Steven Feng
Rice University
Houston (29.7° N, 95.3° W)

featuring Julian Yao, Senior Director of Strategic Initiatives, Covera Health, New York  (40.7° N, 74.0° W)

Julian Yao is the senior director of strategic initiatives at Covera Health, a New York-based clinical analytics startup that uses advanced data science to develop meaningful quality metrics to improve diagnostic accuracy and help ensure patients get the right care from the start. Though his current career is now focused on innovation in healthcare, his time at Rice University as an undergraduate reflected various interests; Julian matriculated at Rice University as a music major at the Shepherd School of Music, but he eventually graduated with a degree in economics and international relations. At Rice, Julian was selected as a Baker Institute Public Policy summer fellow, participated on three alternative spring break trips, and served as the business manager for the Rice Thresher, the campus newspaper. After graduating, he joined Accenture as a strategy consultant before ultimately joining Covera Health.

Initially, my intention with the interview with Julian was to learn more about the intersection between healthcare and data science and how different companies like his are focused in this area; as an economics student myself, I was also curious about how Julian’s undergraduate lessons in economics and international relations led him to the career path he is on today. However, our conversation extended far beyond the intended scope, and we found ourselves talking about job searching and the importance of having a mentor. In short, what started out as an extremely technical and objective conversation eased back into a casual and cordial discussion about education, upbringing, and adulthood.

Prior to having a conversation with Julian, I did not fully understand the importance and applications of data science. Data science is a discipline that I always hear is “up and coming” or “important to learn” but never anything more specific. As I learned, companies in this space usually focus first on acquiring large amounts of data and then on employing different mathematical methods to analyze the data to uncover trends or patterns. The applications of data science are ubiquitous in our daily lives, from Google and Facebook collecting our data to personalize advertisements to self-driving cars learning the right and wrong ways to navigate the street. Julian and Covera Health use the clinical data they amass to create meaningful quality metrics to help improve diagnostic accuracy and ensure patients get the right care from the start of their care journeys. One of the key points Julian emphasized during our discussion was that undergraduates need to learn skills associated with data science. He noted that data science has been integrated into every industry and career path, and he regrets not taking more advanced statistics and computer science classes as an undergraduate. I mentioned Rice’s new data science minor to Julian during our time together, and he suggested that I—and other economics students—consider pursuing the minor.

Interestingly, talking to Julian about his time at Rice made me more confident about my future. While our conversation focused a lot on data science, a recurring theme in our conversation was the usefulness of “soft skills,” a term broadly describing interpersonal skills not acquired within the classroom. Drawing from his experiences at Accenture and Covera, Julian explained the times in his career where communicating effectively, being responsible, and leading groups of people were just as important as the computational techniques he learned in school. After a gruesome semester of calculus-based microeconomics and linear algebra, I felt reassured by Julian’s words that, while theorems and algorithms are important, they will not be everything in my career, and there is another skill set that I can continue to improve to complement whatever I learn in class. That is the beauty of a Longitude conversation: you can start a conversation at the intersection of healthcare and data science, but you can never predict what ideas and lessons you will learn along the way.

Highlights from the interview

You started your career path as a strategy consultant at Accenture, but you later moved into the startup side of the healthcare business, ending up now at Covera Health. During your upbringing before college, did you ever envision yourself working in healthcare, or working at a big corporation and a startup, and did your perceptions change once you had experience in college?

I actually went to Rice as music major—opera performance—my first year in college, at the Shepherd School of Music. Music is a big part of my life and coming into college, I wanted to explore if music performance was something that I want to really dive into. It was an amazing experience, but I realized very quickly that while I loved music, my passions from an academic perspective were outside of the performance halls. That was a huge turning point in my career.

