alicia Jackson, has outlined key priorities for the agency. These include winning the biotech race, longevity and reversing aging, defeating devastating diseases, and taking a platform approach to AI. Within AI, we have many different programs approaching this from different angles. The program I’m leading is called the Advocate Program, and its vision is to bring AI to the bedside — bring generative and agentic AI that can essentially do everything a clinician can over the phone — and take that through the regulatory pathway so it can open up this entire field for everyone. But to do so not just quickly, also done well, given how critical it is to get this right. The response to not just this program but to many of ARPA-H’s programs is a testament to how important it always was to have us as part of this ecosystem.

Ritu: I want to dig a little deeper into the speed at which AI is moving. When you take on a project like Advocate with a six-month horizon, but everything can change in a week — how do you keep up and think that far ahead?

Haider: It’s a great question, and it’s actually what makes developing a government-funded AI program very difficult. Even the most ambitious projections in this field can become obsolete very quickly given how fast things are moving. Here’s how I’ve approached it. First, there’s a reason Advocate is focused on the highest-risk AI in the most regulated space. There is already significant integration of AI across healthcare, but mostly in low-risk settings — ambient scribe technology has taken off, and clinical decision support will transform how clinicians interface with the healthcare system in the next few years. But Advocate was focused on developing a patient-facing technology, which puts you in a high-risk but also high-reward category. It targets really high-capability functions: making a diagnosis autonomously, performing triage, closing the loop by providing and prescribing treatment directly to the patient, and going through a regulatory pathway where there’s essentially no precedent for technology like this. We also focused on a relatively high-risk population — patients with heart failure — because ARPA-H believes that if you create a demonstration this ambitious, it opens up the field for everyone downstream. The reverse doesn’t happen. I also tell people: if you’re developing an AI program and it doesn’t sound insane to you, you’re going to get scooped. If what you’re doing sounds feasible today, you’re already a few steps behind. A lot of the work we’re developing for the future would sound off the wall if I described it today — but that is the kind of ambition we need to bring to this technology, because so far it has delivered on, if not exceeded, expectations.

Ritu: How do you reconcile that supersonic speed of development with regulatory pathways that are still slow? If it takes months to approve something, you risk falling behind again.

Haider: That is one of the reasons why an agency like ARPA-H should — and does — work very closely with the regulator. The FDA has never had a partner in government like ARPA-H before: one that can make major investments and ensure that the problems we’re solving actually help the regulators, rather than just impeding their work or getting in the way. That partnership is really crucial. I do think that many regulatory unlocks are fundamentally bureaucratic issues — a sense that you need X number of patients, a specific margin, a specific enrollment type. But in the AI space, a lot of the regulatory blockers are technical. We simply don’t yet have the technology infrastructure that this space requires. One of the most important parts of the Advocate Program is not just supporting the development of technologies that will be classified as medical devices by the FDA, but also funding evaluation technology for post-market monitoring. We call this a supervisory agent, or the supervisory layer — and it is being developed at the same pace and with the same level of resources as the primary technology, if not more. Rather than building in sequence, we are building in parallel, because we understand that traditional timelines and pathways are not going to work for this type of technology. If you want to be relevant as a body that can administer regulations in this space, you need technical support. We have to funnel and focus the best minds and best teams in the world on the problems that are going to unlock this field — and working with the regulator to keep their perspective centered ensures we create solutions aligned with their strategy. We plan to keep doing that.

Ritu: One of the most interesting things I picked up is that the Advocate Program is patient-facing. When you’re talking about technology that has to sound almost crazy to be relevant, how do you bring it to people who may not be ready for it?

Haider: There are two things. First, when you start with an ambitious vision, you also need to scope it. Instead of saying the teams Advocate supports are going to build AI cardiologists, what we’re focused on is: can you develop a technology using generative and agentic AI that improves outcomes in patients with heart failure? That immediately scopes the problem — you define the population, the endpoints, what goes on the label, how you design and deliver it. Scoping is very important. Second, communication matters enormously in the AI space — more than in almost any other field — because people genuinely care about this. This is not a technology working quietly in the background. Everyone sees AI as something that could transform society, healthcare, and every industry. That creates excitement, but it also creates anxiety. There is a real risk that poorly developed tools could add more friction to a system that doesn’t have a lot of give. Technology that isn’t well designed, or doesn’t have the patient’s or clinician’s best interest at its core, could be genuinely catastrophic. So how we talk about this technology and how that is reflected in the design matters enormously for getting the market ready. The technology is ready — the market needs more time. But the market also needs to see things in familiar forms. Clinicians take risks with new technology all the time — new implantable devices, new drugs, new chemotherapies, new genetic therapies — but there is always a sequence to what they see before taking that risk: clinical trials, FDA authorization. There is a precedent for what a community needs to see before accepting that the benefits of a new technology exceed the risks. That is what the first generation of clinical AI technologies needs to provide. For patient-facing technologies, the bar should be, and I think it is fair to argue it should be, the same as for a new high-risk medical device or a new drug. Once that foundational evidence exists, it will cause a seismic shift in how these technologies are accepted, implemented, and deployed. That is one of the core theses of the Advocate Program. Even though the technology itself is frontier — agentic AI for clinical care, supervisory agents for real-time post-market monitoring — the evaluation is super traditional: single-site deployments, human factors research, IDE studies, multicenter trials, and outcomes people understand: heart failure hospitalizations, guideline-directed medical therapy, mortality. We chose to innovate in the technology but kept the evaluation as familiar as possible, because that is the evidence people are accustomed to seeing when adopting new technologies.

