World Health Day Special: How AI Is Making Healthcare Smarter, Cheaper, and Kinder
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This is Dan Turchin from AI and the Future of Work. I've digitized my voice with the help of 11 Labs. For today's special episode, we're experimenting with commentary from DigitalMe. The RealMe approved the content and of course approved the digital twin. Let me know what you think. Welcome to this special World Health Day edition of AI and the Future of Work.

Observed each year on April 7th, World Health Day is a global initiative designed to highlight pressing health challenges and inspire solutions that help everyone live healthier lives. For artificial intelligence enthusiasts, it's also a moment to consider how advances in AI are reshaping healthcare, from reducing costs and improving access to freeing up doctors for meaningful face-to-face patient care.

Today, we'll revisit four exceptional conversations with visionaries who are leading this transformation. You'll hear insights from Bo Hartman, Hunit Soni, Dr. Brigham Hyde, Dr. John Halamka, and Dr. Shiv Rao, experts at the intersection of clinical innovation and emerging technologies. They're not just leveraging AI to optimize workflows, they're reimagining what it means to receive care in a fast-evolving digital world.

Let's dive in and learn how we can prioritize better health outcomes, better patient experiences, and system-wide improvements in the age of AI. Enjoy the episode. Let's start with a broad view of the systemic issues in healthcare. Our first guest is Beau Hartman, co-founder and CTO of Nomi Health, who left a successful career in finance, including senior technology roles at Goldman Sachs and Barclays to transform how health services are delivered.

Listen closely as he shares why data and real-time analytics are so crucial to fixing our outdated system. Yeah, so in the United States, for those of your audience who's not in the United States, we all have stories, healthcare stories in the United States. And we all can cite them where we have the best healthcare providers in the world because I've lived internationally. And I would tell you that we have the best doctors, nurses, clinicians,

we have the best. The problem is access to that care. And there's multiple ways we can talk about getting access to that care or leveraging new technologies to get that care.

But when I made partner at Goldman Sachs after building Marcus by Goldman Sachs and building Apple Card, a dear friend of mine named Mark Newman, who had founded a company called HireVue, who you got to meet, came to me and he said, hey, there's this thing in the United States where if you pay a doctor directly, it's 30 to 50% of the cost. The problem is,

those payments aren't efficient. They're not digital, they're not straight through processing. There's a lot of calcification around there. And I had heard about that. Couple that with the story, everyone has a healthcare story. I had to experience that through my life and my friends experienced that life. And so seeing those two problems of if it's a calcified industry, that's not leveraging technology, but we have the best in the world.

How is this something I could pass up? And so the adventurer in me said, well, maybe I can make a difference because I understand payments. I know how to make markets efficient. I know how to leverage technology.

Let me go take a shot at this. So at the ripe old age of 50, I decided that I want to go do a peer startup. And so that's what happened. I decided to turn my back on that seat at Goldman and put my lot in with trying to help. From our perspective, we believe if this country fully adopted the anomie approach to paying in real time, paying for the real cost of health care, not the imaginary cost of health care.

using data analytics to ground decisions in fact and truth.

And enabling providers to do what they went to school to do is practice healthcare and actually reduce the overall cost to the self-insured employers and the government to reduce that overall cost. This is what we believe will be the outcome. One, instead of the healthcare costs going from 4 trillion to 5 trillion to 6 trillion as is projected, we will actually return a trillion dollars back to the economy.

That's one. Two, anybody will be able to shop for healthcare, meaning they can go to any doctor that they want to. The doctor will get the fair market price for their work, but you'll get to choose your provider and you won't be constrained in by these falsely developed contract networks, right? And the organizations that purchase that healthcare, right, will be able to provide

benefits so there won't be a deductible coming out of the employee's paycheck or there won't be a copay. Because all that does is turn the doctor into a collections agency, right? That'll reduce the overall bankruptcy rate because of medical costs. People have access to that care, right? And you'll be going to the same doctor, same care, less price. The last big piece that I really can't wait to see that day is that, and at that point, what we're seeing is

What will happen is because we're using data based on how these organizations provide the benefits to their employees. Bo made it clear, we need technology and healthcare to fix outdated workflows. Now let's go deeper. Puneet Soni, founder and CEO of Suki, explains how his voice-based AI assistant dramatically reduces the time doctors spend on paperwork, bringing them to focus on patient care.

Prior to Suki, Puneet led product teams at Google and Flipkart, and he brings decades of experience deploying cutting-edge tech to enhance user experiences. Yeah, I mean, I think the way to do it is to reverse the amount of time that folks are doing that's not clinical care. The vision of Suki is to make healthcare tech assistive and invisible so that a clinician can focus on what they love most.

which can, by the way, be taking care of their patient, but also could be just going home or spending time with their family. And then if you look at the amount of time they spend, they roughly spend like 30 to 40% of all time outside of clinical care just documenting.

