A Product Market Fit Show | Startup Podcast for Founders
00:10:13 · Rahul Shivkumar
[00:10:13] S1: Yeah, both Varun and I actually took a little bit of time off and by time off, I mean maybe like three or four weeks where we were like, hey, we're not, we're not going to talk about work or maybe not even like going to like talk to each other that actively about anything other than sports or, you know, nothing work related. And the main reason for that was like, I think both of us wanted to really think through, hey, one, do we actually want to go do another company, right? Like I think the, Dawn was like the, obviously the first company that we did and it was a reasonably like intense like experience, right? So it was like, hey, are we, are we ready to go do this again? Do we maybe want to look at even industries outside of healthcare? Do we want to, you know, like interview for jobs, right? Like I wanted to kind of like have all options open and like truly think through like, hey, what was it that I wanted to do next up? And I think when we reconvened, it was, it was sort of funny that we both came to the same conclusion, which was like, there were a set of problems that I think we understood really well because we did Dawn and it makes sense to like kind of double down on, on knowledge that maybe we had that other people didn't have. It seemed like that there was a huge technology wave that was about to happen. I actually think we were late. It had already kind of like started happening with GPT-3. Like, so all of those led us both to the conclusion that we should stay in healthcare. We should start a company around, you know, this new wave of technology that's enabling value creation at maybe a level we've never, at least I haven't seen in my like career before. It kind of made sense to go do another company.
00:11:36 · Pablo Srugo
[00:11:36] S0: And how do you get from that to the specific problem you chose? What did you do? Like a rigorous, like here are all the problems that we've noticed, which ones we go after? Was it more of a gut instinct that led you to just the specific thing you're solving?
00:11:47 · Rahul Shivkumar
[00:11:47] S1: We did a couple of things. I think we did a, what are things we know well that maybe other people don't know? And then we did another market sizing, right? Which I think is the, is a classic thing they teach you. Neither Varun or I went to business school, but it's a classic thing they teach you in business school, right? Which is like, hey, look at the TAM, look at this, look at that. And I actually think we did this thing where we followed, I think, like Peter Thiel's like recommendation from way back in the day, like in, I think one of his books, Zero to One. He had this thing where it was like, hey, you go chase like the really big market, which in our world is like in healthcare, it's like revenue cycle, right? Where there are tons of companies doing that. It was like, it's going to be like very competitive and if there's a technology shift, like incumbents are going to, are going to try to like capitalize on it the most, right? So it was a little bit of like, hey, what's actually like maybe a space that's not very competitive where we have a lot of knowledge, but we think is adjacent to a bigger market or is in a growing market, right? And for us, it felt like the front door of RCM, which is what we're doing today, provider onboarding, credentialing, peer enrollment, felt extremely underserved. It felt right for disruption. It felt like we understood it really well because this huge problem we had.
00:12:59 · Pablo Srugo, Rahul Shivkumar
[00:12:59] S0: And when you say the entry door, so RCM, like revenue cycle management, this is how like all the billing works, right? Like you go in, you see a doctor, doctor goes out, bills you, but the way that the insurer actually pays that doctor later, that's kind of RCM? [00:13:12] S1: Correct. And it's very complicated, right? Because there are all kinds of rules when it comes to revenue cycles. So I could go into, as an example, I live in New York City. So if I went into Mount Sinai and I had a primary care physician see me, my particular rule set might be the insurance company will pay 75% of what's due, but I, Rahul, have to pay 25%. So the revenue cycle department has to have these rule sets of like, I have to collect $25 from Rahul, I have to collect 75 from Blue Cross, they have to pay me out like this way on this date. And like, you can think of that for complicated procedures, for different insurance companies, different types of plans, HMOs, PPOs, it gets very complicated very quickly. So that's the big elephant in healthcare, right? When you look at where a lot of the dollars go into, it's like on the revenue cycle side. [00:14:01] S0: But your point was that that part of it was very saturated, a lot of big players in there. The first step is you got to get a doctor onboard it so that the whole RCM stuff can start. And that's where you decided to kind of begin your journey.
00:14:01 · Pablo Srugo, Rahul Shivkumar
[00:14:01] S0: But your point was that that part of it was very saturated, a lot of big players in there. The first step is you got to get a doctor onboard it so that the whole RCM stuff can start. And that's where you decided to kind of begin your journey. [00:14:14] S1: Exactly. Yeah. And it was a problem that again, when we evaluated solutions at Dawn, this is not something when you're running a clinic, you want to build in-house, right? Like, these are things that if you could buy versus build, you'd always pick kind of like buying. And on the revenue cycle side, there were a lot of good solutions that we could buy. But on the kind of like provider onboarding site, there weren't any. Like, we actually have to staff people internally for it. [00:14:39] S0: This is a tangent, but because you touched on it, like, build versus buy. That's just changing in such a big way. I don't know that anybody has the answer, but because you just mentioned it, you said this is not something that people would want to build. And in the past, I was just thinking face value. Why wouldn't people want to build this today? And obviously, maybe some people are not tech forward, these sort of things, but like, assume all this stuff gets easier, cloud code gets easier, more, it penetrates more, like, why not?
00:15:04 · Rahul Shivkumar
[00:15:04] S1: Yeah, it's a great question. I was referring it to a time when we didn't have this tooling, so no sense to build at all. I think right now, there's an argument for maybe some of these workflows it will sense to build. But the big thing, I think, with a lot of these AI agents that are promising outcomes, right, that are telling you, hey, we can get a resolution. As an example, I think there's a bunch of customer support tools that say, hey, we want to build you by resolution because we're very confident. I think to have that level of confidence, there's a lot of evals running in the back end, but there's a lot of edge cases being tested. I think going from zero to 80%, cloud code makes it significantly better. But the last 80 to 100 is where I think a ton of the value is today, right? Like, for us, if our enrollment workflows were only 80% accurate, like, one out of five of your doctors wouldn't be a network, right? Like, and that is a large amount, right, for an organization that could rack up in missed revenue very, very, very quickly. So I think, like, we, like, from the size of our engineering team as an example, like, we need that many people to make sure that what we're building is actually tested for edge cases. We're constantly updating it with edge cases that are coming in. And as new models come out, we're evaluating what are they specifically good at, what are they not good at, right? Like, we're constantly testing these things that I think in very, like, high, reliable, like, systems, like, the build option is so difficult because you have to account for a lot of the edge cases and a lot of, like, the last percentage, like, accuracy that you need.