Ritu: Great answer. Now I’ll give Rohit a turn.

Rohit: Thank you, Ritu. Haider, I especially liked the point that the FDA has never had a partner in government like ARPA-H — that’s fantastic, because regulatory hurdles need to be surmounted quickly. Tell us a bit more about the Advocate Program: the time horizons, the funding size, the number of projects. And separately, tell us about your own story — what attracted you to healthcare and to ARPA-H, and what you’re looking toward in the future.

Haider: On the timeline: the Advocate Program was launched in January. We solicited solution summaries from teams across each of the technical areas — focused on the development, evaluation, and implementation of clinical agentic AI, patient-facing, for patients with heart failure. That is standard process for every ARPA-H program. We then encouraged teams to submit full proposals, from which we make decisions about who to recommend for funding. Our hope is to announce the selected teams in the coming months, sooner rather than later. On funding: these are contracts, not grants, and a typical ARPA-H program is usually between fifty and a hundred and fifty million dollars. That said, the value the program was always created to provide goes beyond what you can count in dollars. If you’re going to make a dent in the AI space, money alone is not enough — there is plenty of capital available. The real question is how we create value and something that genuinely matters. We are leaning into our strengths as a government agency, which means focusing on the regulatory pathway, on implementation, and on helping health systems get excited about technology where there are still a lot of unknowns. Healthcare systems wanted a partner they could trust as a convener of some of the best technology companies working in this space. And then there is access to data — data that will help determine how well these technologies are developed and how well they perform after deployment. Our goal for the teams is a technology ready to go to FDA for market authorization within two years of the program starting — one year felt too short, three years felt too long. After that, we plan a multicenter trial of the highest-performing technologies in an integrated health system, focused on clinical outcomes but specifically on how to implement this technology in clinical practice. We hope to share more details in the coming months.  As for me: I grew up in Pakistan, not far from where Ritu is from in Delhi, just across the border, and went to medical school there. I have always approached writing as a storyteller first, and that has informed my research and clinical career. I moved to Boston for residency after finishing medical school, completed a cardiology fellowship, and then had the opportunity to join the FDA as a senior advisor on chronic disease to the FDA Commissioner. That experience brought everything into sync — giving me the regulatory perspective, helping me understand the critical bottlenecks, and helping me find a common language that works for technologists, private investors, academics, health systems, and regulators. I have been at ARPA-H for almost a year and a half, and it has been really exciting.

Rohit: Thank you for sharing that. You mentioned the Advocate Program application cycle has closed — are you following a January-through-December cycle, and will the next cycle open in January for teams that want to know?

Haider: The Advocate Program opening has closed and we’ll be able to share more once all the teams are on contract — that is essentially day zero of the program. We’re hoping that comes soon. Overall, things are going well. The way ARPA-H works, program managers are expected to develop new programs roughly every year, ideally building on and extending the thesis of the previous one. I foresee — and I’m hopeful — that you’ll see our team coming out with more programs that build on Advocate and extend its impact across the field. This space is too big to rely on a single program to unlock it. Teams are also always welcome to send their ideas directly to ARPA-H and connect with program managers. That process is competitive and depends on finding the right idea, the right risk space, and the right team, but it is an opportunity that remains open. These investigator-initiated projects, alongside agency and program manager-initiated programs, are the major ways to engage with ARPA-H, and there is always new work and new opportunities coming up.

Ritu: I think we can squeeze in one last question. Seeing this field so up close and being in touch with teams telling you about the future of healthcare innovation — which emerging technology do you think is receiving more attention than it deserves? Where is everyone focused when there are other things we should be looking at?

Haider: My paradoxical answer is probably AI. What I’ll say is that AI is receiving the appropriate level of attention, but there are still real gaps we need to focus on. How high-risk AI technologies are evaluated — real-time evaluation — is going to be incredibly important and needs more attention. AI alignment is another: how do we align AI outputs with the preferences, values, and needs of humans, not just as groups but as individuals? That is especially important in healthcare. I also think AI developed specifically for the populations with the greatest need — older people, people with multiple comorbidities, cognitive dysfunction, sensory issues — needs more focus. Developing and validating tools for those populations, where we think the benefit can be greatest, is underexplored. And finally, the space I’m most personally interested in: I think we have finally reached a point where we have the models, the data, and the compute to develop high-fidelity simulations for healthcare. In every other field — manufacturing, supply chain, logistics, transportation, defense, climate — simulation is a huge part of how innovation happens. It remains essentially nonexistent in healthcare, largely because healthcare is hard, our data is messy, humans act in complex ways, patients have intricate physiologies and pathologies, and the overall complexity has made realistic simulation essentially impossible until now. That is a space I believe has real potential to transform how we develop and value technologies, and could meaningfully shorten both development and implementation cycles.