And then they spend probably another 20-25% time like orders and all of the other things they have to do there. Then they spend another 10-15% of time doing data retrieval. Hey, what information do I need to get? And there's six clicks and seven drop downs before you figure out what vaccination somebody's taken.

And then you're spending a lot of time actually just getting contextual information that you may not even be looking for that, but you know that you probably want to find and so on and so forth. And so if you build an assistant, which starts by actually doing clinical documentation, then the inherent act of creating the document that represents the encounter, patient encounter,

creates the structured data that's used to generate models that can train models that can then solve other problems along the way. And so Suki does clinical documentation. Then it started actually doing coding, which is how doctors get paid.

Then it started actually providing basic Q&A. What medications is Puneet taking? What is A1C level? What are his vital signs? Then it starts getting much more fluid where you can basically just start saying things like, okay, I'm, you know, plot the A1C level for Puneet over the last three months and give it to me. Or what's the FDA recommendation for this particular patient?

And then you start actually doing patient summarization where as before you walk in, you say, well, what should I know about that? And it actually provides you a summary of that. Then you start like staging orders into the thing. Then and so on and so forth. And you get to a world

where there is this assistant that you have with you that actually can tell you what your day looks like, who should you be looking at, what's the summary of the person you're going to see next. You can ask it to pay attention so it can write a note, put together orders, do all the work that you're doing in the act, and then also provide you all the other contextual information to operate. And suddenly, you have it always with you and you are focused on empathy and clinical care and everything else is being taken care of.

If that's actually going to happen, I think it will happen in the cusp of AI, user experience, language models in healthcare. And I think it's somewhat inevitable that in the next 10 years, we're going to see pretty much this is the way doctors are going to operate, which is a very different world from the world they came from, just like we, internet and pro, post-internet, were very different worlds. So we've heard...

how AI can offload the doctor's burden of endless typing and documentation. But what about harnessing massive amounts of real-world patient data to improve diagnoses? Meet Dr. Brigham Hyde, CEO and co-founder of Atropos Health, and Dr. John Halamka, president of the Mayo Clinic platform.

Dr. Hyde has built and led several healthcare data ventures, while Dr. Halamka has spent over two decades pioneering secure data-driven transformations in care at Harvard Medical School and Mayo Clinic. They'll explain how they're turning past patient encounters into valuable clinical evidence.

Yeah, I think this point really resolves around the issue of evidence. We talk about evidence-based medicine. When you go to your GP, there's probably clear guidelines for what they're supposed to do, whether it's the differential diagnosis or it's choosing a therapy. And if you think about where those rubrics come from, they come from evidence produced by clinical trials.

And clinical trials are the gold standard, and I think always will be. However, you also have to acknowledge the fact that most patients would have been excluded from most clinical trials. The estimates are about 70% to 75% of the population would not meet the inclusion-exclusion criteria of those trials.

Not to mention the fact that how we decide what trials to do is often driven by things like drug development and other elements. So we're systematically excluding most of the patient population to develop this evidence. And by the way, there's good and bad reasons for that. I'm not sort of yelling at the sky about that situation. It's just the reality.

And so our belief at Adderpo is this problem can be described as the evidence gap. There's just not enough clinical trial evidence to inform personalized care. And that's when we need to turn to observational research, patient-level data, and

And our point is, let's find a way to deliver that personalized evidence very rapidly again to the point of care. So back to your GP. When you come in, and when I talk to friends and associates outside this industry, they sort of can't believe this is true, but they're not even attempting to personalize your care decision.

And if there happens to be a guideline gap, like maybe there was no trial for a specific situation you're in, they really have nowhere to turn to. And I find when we talk to physicians, they're relying on their knowledge base, but they're essentially extrapolating from experience to determine what should happen when those gaps exist.

Our argument is that doesn't have to be the case. We can personalize that, we can localize it, and we can respond in time to those needs. So I think that's the core of it. We just don't have enough evidence. I'll make one comment about chat GPT and LLMs related to this.

If you think about using that technology today to answer medical questions, and there's a recent publication by a colleague, Morgan Sheetham, where ChatGPT actually passed the Step 2 medical exam. So you're thinking maybe it's getting smart enough. The reality of that, though, is that's relying on all the published evidence, which we just said was incredibly insufficient.

So there's no way for the LLMs, no matter how good they are, to be able to answer all the relevant medical questions if they don't have access to more evidence. So our hope is to raise the corpus of not only content that can be out there, but also enable that personalization element.

Now that we've explored how data insights drive system-wide transformations in healthcare, let's turn our focus to how generative AI can transform real-time clinical documentation. In the following segment, Dr. Shiv Rao, a practicing cardiologist and CEO co-founder of Abridge, describes how automating the note-taking process helps doctors reclaim precious minutes and ultimately deliver a more human and empathetic patient experience.

