![]() ![]() I don’t know if you guys get jealous about that! But there are other players obviously in the game who are thinking about what they could build and what they could do with this. But obviously there are other companies, and another one of your colleagues, competitors, Komodo Health, raised a big chunk of change as well last week- Slightly more than you did. So this idea of putting together data and selling information off it is not new.Īnd what you’re saying essentially is that you yourselves are not necessarily in the data integration, well, you are doing data integration … You’re not in the data grasping business and the data cleaning business, you’re more talking about what you can do with that data set. At one point it had a provisional patent filed to do this - I know some other people built on it later - and then ran out of money before it could actually pay the lawyers ! Maybe we could come back years later but the patents would have expired by then. Funnily enough, I was in a startup in 2000, which was trying to do this way the hell back when. And then you’re talking about the change in technology where you can identify Matthew Holt without knowing the name Matthew Holt, but by figuring out some token that equivalents to it, but you can’t get back to the identity. So, obviously there’ve been companies who’ve been playing in this game for a long time, IQVIA, the former IMS Health is obviously the most example. My own view on this is, aside from maybe certain categories of data like genomics and specialty lab data, we are moving to a world where most other forms of healthcare data will become commodities. Such that in a very short timeframe for healthcare, really 18 to 24 months, they have made it far easier to bring together disparate datasets. So think of it as a token being of that virtual patient identifier, and they have massively lowered the activation energy, if you will, or the barrier in terms of stitching together data. There are companies that have emerged like Datavant and HealthVerity that now do what’s called tokenization of data. Now, up until about two or three years ago, that was a very complex, cumbersome process. ![]() Same for the other categories of data, whether that’s lab, prescription, you can imagine there are folks who process that and are able to, again in a de-identified way, provide it. The folks who manage those pipes are able to resell that data in a de-identified way for example. ![]() So claims for example, as you know there’s a back office or a transmission set of pipes between the providers who submit the claims and the payers and the adjudication processes. Before we even think about how you put the data together, where do you go to get all these different sources?Ībsolutely. I think you wrote a blog post about this saying that interoperability is a big problem in American healthcare. So before we dive into what you do with that stuff, I know one of the sources is CMS and you have a special relationship there-You talked about a lot of data there. So we’re able to stitch together both the clinical picture and the very important social picture to ultimately be able to deliver a far richer longitudinal patient journey. And so think of us as being able to see credit card purchase history, whether someone has a driver’s license, a family member living within five miles that might have moved recently. We have pulled together a data set now on about 300 million Americans that links their claims history, their lab data, their prescription data, some amount of EMR data, and then critically, social determinants of health, not at zip code level as most others typically do, but at individual level in the same thought process that a bank would in looking for credit scores for example, or that Amazon would use to predict what you might want to buy. Very happy to, and thank you for offering us the time today, it’s a pleasure to be with you. So I hope I haven’t garbled that too much, but could you explain to start off with, what are the data sources that you’ve put together to form the base of all the products you’ve then built? You guys raised over a $110 million a couple of weeks back, which I guess is a small round these days considering what everyone else is doing.īut essentially you are one of the new companies who are doing data analytics in a different way for the health care industry, by putting together a lot of different sources of data on a lot of people. ![]() So Jean, Clarify Health is one of the new startups. And I’m with Jean Drouin, who has a French Canadian name, but is an American who’s lived in London–a bit like me–who is the CEO of Clarify Health. Hi, Matthew Holt here with another THCB Spotlight. ![]()
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