Rohit: Haider, would you like to say anything to close out — perhaps about your most recent book or anything you’re working on?

Haider: Before joining government I had written three books over five years, and in some ways joining ARPA-H was my retirement from writing — I wanted to take a break and be fully present in this work. My last book was focused on chronic pain, partly because from a scientific perspective it is a fascinating phenomenon, partly because I practiced at the height of the opioid epidemic in the United States, and partly because I have lived with chronic pain myself — I’m all good now, but for many years it was part of my life. I combined all of those threads into a book called Song for Scars, published around 2021. But it has been a while since then. What I will say is that a lot of the work we do at ARPA-H is unexpectedly similar to writing a book — having a plan, an outline, a vision, and then putting the pieces together, talking to people, doing research, iterating, editing. It is a very creative, collaborative process. So even though I thought I had retired from writing, much of what I learned then has proven deeply applicable to developing programs and working within this amazing ecosystem.

Ritu: Thank you, Dr. Warraich. It’s been a pleasure having you on our podcast. Thank you so much for joining us.

Haider: Thank you. I appreciate it.

—

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 co-host today along with Rohit. We are very excited to welcome Dr. Anne Snowdon to our podcast. Dr. Snowdon is a fascinating guest because she looks at healthcare through a lens that most leaders overlook — the supply chain. While many healthcare executives focus on clinical care, digital transformation, or reimbursement, Anne has spent decades demonstrating that quality, safety, and innovation all depend on the invisible infrastructure that connects products, data, clinicians, and patients. Anne is Professor of Strategy and Entrepreneurship at the University of Windsor, CEO of SCANHealth, and Chief Scientific Research Officer at HIMSS. She has spent decades studying the intersection of healthcare delivery, technology, and system resilience, and we are really happy to welcome her to our podcast today. With that, I’ll ask Rohit for a brief introduction, and then it’s all yours, Anne.

Rohit: I’ll be really brief — thank you, Ritu and Anne. Good to be here with both of you. I’m the co-host of The Big Unlock Podcast, and I look forward to an engaging conversation. Over to you, Anne.

Anne: Thank you. I’m Anne Snowdon. As Ritu has shared, I am the Chief Scientific Research Officer for HIMSS, where our mandate, mission, and vision is to ensure every human everywhere has access to their full health potential. When you really think about that, it requires an impressive flow of data right to the point of every decision, starting with patients and clinicians. I’m a nurse by training, a full professor at the Ontario Business School, with a program of research in supply chain — which is a very important infrastructure piece that most people simply don’t realize is important. I’m delighted to be here and looking forward to our discussion.

Ritu: Great. Let’s jump right in. The keyword of the day is AI, and we are all inundated with promises about its potential — that AI is going to revolutionize healthcare. But what real progress have health systems actually made toward this digital and AI transformation? And in your opinion, can we actually get there? What’s the horizon you see for real change?

Anne: Such a great set of questions. I absolutely think AI holds great promise and is probably one of the most unique groups of technologies health systems have seen in likely decades. The real catch is: what can it actually achieve right now? I think that remains to be seen. At HIMSS we are seeing impressive work across Asia-Pacific countries, North America, and Europe — and much of that work is in the pilot phase, a “try this on for size” stage. Healthcare is a very evidence-driven sector. I’m a nurse by training — if I’m going to use a new technology to deliver care to the children I used to care for, I want to be sure it will be valuable and most definitely not cause harm. I would say we’re early, Ritu, in where health systems are. Great promise, a lot of attention — but as a researcher, when I look at the hard data, it’s pretty early. There’s a lot of anecdotal evidence and impressive work around AI tools for clinicians — quality of work life, ambient listening so documentation doesn’t take so long. But beyond that, it’s early days, and things are unfolding so quickly that health systems are struggling both to understand AI’s full capability and to think through how best to integrate it into care processes to achieve what it promises for patients and the workforce.

Ritu: Thank you, Anne. You’re echoing what we’ve been hearing from C-suite leaders coming onto this podcast — health systems are investing heavily in AI transformation initiatives, but scaling innovation remains a challenge. The anecdotal evidence is there, but based on real numbers we still have some way to go. In your opinion, from everything you see, what are the biggest barriers preventing healthcare organizations from moving beyond pilots to enterprise-wide transformation?

