20 minutes | Jan 29th 2020

Data in Healthcare with Ron Vianu

If you’ve ever tried to find a doctor in the United States, you likely know how hard it is to find one who’s the right fit—it takes quite a bit of research to find good information to make an informed choice. Wouldn’t it be nice to easily find a doctor who is the right fit for you? Using data, Covera Health aims to do just that in the radiology specialty. Ron Vianu: I think the tools are really improving year over year to a significant degree, but like anything else, the tools themselves are only as useful as how you apply them. You can have the most amazing tools that could understand very large datasets, but you know how you approach looking for solutions, I think can dramatically impact. Do you yield anything useful Ginette Methot: I’m Ginette, Curtis Seare: and I’m Curtis, Ginette: and you are listening to Data Crunch, Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. If you’re a business leader listening to our podcast and would like to move 10 times faster and be 10 times smarter than your competitors, we’re running a webinar on February 13th where you can learn how to do this and more. Just go to datacrunchcorp.com/go to sign up today for free. If you’re a subject matter expert in your field, like our guest today, and you’re looking to understand data science and machine learning, brilliant.org is a great place to dig deeper. Their classes, help you understand algorithms, machine learning concepts, computer science basics, and many other important concepts in data science and machine learning. The nice thing about brilliant.org is that you can learn in bite-sized pieces at your own pace. Their courses have storytelling, code writing and interactive challenges, which makes them entertaining, challenging, and educational. Sign up for free and start learning by going to brilliant.org/data crunch. And also the first 200 people that go to that link will get 20% off the annual premium subscription. Today we chat with Ron Vianu, the CEO of Covera Health. Let’s get right to it. Curtis: What inspired you to get into what you’re doing, uh, to start Covera health? Where did the idea come from and what drives you? So if we could start there and learn a little bit about you and the beginnings of Covera health, that would be great. Ron: Sure. Uh, and I, I guess it’s important to state that, you know, I’m a problem solver by nature, and my entire professional career, I’ve been a serial entrepreneur building companies to solve very specific problems. And as it relates to Covera, the, the Genesis of it was understanding that there were two problems in the market with respect to, uh, the healthcare space, which is where we’re focused that were historically unsolved and there were no efforts really to solve them in, from my perspective, a data-driven way. And that was around understanding quality of physicians that is predictive to whether or not they’ll be successful with individual patients as they walk through their practice. And so if you, and we’re focused on the world of radiology, which today is highly commoditized and what that means is that there was a presumption that wherever you get an MRI or a CT study for some injury or illness, it doesn’t matter where you go. It’s more about convenience and price perhaps. Whereas what we understand given our research and the, the various things that we’ve published since our beginning is that one, it’s like every other medical specialty. It’s highly variable. Two, since radiology supports all other medical specialties in a, as a tool for diagnosis, diagnostic purposes, any sort of variability within that specialty has a cascading effect on patients downstream. And so for us, the beginning was, is this something that is solvable through data? Could we understand for an individual patient as they’re looking for medical care, what is the right physician for them that would yield the most accurate diagnosis related to their condition. Curtis: Got it. And I’m assuming you have some experience in the medical field. Do you usually have the companies, you’ve started been in the medical field and so you had insight into this issue or where did that come from? Ron: Yeah, I mean, my background, I was a premed student actually, uh, in New York and I, at the time, I felt like going to medical school really wouldn’t be solving problems as the way I saw, uh, the life of a physician. And so I decided that business was probably a better perspective to solve problems. And ironically I ended up solving problems within healthcare my entire professional career. And so I have a fairly deep knowledge base, if you will, around clinical medicine for a lay person and obviously a lot of experience around starting businesses and using data to solve problems. And so it really, for me it’s an interesting combination of skills that allows me to tackle these things in a way that perhaps a physician or a business person, uh, independently wouldn’t be able to do. Curtis: And where did your expertise in data come from? You seemed to approach things from a very data-driven perspective. Where did you get that from? Ron: I think that’s honestly something that one is innately born with and then one finds the tools to help them explore that. And so in college I studied chemistry and philosophy, and I think part of it is because I was trying to approach different parts of the way my brain functioned. And so when I solve problems today, I try to solve them in a very data-driven manner, generally speaking. And so when I find tools like statistical modeling or AI and so on and so forth, that can further enhance the approach that I would take in solving a problem. Those tools are extraordinarily useful for me. But I don’t think it’s something that I, you know, and one could argue, maybe others have this where you take a course and you’re like, ah, this is an interesting science and I could use this science. For me it was how do I kind of expand the very way I generally function. Curtis: One of the things that we see as the tooling and the understanding around machine learning and analytical practices is becoming better and better. As someone that didn’t study this, you know, computer science, this kind of stuff. Have you found it accessible? Sort of easy to pick up and apply to problems? Ron: Right. So I guess two points I would make there. One, I’m, I’m not a data scientist per se in any traditional way. My background is comp sci meaning back in a, in kind of an untraditional way, meaning both in college and pre college I was programming. And so I have a little bit of that background even though I didn’t study it in a formal setting, but I think the tools are really, uh, improving year over year to, uh, to a significant degree. But like anything else, the tools themselves are only as useful as how you apply them. And so I think, you know, you can have the most amazing tools that could understand very large datasets, but you know, how you approach looking for solutions I think can dramatically impact do you yield anything useful. Curtis: And do you have a specific approach that you do? Is this, does it come naturally to you or do you have some sort of framework or approach that you use to look at things and figure out how you, how you could solve it? Ron: Right. So I’m agnostic from a data science perspective with respect to the actual approach we’re taking, uh, meaning what tools are we going to be using? But moving aside technically, you know, there are two different approaches one can take when one broadly thinks about data science and analytics. And you know, the, the big approach that I think has been very popular over the last, call it five, seven years, is around big data as people call it, which is now that we have access to lots of data and we have access to all these interesting tools and algorithms that can analyze that data, what can we ultimately understand from that data, what patterns can emerge that perhaps we haven’t seen in the past? And I think that’s very productive and useful in many contexts in healthcare where it’s very difficult to understand what data you’re looking at to begin with. And so you have very dirty datasets and cleaning those up becomes half the challenge. And so for me, my approach with respect to healthcare data analytics has been more hypothesis driven rather than that big data approach. And what I mean by that is if you speak to physicians around this thing called quality, which is what we’re trying to solve, how do you understand what physician is ideally suited for a particular patient in order to yield the best outcome? And so as we approach that problem, we work with many experts across the field and we ask to understand their intuition around quality, what makes a good physician. And once we have a unified sense of what the experts think, then we start attacking the data in a way that explores those theories and understands if we can ultimately find some signal with respect to those theories or rather correlations with respect to those theories. And so it’s, it’s a little bit of a different approach, much more hypothesis driven than big data-driven. Curtis: So instead of sifting through the data to find random signals and then seeing if those are useful for some application, you then make some hypotheses, uh, bring domain knowledge and then see if you can find some signals in data that, that, that you have available. Is that accurate? Ron: That’s accurate. And, and I can give you a concrete
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