The Exchanges

Q And so the, the idea of the network is that some of this is shared? So some of the data is shared for research purposes?

A Yeah, so if you, if you work with Flatiron as a provider, so if you are a cancer center, um, one of the things you agree, in addition to kind of sending us your complete copy of your electronic health record, all the documents, we, we see everything, um, on a nightly basis, you agree to allow us to process and de-identify Your data so that we can aggregate it amongst the broader cohort. And in return, we are kind of providing folks with value props from the network itself. So you could think of things like benchmarking or the ability to do research on broader cohorts that any one single cancer center might not be able to do. They only see, you know, 3000 patients a year. We see a little under 700,000. So there are, Fred Wilson writes the best kind of network build out. Which I really, I really love. He talks about the single player use case and the multiplayer use case, and that's what we have. We have a, a single player use case, which is, we help you use your data better, and then we have a multiplayer use case, which is, oh no, by the way, now we've got this network behind us. Here are values that you can derive from the fact that there's, you know, 2000 other doctors also sharing their data. And that's the, the single player, multiplayer, um, game.

AI assessment note: “you agree to allow us to process and de-identify Your data so that we can aggregate it”

Q So I assume you have to, you know, a little bit to Mark's presentation in a slightly different field, although I guess there is a big genomics component to all the cancer data, but, um, so do you see a lot of stuff like that, like the, just like crazy dirty data in lots of different formats?

A Yeah, I would say, we talk about all the, the fun software, but the reality is we spend most of our time cleaning data. Um, it's like very non-glamorous, uh, kind of work and infrastructure. Um, so this is real-world data. This is a, this is the equivalent of, like, what the doctor used to write by hand in a chart, but just now typed in a chart. Um, but the amount of kind of structured, normalized data that we see at the source is extraordinarily limited. Um, we see lab values with different units of measurement. We see sometimes scientific names for drugs versus generic names versus, you know, um, brand names, for example. Uh, so on the structured data side, there's this huge, what I would call, kind of, data normalization effort. Um, that we have behind the scenes, so we, we map. Um, much of this is actually done with people. Uh, and then we have this really unique infrastructure around unstructured data, which is all the notes, the pathology reports, where the genomic data actually ends up showing up. Uh, you know, you get a report, and when, when I say get a report, what, what that typically means is the report is faxed to you as the physician, and you, you read it, and then you, you hand it to your, your, uh, medical records person, and they scan it in. And so we see those. They're, you know, you can tell they're tilted, and sometimes there's like a crease down the middle,…

AI assessment note: “the reality is we spend most of our time cleaning data.”

Q And how does one even start something like this? So you, you show up, say, hey, you're gonna, you know, a little bit, and then who are the first, and how do you start?

A Uh, so, you know, our tip, our, our tip and trick for this was always just to ask for advice. Um, pretend like you're not actually selling something so people enjoy giving feedback they don't like being sold to. And so we started coming up with ideas and presenting them to folks as, as ideas. Would you, would you give your feedback? And kind of continued to tweak and tweak and tweak, and still doing it to this day, by the way. Um, until we felt like, okay, this is something that we could run with. Uh, and at that point we, we quit Google and, Raised a round of funding, almost from, from you guys. Um, and, ah, and off we go.

AI assessment note: “our tip and trick for this was always just to ask for advice.”

Q And, and I, I realize I said that this was gonna be the last question, but just, just quickly, the, uh, So, so you raised this, um, super large amount, uh, the Series B level, a hundred and thirty million. Can you tell us about the, the thinking behind that?

A Yeah, so, um, so we raised a Series B is a hundred and thirty million, um, and we used some of that money, um, you know, more than 50%, let's say, to acquire this electronic health records company that builds a specialized oncology EHR. Um, and so, it was this really unique process, ah, where we were a 30 person company buying a 55 person company. Um, which, just recipe for disaster, I think, ah, in many cases, and the, the unique thing was that Nat and I had just been through it on the other side. We were, when Google bought our last business, we were about 55, so we kind of knew some mistakes not to make. Um, so we reused it to, to, to acquire this business, which got us to scale, is kind of a good way of thinking about it. Um, we probably shaved like three, four, five years off of our, off of our roadmap. Um, and now it's, you know, we're using the, the extra money to, to grow. Um, and I think it's just a, you know, it, it's, it's a unique industry where if you can break out, the, the opportunities are huge. The, the hard part is breaking out. The hard part is kind of getting, it's getting to scale where people will trust you and believe in you and, um, just like take the meeting. You know, when you email them, will they meet you? That was, you know, one of our biggest challenges in, in the beginning.