Anne: I think there are three things. First, AI is a unique group of technology — it learns and changes over time. We are a sector accustomed to testing and evaluating, like a clinical trial of a device or a medication, and those devices and medications don’t change. We test until we’re confident they work for every patient. The challenge with AI is that where it starts and where it learns, shifts, and possibly drifts — we don’t have the methodologies to track that as directly as we would with any other kind of technology. So the first barrier is understanding that this is a technology with great promise, but it’s continually learning. It’s about building a lifecycle approach to understanding what it is, what its purpose is, for whom it is best suited, and how you track, once you implement it, that it’s achieving everything it promises. That’s a significant shift in how health systems have always operated — you’re tracking a moving target, not a fixed quality outcome.  The second barrier is the workforce and workforce confidence. Physicians, nurses, pharmacists, and allied health professionals have not been broadly educated on what AI is and is not, and what it does and can achieve — so it’s a bit of a black box for the workforce. And there’s a deeper issue: AI is about prediction. A machine learning tool predicts whether the child I’m looking after in an intensive care unit may be at risk for an infection. But we are not educated or socialized as a workforce to think about probabilities — which is what a prediction is. We’re trained on very deterministic thinking: here’s the assessment, here’s the diagnosis, here’s the best clinical pathway to care for this patient. That is a completely different way of thinking. That migration from deterministic best practice to a technology predicting probabilities and enabling us to prevent risks — we don’t have much experience doing that, and there is understandable hesitancy and many questions in the workforce.  The third barrier is the evidence. We are a very evidence-driven sector, and the evidence base on AI applications and tools is still pretty early. When you put all those things together — it’s fascinating, exciting, and compelling, but also a little daunting, because we’re being asked to think differently and make decisions differently. The system hasn’t yet built the foundations to ensure that when you spend the money on AI — which is expensive — you can be confident it will return tremendous value for patients, the workforce, and your capacity to deliver care.

Ritu: That’s an absolutely brilliant summary. The first point about AI being a moving target with new headlines every single day, the second about digital and AI literacy where people just aren’t ready and information is coming in too fast, and the third about the shift from deterministic thinking to probabilistic — you can really see how that migration would be difficult. Great answer, thank you.

Rohit: I was intently listening and thinking about the same thing — how evidence-driven this industry is, and how preventing harm and mitigating risk is always at the forefront. Anne, could you tell us more about your role at HIMSS and some of the key initiatives you’re driving this year and next? And also please share your origin story — what attracted you into healthcare and how your journey unfolded.

Anne: Let me start with what I do at HIMSS. I lead the research at the Office of Scientific Research. The basis of that work really reflects the vision of Hal Wolf, our CEO, and Reid Oakes, our COO. Because healthcare is so evidence-based and research-driven, HIMSS as a global network advancing digital health transformation needs to be the epicenter for creating the evidence of what digital health transformation actually achieves. That is very much the work I lead. We’ve published studies on what a highly digitally advanced hospital achieves for patients, for the workforce, and for performance outcomes compared to hospitals with much less progress in digital health transformation. One clear finding is that quality and safety outcomes are exponentially stronger in highly digitally advanced hospitals. How exactly those digital infrastructure systems drive those stronger outcomes is the next question we’re working to answer. We’ve also looked at the patient experience — highly digitally advanced hospitals show much stronger patient experience outcomes as well. A lot of our work at HIMSS involves measuring where hospitals are starting from in their digital health transformation journey. Many hospitals say, “We’re impressed by this AI technology — where do we start?” It’s a bit like a GPS on a road trip: where are you starting from, and where do you want to get to? We measure what digital assets and strengths a hospital already has that will serve them well as they advance AI strategies and technologies. So I lead the research and evidence base that defines what digital health transformation is, what it achieves, and for whom — and I work closely with health systems globally to define the future of digital health transformation, measured through our maturity models.  As for my story — I built my very first machine learning algorithm in around 2004 or 2005. I was leading a national child seat safety study in Canada, because road crashes are a leading cause of death in Canadian children. As an ICU nurse at the time, I cared for a lot of profoundly ill children as a result of head, neck, and spinal cord injuries from these crashes. I went to the auto industry and asked why they couldn’t better protect children, and the answer came down to how children are securely seated in vehicles. We conducted a national child seat survey and built a machine learning algorithm to predict which ways of seating children in vehicles were associated with the greatest risk of injury in a crash. If we know what puts children at risk, the auto industry can make much safer seating, seat belts, and child seat companies and policymakers can act accordingly. That’s how I first really understood the opportunity of AI and what it can achieve.  As a nurse, I also spent a lot of time focused on quality and safety outcomes. Medical error remains a problem even after two decades of healthcare technology — and that led me to supply chain. In automotive, I learned that they can track and trace a brake pad right to a vehicle owned by a citizen in any Canadian city, and can tell you exactly who made that brake pad and why it failed. Can we do that in healthcare? Can we know exactly what medication was prescribed to which patient and whether it helped, achieved nothing, or caused harm? That’s a supply chain question, and the answer is currently no. If we had that data, we could build the AI tools that predict which patients are at greatest risk, and then as a nurse I would know to find an alternative. In a nutshell, my career has been about solving these challenges by leveraging technology and transforming systems to achieve what they need to for the people they serve.

Rohit: Very interesting — thank you for sharing.

Ritu: From your unique vantage point as Chief Scientific Research Officer at HIMSS, what key areas of health and care do you see as most likely to benefit from AI-powered solutions right now, across all your research?

