The Exchanges

Q to this, that, uh, Hippocratic is, is pre-launch. Um, you just looking at the websites, you, you, you, you, You cover a broad range of, um, uh, you know, things that go from healthcare administration and helping people just, uh, process, um, operations faster to having dieticians involved. So what, what, what is the space you're operating in and the general problem set that you're looking to solve with LMS?

A Yeah, you know, I saw, I lay out three different kind of, um, uses of LLMs to impact healthcare, you know, and that's why I call it a healthcare LLM because healthcare is broader than just diagnoses, right? It's, it's all the things going on, but there's three use cases. First, let's call it productivity in workflow. That means you're in the electronic medical record. You're helping the doctor write their answer or communication from a patient, something called the in basket, which is kind of their equivalent of an inbox effectively, um, or a doctor writing a note to an insurance company to escalate. A lot of people have come up with these ideas. They're interesting. They're helpful. They're actually probably better suited for the people who sell those software systems today to build in. Um, but they don't change healthcare that much. They maybe make you five percent more efficient, 10% more efficient. It's not clear if you make, um, somebody 10% more efficient that they see 10% more patients, right? In fact, if they're right now spending every evening answering their in basket, which most doctors are, and that's time they would rather have spent with their kids, when they get that 10% back from that time, they spend it with their kids, rightfully so. But that means the system didn't get any efficiency. That means we didn't see more patients and that's, you know, that's what ha…

AI assessment note: “I lay out three different kind of, um, uses of LLMs to impact healthcare”

Q call on what may be happening. Uh, and then, uh, there is a, you know, the whole world of, uh, health Healthcare, management, operations, efficiency, and all the things. Uh, you, you've resolutely decided not to focus on diagnosis. Uh, I'd love to, uh, hear the, the, the rational, uh, why. So, if generative AI changes everything, does it not change that and open up the opportunity to do diagnosis?

A I don't think it's safe enough. I mean, it's really simple in some ways. Like, I think, I don't know why people keep, like, it's, it's like flies to the flame. I mean, they just keep going there. I'm going to do diagnoses because it's grand or it's challenging, but I mean, they're going to kill somebody. I'm like, this is really serious. Like, I don't want that to be my legacy. Like, so my view was, you know, and they've not done the math. Healthcare is 4.2 trillion dollars. Let's take all the spend that goes to doctors and take that as a proxy for diagnoses, right? It's not perfect proxy, but it's a decent proxy. That's only six hundred billion. Okay. Where's the other 3.6, I mean, 3.6 trillion? Like, let's go solve that. They're like, oh, let's do drug design. Drugs are not that much either. Right? So, I mean, there's, they're a big chunk. Maybe I think there's 400,000,000,500 billion. I forgot the exact number, but it's in that range. I'm like, look, there's so much more. There's another three trillion dollars that's non-drug, non-diagnoses. Let's go make that part of healthcare work. And it's not like the country is short making the right diagnoses. Our biggest issue in the U.S. and most of the developed world is the people don't follow the directions after they're diagnosed. Like, it's not an issue of diagnoses. It's an issue of adherence. It's an issue of ongoing attentio…

AI assessment note: “I don't think it's safe enough. I mean, it's really simple in some ways.”

Q Fascinating. So as you build the product, um, are you thinking, uh, or are you building a co-pilot, uh, kind of experience for the staffers, or are you, um, eventually, uh, also creating those, uh, super staffers as independent AIs that will be, uh, disconnected from any kind of human?

A We think all, all of the, um, leverage only comes from creating kind of the automated bot, but with the right kick out. Um, and, uh, but I don't know that we won't do a transitional strategy with a copilot, but I, I think of the copilot is different. There's copilot where you're helping the nurse. Right. And telling her, Hey, say this, say that, do this, do that. Then there's copilot. That's like a three-way call. Why don't we just have it on the line? And when the nurse talks to the patient and says, Hey, do you need a ride to your appointment next week? Oh yeah, I do. I, I don't have a way to get there. Well, which is a major barrier of access for healthcare in, in, in the country. And so great. Then the LLM pipes up. Maybe the LLM was introduced at the beginning. The, the nurse introduced the LLM said, Hey, here's my LLM, Rachel. Or, you know, if you want to say that or say, here's my virtual assistant, Rachel. She's here on the line. Rachel goes, hi, how are you? I'm here to help out with anything. And then while we're talking, oh, you need a ride? Then Rachel pipes up and goes, hey, you need a ride? You know what? Why don't you guys keep talking? I'll call a bunch of transportation providers right now and see if I can get you a ride. And then two minutes later, because she could parallel dial five of them, right? And she comes back and says, hey, I found one. They can't pi…

AI assessment note: “We think all, all of the, um, leverage only comes from creating kind of the automated bot”

Q In a world where everybody's imagination has been caught by a chat GPT, are you finding that the healthcare regulators, broadly speaking, are excited by AI, scared about AI? Are they knowledgeable about it? Do you have an opportunity to engage in constructive conversations? What's the general vibe, I guess, for lack of a better term?

A Um, I think it's, uh, my observation so far is they're on it. Um, they're focused on it. They're thinking about it. Um, but rightfully so from what I've seen, they're far more on it for, um, diagnoses. Right. I mean, almost all your AI regulation to date from the FDA has been on diagnostic products. Um, and so, uh, you know, again, yet another reason not to do diagnoses because there is going to be regulation and it is going to be important. Um, uh, and that probably should be slower. So, I mean, that's what I'm saying. I think there's, You know, there are areas where we can add value and we can change healthcare and we can lead to better outcomes and we should do those. And there's areas that are just going to take more regulation, need to go slower. We need to maybe even have advances in core model development, um, to take hallucination rates much lower, like, you know, before we utilize it for that. And I think people have, have to just accept that, You know, there's, there's, there are better and worse applications for each technology even within a vertical.

AI assessment note: “my observation so far is they're on it. Um, they're focused on it.”