Outcomes Rocket Podcast_Jean Drouin, CEO of Clarify Health: this mp3 audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.
Saul Marquez:
Hey everybody! Saul Marquez with the Outcomes Rocket. Thanks so much for joining us again. Today, I am privileged to have the amazing Jean Drouin. He is the chief executive officer at Clarify Health, he is a leader with over 25 years of experience in healthcare management, technology, operations, financing, and cultural change. As CEO, Jean focuses on creating the environment that allows Clarify to deliver on its mission by delighting customers and growing a great team. He believes that healthcare has been held back by the lack of actionable insights and that by integrating innovative analytics and incentives, we can power better health and outcomes. Prior to founding Clarify, Jean was Senior Partner at McKinsey, where he led the digital care and IT practice. He has successfully led efforts touching millions of patients and billions of revenue in the private and public sectors, and today we have the privilege of having him here on the podcast. So, Jean, thanks for joining us.
Jean Drouin:
Thank you Saul, pleasure to meet you.
Saul Marquez:
Likewise, and so I’ve been a fan of Clarify and the work that you guys do, lots going on with it. But before we dive into the insights and the work that you guys do to make healthcare better, tell us what inspires your work in healthcare.
Jean Drouin:
Okay, so I come from a family of physicians so my, originally in Canada, so my great-grandfather was a GP up in Quebec City, so effectively where the civilized world ends, and you started getting logging camps back then, and then my grandfather was a cardiologist, and my dad’s an immunologist. So I grew up in a family of physicians and I suppose I caught the bug in terms of wanting to do something that made a difference. Now, specifically with regards to Clarify, I had an interesting experience when, I spent eight years in London back when I was with McKinsey and I was loaned out or the British expression is seconded to NHS London. So that was effectively the entire London health region, think of that a little bit as the equivalent of a geographically centralized Kaiser Permanente.
Saul Marquez:
Yeah.
Jean Drouin:
Where there were about 10 million people in London, about 10 billion pounds in spend, and about a hundred-and-thousand health system employees. And as the Head of Strategy in the budget, when we first set up NHS London, my role was to help approve the business plans that the hospital CEOs and the regional CEOs would bring. And one thing I learned there was, in England the system is incredibly centralized. And so theoretically, if you thought you had the perfect policy, you could put it in place. I learned very quickly that if we’re going to fix healthcare, we have to do it in a far more democratic, bottom-up way where any and every patient journey has a shot to get to a better outcome, better, cheaper, faster. Because it’s when you add up every single sub-optimized patient journey that you end up with the total waste and inefficiency in healthcare, and no amount of magical policy pixie dust is going to magically melt away all of the suboptimal decisions that are made each and every day and each and every patient journey that’s ongoing. So I stored that memory in the back of my mind and I said, all right, someday if there’s a chance to have the data to be able to light up every patient journey and give every patient, or family member, the chance to get to a better journey, that would be how ultimately we could empower the health system and those involved in the health system to get to a far better place than we’ve experienced in the more traditional fee-for-service system in the US or budgetary system in Europe.
Saul Marquez:
You know, thank you for that. And Jean, that’s a great analogy, you know, the London NHS to a more regionalized Kaiser. Connecting the dots is important and helping people on their individual journeys is key. Talk to us about what Clarify is doing that to, there to, make that a possibility.