Anne: I can tell you what I’m seeing and what I’m not seeing — and it’s likely what I’m not seeing that I’m most anxious to pursue. What I am seeing is that health systems are generally adopting AI to optimize or automate processes. Patients can engage with an AI chatbot to book a visit or a procedure. Ambient listening for clinicians is making documentation better and smarter. There are a number of these AI technologies within health systems today that show real promise. But the common theme across almost all of them is making the system work better for the system. The gap I’m seeing — and what I’d really like to see more of — is what these technologies are achieving for patients. What are these AI tools, these agentic AI chatbots, actually doing for and with patients? How are they helping people manage their health? How are they connecting people to providers in much easier ways than making an appointment, travelling to a facility, waiting in a waiting room, and hoping to be seen within a timely manner? What I see today is that patients still have to meet the needs of the system to get care. The gap I really hope we can influence is how we can leverage AI tools to help people help themselves. If I’m a parent with a two-year-old with asthma, where are the AI tools that help me understand my son’s asthma triggers and how air quality changes put him at risk when he goes to school — tools that alert me that today is a day he should stay inside? In other words, help me help my son manage his condition. Those are the tools I’m not seeing yet. I think AI has a wonderful opportunity there. There are many AI tools in the commercial space, and that presents both opportunity and challenge. I recently had a pediatrician share this story with me: a mother came into the emergency room, the physician assessed and diagnosed the child and created a care plan, and the mother said, “My agent tells me I should be doing something different. Why are you telling me something else?” So the AI available in the commercial space is trying to help people, but it’s not connected to the health system. It potentially creates a divide between best evidence-based clinical practice and what a consumer AI agent has surfaced from the public domain. I think we need to close that gap — give people technology tools that are connected to evidence-based, talented provider teams, so they get accurate, truthful information that helps them make good decisions. That’s a space where I’m really looking for progress.

Ritu: That’s exactly why OpenAI and Anthropic have jumped directly into health — they’re seeing tens of millions of queries a month from people asking exactly these questions: how to be better prepared for a doctor’s appointment, what to ask, how to manage their own health proactively. People want to do this, they just haven’t had the right tools. Maybe now they will.

Anne: But those tools are coming from the commercial space, which isn’t connected to the reality of care delivery. That’s the gap.

Ritu: That leads naturally to the next question. Healthcare leaders often focus on clinical quality, patient experience, and financial performance as separate priorities. How can a modern digitally enabled supply chain help health systems improve all three simultaneously? And what metrics should executives actually be tracking? Why does it remain so siloed — one person looking at finance, another at patient experience, and nobody really talking to each other?

Anne: It’s very true, and I literally just gave a presentation on this. In every other business sector, supply chain is highly digitally enabled. Think about going online to buy a book — the system tells you, “You’re interested in these books; here are seven others you might consider.” It’s transparent and real-time. In healthcare, we collect all our health and safety data, and that team works with it. We have a supply chain team that knows what products they ordered but has no idea where those products were delivered, and definitely no idea who used them or what those products ultimately achieved. This is about an integrated, interoperable, near-real-time flow of data to the point of every decision. Part of this is digital infrastructure — supply chain tools and products typically live in the financial side of the organization, while patient care data lives in the electronic medical record. You have to bring those together. When you do, you know: what products do we have, what patients are we caring for, what care do they need, and what outcomes are we achieving? That transparency across a patient’s full journey of care — from the primary care physician who referred, to the surgeon who operated, to the hospital that provided care, right through to home — and tracking outcomes across that entire journey tells you exactly what patients you’re seeing, what care you’re delivering, what products you’re using, and which products are working best and for whom. When you know that, you can customize product and care delivery to what’s going to achieve the best outcomes for each individual. That’s where supply chain creates an integrated digital highway of information that informs every decision, particularly the patient’s own decisions across their care journey. That’s the real opportunity, and it’s another area where I don’t yet see AI being applied — but where I see it as a phenomenally big opportunity to advance.

Rohit: Very interesting conversation so far. Anne, anything you see in the crystal ball for next year?

Anne: With the way AI is unfolding, Rohit, your question might as well be what’s my prediction for Monday. I think predictions are always difficult to make. But I do think that in the next year or so — maybe less, maybe a little longer — health systems are going to start engaging much more directly with patients around how and where they can become very much more part of their own health ecosystem. Patients will be able to have and select the tools that work for them — particularly AI tools — rather than having to rely on whatever they find in the consumer space on one hand, and health systems on the other trying hard to deliver wonderful care but lacking the connectivity they need. I think that engagement, that opportunity to really mobilize these technologies to ensure they are working for people starting with patients — supporting their health, making sure the workforce is meaningfully connected to their patients — is where I hope we’re heading. One of the reasons so many of us go into nursing is to make a difference in people’s lives. If these technologies help us better understand what health and care means for our patients, there isn’t a nurse on the planet who doesn’t want to use that understanding to ensure every patient gets care that is meaningful and contributes to their quality of life. Maybe that’s a hope and a wish more than a crystal ball — but it’s a phenomenal opportunity, and one that HIMSS and many of our colleagues and global members are really driving to advance.

Rohit: Great. Thank you for those comments, Anne.

Ritu: It’s been an absolute pleasure to have you on our podcast, Dr. Snowdon. Thank you so much for being here today.

Anne: My pleasure. Thank you so much for having me.

—

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’ll be your host today. We are really excited to have Naomi with us. Naomi Fried is one of healthcare’s most respected innovation leaders. She’s the founder and CEO of PharmStars, where she’s helping pharma companies engage more effectively with digital health startups to accelerate innovation and improve patient outcomes. Prior to PharmStars, Naomi served as Chief Innovation Officer at Boston Children’s, where she helped establish one of the nation’s leading pediatric digital health innovation programs. Her unique perspective across providers, startups, and life sciences gives her a front-row view into what it really takes to move healthcare innovation from promising pilots to meaningful impact. Welcome, Naomi.

