Jean Drouin, Clarify Health, on the new data stack.
Clarify Health has linked (but anonymized) data on about 300m Americans, including their claims, lab, (some) EMR data and their SDOH data. They then use it to help providers, plans and pharma figure out what is going on with their patients, and how their doctors et al are behaving. CEO Jean Drouin, a French-Canadian who incidentally at one point ran strategy for the NHS in London, explained to me what Clarify does, how it’s going to help improve health care, where these data products are going next–and why they needed to raise $116m in March to build it out. Jean thinks about creating a single source of truth, and I asked him a couple of tricky questions about whether his customers would want to know the answer. A fascinating discussion. (Full transcript below)
Matthew Holt:
Hi, Matthew Holt here with another THCB Spotlight. 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. So Jean, Clarify Health is one of the new startups. 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.
But 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 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?
Jean Drouin:
Very happy to, and thank you for offering us the time today, it’s a pleasure to be with you. 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. 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. 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.
Matthew Holt:
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. I think you wrote a blog post about this saying that interoperability is a big problem in American healthcare. No shit. Before we even think about how you put the data together, where do you go to get all these different sources?
Jean Drouin:
Absolutely. 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. The folks who manage those pipes are able to resell that data in a de-identified way for example. 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.
Jean Drouin:
Now, up until about two or three years ago, that was a very complex, cumbersome process. There are companies that have emerged like Datavant and HealthVerity that now do what’s called tokenization of data. 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. 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. 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.
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