I switched my second year into pursing a degree in economics and political science. Growing up abroad in Asia, I had always been interested in international relations and political science. I spent two summers interning in Washington D.C., one of which was through Rice University’s Baker Institute summer program where I ended up working at the thinktank Center for American Progress, working on international economic policy research. Going into those internships, I really bought into the idea that I could effect change in the world through policy. But what I quickly realized being in D.C. for two summers is that policy and politics are inherently inseparable and thus extremely slow-moving and cumbersome. That was another turning point in my career.

And literally on the second to last week in my internship before heading back to Rice, I ran into Kyle Clark on the street in D.C. Kyle, who I consider a dear friend and a mentor to this day, was Will Rice’s president when I was a freshman. Kyle was working at Accenture and urged me to explore consulting as a career. I was drawn to the fact that he, only a few years out of college, was effecting change through the private sector and building skills to bring value to his clients. I got back to Rice and started practicing case interviews, which are basically interviews that test how you think for these big consulting firms. I really enjoyed them because it makes you feel like you’re solving a meaningful business problem. I went through the interview process and accepted a role at Accenture. After I joined, I jumped at the first opportunity to be in the public sector and health services division at Accenture and have been in healthcare ever since.

Covera uses data analytics to prevent radiology misdiagnoses. Could you provide a brief example of a project you worked on that had to do with the data analytics side of healthcare? What skills did you and your team use to complete that task?

Yeah, but I think it’s good to put it in context. I was at Accenture for three years. I learned a lot about healthcare as an industry. I learned about who the main players are—provider groups, academic hospitals, insurance companies, different types of vendors—but also became an expert about a wave of new tech startups that many of our clients at large insurance companies were asking us about. At Accenture you get thrown in immediately—it’s pretty crazy—as a college graduate you’re in the same room, sometimes presenting to CEOs, CFOs, and big decision makers at large corporations. Anyway, many of them were asking us, “Oh, we heard about XYZ healthcare startup. What are they doing? What are they up to? How do we work with them? Do you think them as competitors?” It was really interesting for us, as consultants, to be in a position that monitors the field and to connect the lines to identify trends and which organizations were gaining traction.

After a few years at Accenture I really wanted to have the experience of building something from scratch, so I joined Spreemo Health, which is chapter one of the current company I work at. We had a growing operational business, but we were also concurrently building a robust data analytics infrastructure about measuring quality in radiology. I was brought in to tackle the question of, “How do we expand our business model to more clients in a different market segment?” Twelve months after I joined, we sold 90 percent of the company and spun out our analytics infrastructure and team into Covera Health.

We spun out [Covera] with around 10 people. Now 18 months later, we’re at over 40. We just launched a nationwide program for a Fortune 10 employer client and are looking to implement with new clients this year. We closed our Series A funding of $8.5 million. Looking back, the entire process, and project I was involved in, leverages something I learned from my college years

A lot of the skills around thinking about data analysis I’ve built and learned from my classes, internships, my experience as a management consultant, and I’m still very much learning now. I think the biggest lesson I learned is really about getting into the mindset of how do I step back and think of the data analysis as a whole and be able to specifically ask: what is the business question that we’re trying to answer here? Of course, at the very beginning of your career, it’s about how to create value as an entry-level analyst, right out of college. This will grow into thinking about data analysis from a high-level perspective and how to translate data analytics to make business decisions.

There are a lot of “hard skills” that you learn, but I’ve also learned a lot of “soft skills,” specifically around the art of sales through storytelling. For example, how do you sell a vision, how do you tell a story, how do you provide data behind that story to make it compelling, and how do you get the buy-in of not only the person you’re trying to sell but all of their vendors to make sure that your clients work with? A lot of these softer skills pertain to people communication. It’s really about how do you deal with people who have their own agendas, have their own things that they’re working on, and aren’t necessarily incentivized to work with you.

Can you describe the dynamics of a team that works on a project at Covera in terms of the structure, the organization? Are there important characteristics that make a good team in your company?