Naomi: Thank you, Ritu. It’s a pleasure to be here. Thank you for having me.

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

Naomi: Sure, happy to share a little more about how I ended up founding PharmStars. My roots actually start out working with digital health startups a long time ago, then advising venture capitalists, and then I was the first Vice President of Innovation and Advanced Technology at Kaiser Permanente. I became really intrigued with how innovation happens in large healthcare organizations. After five years there, I moved to the East Coast and, as you mentioned, became the first Chief Innovation Officer at Boston Children’s Hospital, where we built a fabulous program helping doctors and nurses advance innovation. From there I was recruited to Biogen, and this is the most relevant part of how I ended up at PharmStars. I thought moving to Biogen would be an easy transition, since I’d been in healthcare innovation in large organizations for a long time. But I wasn’t prepared for how different the pharmaceutical industry was. When I got to Biogen, I discovered I didn’t fully understand how the company was organized, and I certainly didn’t understand how this very complicated product — a drug — actually worked. It was a very steep learning curve, and it made me appreciate how hard it is for someone outside of pharma to understand what’s happening inside of it. But I also fell in love with the potential for digital solutions to transform every part of the pharmaceutical value chain, from drug discovery through development, commercialization, and beyond the molecule for patients. That became my life’s mission, and I went on to advise other pharmaceutical companies on digital health strategy. I had a real aha moment, Ritu, when I was working with a large pharmaceutical company. They told me they were looking for a particular type of innovation, and I found a startup doing exactly what they wanted. We got everybody into the room, and we just weren’t getting anywhere. I could see connections weren’t being made, that people weren’t understanding each other. I realized I was watching the pharma-startup gap in action — that fundamentally different way that health tech companies and biopharma operate. That gap is what we set out to solve through PharmStars. We bridge the pharma-startup gap, bringing these two groups together. That was really the motivation for founding PharmStars five years ago.

Ritu: Thank you for sharing that, Naomi — really interesting to hear your perspective. That leads to my next question. PharmStars sits at the intersection of pharma companies and digital health startups, and you’ve worked on both sides — Kaiser, Biogen, and now PharmStars. What in your mind are the biggest misconceptions each group has about the other, and what separates successful partnerships from those that never move beyond the initial stage?

Naomi: Great question. Going back to the pharma-startup gap, the elements of it are that tech startups and big pharma speak a different language. They have very different cultures — I don’t have to explain that — but they also move at different speeds. They have different appetites for risk. They operate in different regulated environments. The real challenge is understanding each other, and to bridge that gap you have to understand where the other side is coming from. That’s why at PharmStars we focus on educating startups about how pharma works. Our program is called Pharma University, and it has two key components for startups. One is literally teaching them about pharma — how drugs are developed, how pharma companies are organized, the regulations they need to be concerned with, their business models. The second is personalized mentoring by former pharma executives. I think that education and understanding of the other side is what really helps build a successful relationship. You also need communication and the ability to connect — those are table stakes. That’s another way we help our startups: at the end of PharmStars, they present to our pharma members and then have one-on-one meetings with them. We always coach our startups that they have to be able to clearly explain what pharma problem they’re solving — stepping into the other side’s shoes is essential to building a relationship and getting a deal done. On the pharma side, we hope they can understand the pressures and needs of startups too. Startups want to move quickly, which isn’t something pharma typically does. They’re not heavily staffed — they won’t have a general counsel, just a lawyer they work with. Understanding that they’re operating in very different worlds matters. But when startups and pharma come together to work on innovation, the patient always benefits, the outcomes are tremendous, and it’s genuinely synergistic. We’re very excited about bringing these groups together and bridging that gap.

Ritu: I think you’re doing excellent work with PharmStars. We’ve actually applied twice ourselves, so we’ve gone through the entire website and understand the program, and it’s absolutely brilliant — really amazing what you’ve put together. We’re happy to spread the word and encourage more people to apply.

Naomi: Thank you. At this point we’ve run ten cohorts, we’re about to start our eleventh, and a hundred and twelve startups have come through the program. Because they learn product-market fit, they go on to be very successful selling to pharma — our first thirty-five startups alone have done over a hundred deals with pharma companies. Another statistic we’re very proud of is that our startups have collectively raised over a billion dollars in funding. Really understanding what your client needs is transformative for a business.

Ritu: I absolutely agree, and that leads right into the next question. We’re seeing huge pharma companies investing heavily in digital health, AI, and patient engagement technologies. Looking ahead, where do you see the greatest opportunity for startups to create value for pharma beyond the traditional patient support or adherence programs that everybody is already doing?