I actually think that AI actually is making, in some cases, like with a bridge, healthcare feel more human again. Because what it's doing is it's pushing value up the stack, where what

What really matters is that bedside manner. What matters is conversation, being as complete, as thorough as possible, being present with your patient, really helping them totally grok and understand and make decisions together, do shared decision making such that they can be the healthiest versions of themselves. I think AI can actually unlock more of those quote unquote value-based revenue models where

actually accrues to those who actually deliver the best possible experience in healthcare, as well as outcome. So I think that they're not...

necessarily in conflict with each other. I think that AI can actually make healthcare what it used to feel like or what we idealistically always wanted it to be like. But I relate to what you're saying. A quick story. When we started the company in March of 2018, I saw a patient in my clinic. And this was just as we were starting the company. And she gave me so much conviction. This was absolutely the right thing and the most important thing I could dedicate myself to.

She had a 10-year history of breast cancer, and she'd come in for preoperative cardiac evaluation. So that's where you see a cardiologist just for a rubber stamp on being able to have a certain chemotherapy or certain surgery. And she was about to start a new chemotherapy. We had a conversation, and I could tell throughout she was really, really anxious and nervous, like, what's going on?

falling out of her skin, kind of uncomfortable. And I couldn't tell why. So I asked her towards the end, I can tell you're uncomfortable. Was it something I said, something I did? And she told me that for the last 10 years at that time, her husband had come to every single visit with a doctor except this one. He just couldn't make it. And I asked her, well, what does he do that's not obvious? And she told me he sits in the corner. He's quiet. He just takes notes.

And she's an English professor, incredibly eloquent. She told me that him taking notes meant that she could feel present, be in the moment with her clinician, knowing full well that even though she would probably forget, and there's data to suggest people forget up to 80% of what they've heard from doctors and nurses, it's just how we're wired, that she could go home later and unpack those notes, rewrite them in words they understood, and then go to the next clinician and feel like the main characters of their story as opposed to someone looking in from the outside.

And so the needle we're threading here is that when a bridge is a part of the conversation, we can create that value for the clinician first here. We can really focus on helping unburden them so that they can focus fully on their patient, have the best bedside manner, converse knowing that they don't need to be distracted with all the clerical work because we've taken that off the table.

But then what we can also do is take something else off the table for them, which is an extension of their best intention to be there for their patient, even when they're not in front of them. They don't need to sort of

leave their patient and worry, did she remember what I just said? Did she remember the metaphor I gave her about the heart being like a house and how this chemotherapy is going to be something that could affect the pump? They don't need to worry about all those things because when we're a part of the conversation, we can create, in addition to the clinically useful and billable note, we can create a summary for the patient.

And that summary is sort of souped up. It's got the machine learning steroids in it. It can nudge her, the patient, in a week or in two weeks, reminding them, unpacking big words at a fourth grade reading level, helping really translate all the jargon and all the process. And it can, over time, be a kind of care manager that sort of guides her through all of her next steps.

So, I think in this world that I'm describing, I think it's a bit of both what you said. There is AI here that's clearly doing a lot of heavy lifting for everyone involved, but it's also, I think, creating a better human interaction at the same time.

This can also pose kind of an existential threat in the medical community. How do you get doctors who've spent 12 plus years going through medical training to A, trust AI to summarize these important conversations, and then B, that the logical extension of this AI incursion into this private patient-doctor conversation space doesn't ultimately edge out the need for a human doctor?

Well, I think it's time horizons here that we're talking about. And I think in any nearish term time horizon, AI being able to replace doctors is something that I don't believe is going to happen anytime too soon. I think that it will assist them. Absolutely. It will augment them. And I also think it's going to automate a whole bunch of workflows like the ones that Average focuses on.

But AI being able to sort of deal with that patient with a 10-year history of breast cancer and guide her towards the right chemotherapy and then sort of be there for her, supporting her, not just medically, but in all the ways that the best clinicians do as she navigates that potential lifelong journey with cancer is not something that I think a machine is going to be able to replace anytime soon.

And I think if you were to survey a whole bunch of clinicians, or I should say consumers out there, I should say patients, if we were to talk to a whole bunch of people, and if we were to ask them, hey, would you want to get on a plane that's autopilot with AI, or that also includes a pilot who's using autopilot?

Everyone's going to prefer the latter, I think. They're going to want that human backstop, that expert who can leverage AI. And so I think the refrain that most many people are increasingly embracing is that doctors who use AI are going to replace doctors who don't. And I think that's what we're going to see, at least in any time horizon in the near term. ♪

As we've heard today, World Health Day is a call to action for all of us who believe in the transformative power of AI to improve patient outcomes, whether it's streamlining paperwork to free up doctor's time or using real-world data to tackle complex diagnoses. The future of healthcare depends on how we harness these technologies responsibly, ethically, and with empathy at the core.

If you enjoyed this episode, check the show notes for the full versions of each conversation. And if you know someone who'd appreciate these insights, feel free to share. Who knows what new ideas it might spark? Thank you for joining this special World Health Day edition of AI and the Future of Work. Until next time, stay curious and stay healthy.