Cheaper medical AI needs workflow integration to cut costs

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 43:39

   just the cost of intelligence as an input to medical intelligence coming way down. And, you know, at some point it'll be too cheap to meter. I spent a lot of time and we as a team spent a lot of time trying to understand how these technologies will make an impact on our healthcare system that is inflating well ahead of inflation. 5.3 plus trillion we spend on healthcare. If it were its own country, it'd be the third largest GDP.

2. 44:07

   It's the biggest part of our economy. It's also the biggest employer in our economy. 23 million Americans work in healthcare. There's so much, there's just so much we spend on healthcare. What do you sort of make of the work that you're doing and the impact that it'll have on the cost of care? I think this is like one of the most important ways that we can pursue our mission is to not only make our models better at the frontier, but make that widely accessible to everybody.

3. 44:31

   So that's, you know, obviously a big part of how we've been able to deploy health intelligence to a lot of people across the world for free. I think it's also a big part of how we think about, you know, API and how good our models are there for health and pricing there and stuff like this. You know, one recent update was we recently released GPT-6 Luna. and And GPT-6 Luna is our current cheapest model on the API, or cheapest frontier model on the API. And what we found with GPT-6 Luna was that it was actually doing better on our benchmark HealthBenchProfessional, our current latest benchmark of clinician workflows that are really difficult, selected for difficulty for our current models across all the different ways in which clinicians are using our models and the models still could use some improvement.

4. 45:18

   it performed better than 5.6 soul which was literally our best model like a month ago and so and it's it performs better despite being 34 times cheaper and we've had a couple of those moments even even in the past year and you know there's been many trends showing that ai compared to other technologies that have been introduced in the past has been reducing a cost at a given level of kind of capability or usefulness faster than many of these other technologies and so one bet we've had at opening eyes that the cost of health intelligence will will plummet at a given level of health intelligence and the the level of health intelligence where you know clinicians and patients feel like they can trust in the care journey is starting to get there right so that's like a fixed level of intelligence in some sense hopefully the bar after that raises over time as

5. 45:59

   well um my expectation is once we get close to that level of intelligence the cost for that level of intelligence also dramatically reduces like it has been as the hope there is that we get like a kind of this commoditization of health intelligence, right? We don't want to hoard that health intelligence. We want everybody to use it. So I hope that that contributes. But, you know, to answer your question about the whole of healthcare spending, I don't think all of that is about health intelligence.

6. 46:23

   I don't think like health intelligence has had yet a macro effect on the problem that you're describing. If you look at like the whole of health, there is quite a bit of areas though, where if you look at, for example, chronic care management or documentation or administration, or a lot of kind of administrative workflows of various kinds, back office workflows, there's a lot of money spent in ways that probably don't need to be spent that way.

7. 46:46

   And so we are very optimistic about kind of an overall compression of spend over the coming years. I don't think that's happened yet. And I don't think that just happens from making health intelligence abundant. I think that happens from integration and workflows and deployment and scaling that up and really making sure that these models are valuable in the economically valuable tasks that people are using them for.

8. 47:04

   But I think it's a prerequisite to making that future happen. More care, less paperwork. is sort of where we're heading. At least there's a lot of the cost savings. I'm sure you saw some of the headlines over the last few weeks of, there's a New York Times article that basically provider AIs and payer AIs are fighting. And there was some Blue Cross Blue Shield Association analysis that showed that the respective billing bots and paperwork bots effectively increased spend by a billion dollars or something like that.

9. 47:35

   When you are doing cutting-edge research on health and trying to bring down the cost of medical intelligence to near zero, and then you see headlines like that. What do you make of that? I think this might be one of those areas where there might be some kind of short-term pain around this kind of provider-payer-arms race, but in the long run, there's no reason this has to exist, right? And so really, I think, you know, you're entering a world where a lot of visits actually have some kind of ambient intelligence in the room, listening, transcribing, things like this.

10. 48:05

    There's notes, there's clinical notes, it's just AI details of clinical notes and things like this. And so you have a lot of the details you need to adjudicate visits, right? And so the ideal world, in my view, is a world in which providers and payers kind of have a handshake, like, hey, here's our ground truth of what happened in this visit. Like, here's all the information we have. There's just like no other source of information that there is.

11. 48:26

    And then an AI helps adjudicate that situation. And maybe there's like some limited appeals process or things like this, but I don't think you need to have all this process where you have like an AI arms race between pairs and providers that can just be completely avoided. So I view that as a bit of a short-term distraction.