Naomi: Great question. There are opportunities for digital health startups across the entire pharma value chain. In the very early stages of drug discovery, people are applying AI and we’re seeing real acceleration in identifying targets for new drugs. But I think the biggest area of opportunity is still in clinical trials, because that is the slowest and most expensive part of drug development — solutions that can help pharma there are genuinely still needed. If you look in particular at Phase 3 trials, which are the longest and most expensive, finding patients, keeping them in the trials, anticipating issues, and helping with data management — it’s incredibly complicated to run a clinical trial, and that’s where I think the biggest opportunity is. There are certainly opportunities on the commercial side in marketing too, and we’ve seen some really exciting solutions there. But I’m personally most excited about the clinical trial phase, and also what we call the “beyond molecule” part of the pharma value chain — once the patient is actually taking the drug, digital tools that help with adherence or monitoring the condition. There’s a lot of opportunity there. I’m very excited about how digital and AI are helping pharma take what is a very slow and very expensive process and hopefully make it faster, cheaper, and bring more drugs to market more quickly.

Ritu: That’s something we’ve been hearing consistently across all our conversations — everyone is now having to move at warp speed. Even healthcare, traditionally one of the slowest adopters, is now learning to talk in weeks or days rather than years because the pace of innovation is so rapid.

Naomi: In pharma, time is genuinely money, Ritu, because you have a patent clock that’s ticking. If it takes fourteen years to bring your drug to market, you might have only five years left to actually sell it and recoup an investment that’s usually a couple billion dollars. Every month you can bring your drug to market earlier translates into millions and millions of dollars in additional revenue. So for pharma, anything that can shorten that drug development cycle and move the line between developing the drug and actually selling it is really important.

Ritu: When we talk to C-suite leaders across health systems, one recurring challenge is the gap between innovation and adoption — startups have promising technologies, but when they move into clinical settings they often fail to scale. In your mind, what can entrepreneurs do differently to design solutions that fit naturally into provider and patient workflows, especially in a slow-moving environment like Phase 3 trials?

Naomi: I think in the pharma world we also suffer from the problem of pilots that get started but never scale to adoption. This goes back to the fundamentals of the project: are you solving an important problem? Can you demonstrate value? Do you have clear metrics you’re aiming to achieve in the pilot, so you can convince people it’s worth scaling and that broad adoption makes sense? We advise our startups to think carefully about the pilot — to plan and discuss with their business partners what success looks like, and ideally to map out what happens next if the pilot goes well. Leaving the adoption question to a future conversation is not always advisable. Of course you can’t resolve every adoption issue in advance, but anticipating that adoption will need to happen and thinking through how it will happen ahead of time is very advantageous.

Ritu: That’s good advice — thank you for sharing that. Let’s change tracks a little. I would love to hear your origin story — how you came into science and how you landed here. What was that unique combination of skills that brought you to where you are?

Naomi: I studied chemistry as an undergrad and then got a PhD in material science and engineering at MIT. I started working at a startup in Kendall Square back when it looked very different than it does now. I was employee number four at a small startup on the tech side. Our startup was having a conversation with Boston Scientific about a possible partnership, and I found the business conversation so interesting. Even though I’d trained in science and technology, this business side was fascinating to me. When I moved to California for my husband’s job, I had the opportunity to get involved with a startup being spun out of Stanford School of Medicine — in what we used to call the e-health space, even before there was “digital health.” The technology was developed by a physician at Stanford to help doctors and nurses get quick answers to questions that came up while treating patients. This was before Google, before WebMD — a genuinely novel tool. I thought this was really cool — I loved the idea of using technology to help patients and doctors, and that was my real transition into healthcare. From there I worked with startups, went on to Kaiser, and the rest is sort of history. That transition from science to business happened organically. I never thought I’d end up in healthcare, but I fell in love with the idea and became excited about how digital solutions can help patients and doctors. Even now through PharmStars, we see all sorts of digital health solutions — medical devices, software, AI — and there are some really cool ways that patients, clinicians, and pharma can all benefit. I love what I do now.

Ritu: That’s amazing. Before we get to the latest PharmStars cohort, I want to dig a little deeper — when we ask physicians why they became doctors, we sometimes hear the most amazing answers. Did you always know you wanted to study chemistry, or when did that realization come?

Naomi: No, I didn’t know what I was going to do, basically, through most of high school. Then I took a chemistry class, I think my junior year, and I just thought, this is so cool. I fell in love with it — there’s no other way to describe it. I decided I wanted to study chemistry because it was so interesting. I went to UC Berkeley and started studying chemistry, which I really enjoyed, and I got into total organic synthesis — figuring out conceptually how to build molecules on paper. I think I like puzzles and putting things together. I did some lab work, but what I found was that in the lab — which was obviously where my future would be — we were making micrograms of things and then analyzing them, and I thought, this isn’t very practical. I’m a really practical person. I love chemistry, I love thinking about process, but this was getting too esoteric for me. So I took an engineering class — material science engineering — and thought, this is better, this is more my speed, this is applying chemistry to the real world. I got interested in metallurgy and extraction of metals, and that’s what I did my PhD in. So I’ve been on this path of trying to do practical things, and I guess I ended up wanting to help people have better health. That’s where I landed.

Ritu: Thank you for sharing that, Naomi. Now we would love to hear about your current cohort and what the theme is this time.