Jean Drouin:
Sure, so our mission at Clarify is to power better care, and the reason we framed it that way is we’re not ultimately the ones delivering the care, we empower others to get to better care. Now, one of the lessons I definitely learned over the last 25 years is, in healthcare if you want to change anything, you have to do it in a way that fits with whatever payment model is dominant at any given time, and you have to do it in a way where the workflow doesn’t make it harder on folks, whether it’s clinicians or patients and members. So at Clarify, philosophically, what we’ve said is, okay, let’s give clinicians, patients, and their family members better insights for how to improve any journey, so let’s light up a journey. And interestingly, our philosophy of improvement is: the last thing you want to do is to go and put a clinician in a straightjacket and say that some machine learning and AI model is going to remove the joy you have in your own judgment and how to practice, but instead, it’s a lot more like sequencing the human genome. Let’s go sequence a journey and see where the relevant markers are, where if you did something different, an improvement would occur. So what we’ve done to do that at Clarify is we have sought to bring together the largest ever data set on patient journeys because you have to start with that raw intelligence. And so we stitch together now over 300 million lives worth of claims data, prescription data, lab data, EMR data, and social determinants of health. Now the social determinants, because our team came out of banking, we do it like a bank or Amazon. So at individual level, we can see the food people buy, where they buy it, that they get a caramel latte at Starbucks every day at 10 a.m., that kind of thing. And when you bring that raw intelligence together, you have the initial raw intelligence, but ultimately, as you know, healthcare data is incredibly dirty. So you then have to build a refinery to turn that dirty data into the cleanest possible fuel to then put into whatever intelligence layer is ultimately surfacing the insights. Now, notice I haven’t mentioned machine learning and AI all that much, but interestingly, we use a lot of machine learning and AI in that refinery to clean the data, and then we use it again to generate very precise insights on what should an ideal journey look like, and this particular member or patient coming in, what are the two or three things that would manifestly improve the odds that it ends up being a better outcome, shorter, better, cheaper journey for that individual?
Saul Marquez:
That’s great. We think about the common themes of access or cost. These are the things that we’re looking at to improve outcomes. Talk to us about how that matches up with lighting up journeys.
Jean Drouin:
Sure, so ultimately, the goal that one sets is incredibly important, as you’ve just intimated, and so we would argue that most individuals who need to go through a journey, right? And think of a journey as, I need a hip replacement, I have trouble walking upstairs, I need the right cardiac care, maybe I need a pacemaker, that kind of thing, right? Most people would say I want, well, ideally a delightful experience that’s as short as possible with the least hassle as possible that gets me as close to better as I can be.
Saul Marquez:
Yes.
Jean Drouin:
Right? And so in that case then, you don’t want unnecessary tests, you don’t want the wrong diagnosis made, you don’t want too much activity to be done that’s not required. So if, imagine a journey where even though you’re going to a new facility, they have access to all of your prior medical history so they don’t have to run unnecessary extra tests. Imagine that because we’ve got data on your social context, we can also say that post a procedure, you might need some help and someone to visit the home, right? All of those kinds of things, if those things were known ahead of time, then the stitching together of your journey could be far more effective and efficient and get you to that better outcome in a more delightful way, better, cheaper, faster. So how do we do that? If you look along the journey, there’s key questions. Is this the right doctor or facility for me? Is that the right therapy or intervention? Is that doctor or therapy or intervention even in-network within my provider? And then there’s underlying questions, how are people getting paid for doing the right thing? Clarify has a set of products or business applications to answer each of those questions based off of the underlying data that we’ve got on effectively the entire healthcare in the country.
Saul Marquez:
Fascinating, and you know, it’s this tool kit that helps you pick and choose how you’re going to improve on specific levers that need moving. So walk us through an example and maybe even, to add some color here, the typical user of the platform.
Jean Drouin:
Sure.
Saul Marquez:
Yeah.
Jean Drouin:
Great question, so remember earlier on I said, it’s so important in healthcare to follow how people get paid?
Saul Marquez:
Yes.
Jean Drouin:
That’s meant for us that we’ve had to, like most other analytics companies in healthcare, sell directly to other businesses, so hospitals, health systems like pharmaceutical companies, because it’s very hard to go direct-to-consumer because rarely does the consumer pay direct in healthcare, right? So I’ll give you a health insurer example. One of the biggest levers they’ve got is picking and choosing the most effective and efficient physicians to put in their networks, in the lingo that’s called network design. The way that used to be done is in a very manual way where they would go to local brokers in, say, the Denver area and they would say, hey, in Denver, who are the best primary care physicians and specialists? And they interview a few people, and often they would say, oh, well, we’ll take whomever is going to give us the biggest discount. Imagine instead if you could have software that would say, Denver, and immediately come out with a list of scores, a bit like in baseball with wins above replacement, batting averages, etc. that would say, okay, in cardiology for these needs, here’s the best cardiologist, and immediately you could pick and choose those and put those in your network. That’s the power of the Clarify software for that use case, and we have reduced the time it takes to build networks from over two years to less than a few months.
Saul Marquez:
Amazing.