AI assessment note: “we used some of that money, um, you know, more than 50%, let's say, to acquire”

Q That's not the story I heard, but. Um, ok, and, ah, so tell us about Oncology Cloud now. What, ah, what does it do?

A Yeah, so I'll, I'll try and set the context, because Flatiron is a super complicated business, and everybody thinks we're always a data company. We're not a data company. Um, so we're, we're about a 160 people. We're based here in New York. We, we've raised about a hundred and thirty eight million dollars from, from Google. We're their largest healthcare investment to date. Um, at our core, we are a network business. And, and what I mean by that is, if you think of cancer care in the United States, uh, if you are a patient diagnosed with cancer, you go and see your medical oncologist. And the information about you and the touch points with you kind of live in these little isolated buckets. You know, each provider, as we call them, is its own unique little silo. They do something to you, they capture some information about you, and then they go see the next patient. And our thesis was always, if you could connect these disparate hospitals, if you will, these disparate cancer centers on a common technology platform, and that means both Data, as well as touch points with those doctors. So it's not just data. It's also the fact that we, we have touch points with doctors. There were a lot of really interesting things you could build on top. Um, and so when we think about Flatiron as a company, the foundational layer are things we sell to cancer centers. It's kind of an easy way to t…

AI assessment note: “if you could connect these disparate hospitals, if you will, these disparate cancer centers on a common technology platform”

Q to the question we were discussing with Ron earlier. Why is it so, why is it so, uh, archaic, like the, you know, state of the industry, and is that, ah, you know, it's easy for us, like we spend so much time in the, sort of, like, internet tech world, and everything. So, you know, and, and is now the tipping point, and why is now the tipping point?

A So I, I may have like a, a contrarian view here, which is that doctors are, are not going to change. Um, and at least not in any sort of timeframe that matters for a startup. Uh, maybe, maybe 1015 years. Um, so when, when we first started doing this, we were doing our, our 18 months of research. I'll give you a little anecdote. You know, for us, we kind of said, well, why don't they just fill the EGFR thing out and just put it in a dropdown. Uh, can't be that hard. You know, we, we do this every day. Um, and then you go to the clinic, and we actually went and we did rounds. I, I probably saw 50 patients or 60 patients. Um, we, we pretended to be, to be med students, I guess. Uh, they introduced, they asked the patient, are you willing to have somebody in there? They say yes, then we, then we walk in. Um, and you watch the doc go through their motions of asking questions and, and trying to understand symptoms and, and issues. And this is cancer, so this is, this is not an easy conversation for, for many folks. We saw Um, some crazy, crazy meetings. Folks relapsing and, and all this. And then there's another patient waiting for them right next door who has the same disease and, and the same issues. And for them to go and add five minutes of structured data entry to their, their daily workflow will reduce the number of patients they can see. Frankly, it reduces the amount of money…

AI assessment note: “I may have like a, a contrarian view here, which is that doctors are, are not going to change.”

Q It's like this very rare form of cancer that I'm gonna see once a year, but the, for whatever reason in Kansas they have it, you know, 20 times a year I'll be able to see what, what they've done, or?

A So, so actually, funny enough, today we've spent probably 90% of our time focused on the single player use case. As much as the multiplayer side sounds like super compelling and interesting, the reality is people just want, like, their problem solved now. Um, and so we spend most of our time On the single player side, I think on the, the aggregate, we are, we, we announced a partnership with this group called the, the NCCN, which is the National Comprehensive Cancer Network. It's 25 of the largest cancer centers in the US, um, where if you contribute your data, uh, you will be able to get the, the broader data set for, for research purposes. Again, all de-identified, anonymous, and, and aggregated, but, um, for a cancer center that sees, 3500 patients a year, you know, this is an order of magnitude. Could be a hundred X what they would have themselves.

AI assessment note: “if you contribute your data, uh, you will be able to get the, the broader data set”