Naomi: Thank you for asking. We’re on our eleventh cohort now, and we’re open and looking for digital health startups that want to engage pharma as clients. We’re focused on the theme of digital innovations in immunology. These themes are always selected by our pharma members, who meet with startups and hear their pitches at the end of each cohort. Immunology is actually a really broad topic. Pharma is working a lot on immune-mediated diseases — allergy, asthma, and similar conditions — so if startups have digital solutions supporting those conditions, that’s of great interest. Immuno-oncology is also a really important and growing area for pharma, with a lot of unmet needs and digital opportunity. And then there are advanced therapies based on impacting the immune system, which is incredibly complex and affects many organs — so this theme is very broad. Immunology patients also often deal with chronic conditions and flares, so solutions around patient monitoring, patient identification, and diagnosis are all of interest to pharma, and therefore to PharmStars. It’s a very broad and exciting area, and we hope we’ll see a lot of startups apply. Our accelerator is virtual, which means startups from around the world can apply. It’s a ten-week boot camp where startups attend class virtually for most of the program, with an in-person launch event and an in-person showcase at the end. We’ve had amazing startups from about thirty-nine countries participate so far, so it’s a great gateway for international startups to find their way to global US pharma companies.

Ritu: That’s an incredible opportunity, thank you for clarifying that. I’m sure we have listeners from all over the world, so hopefully this reaches someone who applies. Let’s go back a bit to your time at Boston Children’s and Kaiser, where you were evaluating and scaling a lot of healthcare innovations. What lessons from that experience still guide how you assess startups today? And what qualities do you think the most successful healthcare innovators share?

Naomi: That’s a big question. At Kaiser Permanente, my team was innovation and advanced technology, and we were identifying emerging technologies from outside Kaiser and assessing fit with the organization. It was very important there to understand what doctors were actually looking for — to make sure solutions worked well, solved the problem, and didn’t cost the doctor more time. They needed to work within the clinician’s actual workflow. When I moved to Boston Children’s Hospital, our innovation acceleration program had a different mission — to enhance the innovation culture itself. We were helping doctors identify problems and actually building a lot of the solutions for them. We had a digital health innovation lab and funding to support it, and we did a lot to help doctors develop their own ideas, which was really exciting. There were also a lot of startups at the time who wanted to bring their innovations into the organization, and one thing I always advised them was that they would need an internal champion. Navigating even a hospital like Boston Children’s — which isn’t the largest in the country, but is certainly complex — requires finding someone inside the organization who wants to use your solution, who will be your champion and guide you through getting funding and approval, and help your product actually get used and tested. That was really important advice. To answer your question about what qualities successful startups share — the theme throughout is really understanding your client’s needs and building a genuine relationship with them, with very open communication. Listening matters enormously. Startups are very excited about their solutions, and they’ll go into a meeting wanting to do all the talking and all the pitching — but it’s really important to listen first. What does the client or potential customer actually need? We coach our startups to do a lot of research before that first meeting — to understand, in the case of pharma, what their pipeline looks like and what their pain points are, and to speak directly to that. Beyond listening, deals are relationships. The startups that are really successful understand that and build relationships that go beyond “here’s the solution, how much will you pay me” — they ask, how is it going, how can I help, what will make this successful for you? We really advise people to build relationships. The successful startups I see listen, they have a solution, they step into their client’s shoes and talk about their client’s problems, and they build genuine trust. People want to work with people they trust. People want to work with people they like. Beyond the obvious things — passion, great communication skills, love of technology — I think it really comes down to your ability to build a relationship and connect with the client.

Ritu: Great advice, thank you for sharing that. We were at another accelerator program recently, and one of the key insights they offered was that most successful startups have pivoted at least once. Has that been your experience as well? Because listening, as you said, and pivoting seem closely related.

Naomi: I think that’s exactly right. If you’re listening, chances are you’ll hear how to change and improve your product. We get a lot of startups into our program who are already selling their solution successfully elsewhere in healthcare — to providers, to payers — and they believe there might be a pharma opportunity, and we agree with them, which is why we accept them into the program. They’re not really pivoting their product, because the product is already built, but they are pivoting the application — how they package it, how they explain it, and what problem they say they’re addressing. I’d say maybe forty or fifty percent of the startups in our program aren’t pharma-first. They’re coming from elsewhere, but they’re recognizing that pharma needs their solution too, and that’s what we help them with — understanding the pharma industry, positioning their solution, and successfully selling it. So pivoting is just part and parcel of this. I’d be shocked if the product and the explanation any startup started with is exactly where they are a couple of years later, assuming they’re still in business.

Ritu: Thank you, Naomi. As usual, the time has flown by and we’re almost at the end of the podcast. Thank you for being our guest. Would you like to offer any closing thoughts for our listeners before we wrap up?

Naomi: Well, Ritu, first of all, thank you for having me on The Big Unlock — this is an awesome podcast and I’m honored to have been your guest. I just want to again invite startups to apply for our upcoming eleventh cohort. Our virtual accelerator will run in the fall of 2026. We’ll accept ten startups from around the world with a focus on digital innovations in immunology. It’s an opportunity to learn about pharma, to be mentored, and we also invest in our startups through the PharmStars Venture Fund. It’s a great accelerator, and I appreciate the opportunity to share it with you and your audience today.

Ritu: Great. Thank you so much, Naomi.

Naomi: 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.