Jean Drouin:
And those networks are higher quality, higher efficiency, etc. Another example is in life sciences. A big issue around health equity is clinical trials and ensuring that clinical trials are appropriately diverse. Imagine that instead of manually going through dirty data, looking at which sites might have more diverse patients for trials, instead you could say, oh, my trial is on congestive heart failure, I’m looking for a set of populations with these inclusions and exclusions. And all an analyst did is filter, filter, filter, click, and then 15 seconds later your answer came out, which is, there are 232 sites with potentially 8000 candidates, here’s where the sites are, or who the physicians are, and here you go, you can get into touch with them immediately. So it’s effectively, sometimes the analogy we use is we have created a Bloomberg terminal on top of a massive data set where anthropologically you could say, what did the Bloomberg do to banking? Well, it enabled self-service on-demand analytics on the part of a college-trained user of the platform.
Saul Marquez:
Yep, yep, that’s a great way to put it. And two great examples right on the life sciences and payer side, and I’m sure there are many more. So, and I had a chance to sit with your team, actually, while at the ViVE meeting, they ran me through some examples, just beautiful displays, and the way that you could make decisions based off of those displays, incredible. So kudos to you and the team for the work you’ve done here. What is one of the things, Jean, that you’re most excited about today?
Jean Drouin:
What I’m most excited about is ultimately, I believe that if we’re going to have change at scale in healthcare, we do ultimately need to have a payment model that supports that change. And so we have talked for a long time, not just in the US, but all over the world, about moving from fee-for-service to, or budgets, to value-based care. My view is that what’s made it very difficult, or we’ve had a lot of friction in moving there, it’s been because we’ve lacked the ability to fairly assess clinical performance and adjust for the difficulty of patients that different physicians were seeing. And we’ve lacked the precision to be able to say, okay, we’re going to pay based on either outcomes or doing the right things because the right things are going to deliver better outcomes. What’s different now than five years ago is, because we now have the precision of insight on how a journey should unfold and we’re able to compare how it went versus how it should have been, and therefore, we could potentially pay on that difference, we finally have the wherewithal and the mechanism to be able to enable a payment model that will encourage value-based care to occur. And to bring it home, I believe that clinicians absolutely want to do the right thing. At the same time, they’re human beings like you and I, and so they will respond to incentives just like we do.
Saul Marquez:
Yes.
Jean Drouin:
I can envision a payment model where you said, okay, here’s a base salary, you get paid during the year, and here’s a pool of bonus dollars, and the bonus dollars are going to get allocated based on doing the right thing, journey by journey. And if in the majority of journeys doing those things improves things, then absolutely you deserve to get those bonuses. But part of the issue has been that you’d have to wait a year to get the bonus before. What if you use predictive analytics to say, I understand how you perform historically, I’m willing to pay you today. Then there’s an immediate connection between doing the right thing and how you’re getting rewarded, and my sense is, it’s that kind of innovation around how we pay for care that will finally move us from fee-for-service to a model where we’re truly paying for doing the right activity that delivers better outcomes.
Saul Marquez:
Without any of the lag.
Jean Drouin:
Without any of the lag. And that’s the second thing that we’re seeking to enable at Clarify, is using the basis of our understanding of clinical performance, of patient journeys, enable a value-based care and settlements capability or platforms so that payers can offer contracts to providers where the providers are now paid in this way, and they can see immediately how they’re tracking and how they’re doing based on the real-time needs of the patients and members that they’re seeing.
Saul Marquez:
Well, so a very innovative approach that I think the insights that you and the team at Clarify are up to could be a real catalyst for that type of future. So, Jean, I got to tell you, super exciting work that you and your team are up to. If the listeners want to learn more about you, about the company, where can they go do that?
Jean Drouin:
So, best place, I think, would be either the Clarify website or reach out to me directly on LinkedIn. I’m usually very good about replying and I just want to thank you for giving us the opportunity today, it was a lot of fun, I really enjoyed the conversation, and I wish you well.
Saul Marquez:
Thanks, Jean, you too. And listeners, make sure you check out Clarify Health online. We’ll leave a link to the company as well as other ways you could interact with them in the show notes. Jean, thanks so much.
Jean Drouin:
Thank you, Saul, I really appreciate it.
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