My answer is probably a little different from others because we’re a 40-person startup. I’ve been lucky in that I’ve been in a situation to hire people to build out our team, including people who are higher on the totem pole than me. As a founding member of this company, it’s amazing to have the opportunity to shape the organization of the company. But at the end of the day you really have to break it down to, what is the problem you’re trying to solve and how do you build up a team with that?

For example, we have three basic teams in our company, big teams. One is operations: how do you think about day-to-day operations, how do you make sure you’re plugging in with all these different vendors correctly so the data is coming through you and then you can monitor the progress and see how things are working and everything around that. Next to operations, we have another function that focuses on how do you build relationships with providers across the company to make sure they join our program. And, lastly, it’s data science and clinical: how do we then measure provider quality over time, give feedback that is relevant to them, et cetera. So if you break it down really simply: teams are really built around what are the problems the company is trying to solve. I’m oversimplifying, obviously, but ultimately, teams shape up to that.

Hiring is definitely the hardest part of our job; it’s a problem that every company faces. I think one of the hardest things is finding a team that is able to work cohesively next to each other but just as challenging is about hiring the right person at the right time. It’s thinking about when do I hire, how to hire, and is this the right hire? Is it the right time, the right person, the right skill set? How does a company make sure that the next person they bring on is going to be an overall win for the company? Oftentimes at a small company, it’s also about asking how do I make sure that if my business changes all of a sudden, my employees can be flexible enough to do something else and still add value?

That’s why at a startup you often hear there are a lot of “generalists.” Everybody is a jack-of-all-trades; they can do anything just to get an idea off the ground. Then later on as you grow, you hire people who are more skilled and specialized in a specific area. That’s something where we found, as we’ve grown, as we’ve gotten more traction in the market, we have to bring in people who are more skilled and experts in doing one thing—for example, in enterprise sales or Bayesian statistics. The ethos behind hiring changes as a company grows.

So the long answer to your question, it’s really changing on a day-to-day basis. You have to be very thoughtful about how you set up a company structure. Bigger companies like Accenture—it’s a completely different story, where there are 300,000 employees across the globe. One of the things that we very much screen for is culture fit. Everybody says that, but it’s really worked for us, and I think it’s very similar to the Orientation Week advisor selection process [in college] where you not only have to bring in somebody that you can make sure that they get the job done, but beyond just that. It’s around how do they interact with other people in the company and if they fit with in the mission-oriented focus of the company.

Being able to prevent misdiagnoses in data science is already a monumental feat in healthcare; that’s something Covera does. Do you think that there are any other areas in healthcare where a deliberate data science approach could really move things forward and have a really big impact?

Absolutely. That’s why you see so many healthcare startups in the market today. Everybody is applying data science and data analytics. I’ll just talk about Covera Health since it’s closest to home. Healthcare is huge—there are so many different facets of healthcare, e.g., pharmaceuticals insurance, providers, et cetera. Healthcare is a huge industry, and it’s been very slow and stuck in the past because a lot of the systems were built then, and there has not been much incentive to change things. In the US, the system is notoriously cumbersome and slow. There are a lot of companies that are really focused on how to reduce waste in healthcare. There’s a notion that there’s a lot of unnecessary care happening; a lot of inappropriate care happening. From a nonclinical side, there’s also a lot of administrative waste.

For Covera, the approach is to say, how do we think about healthcare quality through a very objective, data-driven way? It’s really hard to measure quality in healthcare. How do you say Doctor X is good, Doctor Y is better, Doctor Z is the best? You can get three patients and they say, “Oh, I like Doctor Z because he has very good bedside manner.” And he/she gets five stars on Yelp or on Zocdoc. While we think that is important, for us, what we want to focus on is the quality of what the doctor is trying to do.

In our world we currently only focus on advanced radiology. Think very complex images (e.g., MRIs, CTs) that patients are getting, a consultation to get a diagnosis. This is extremely important because this diagnosis basically cascades downstream to what your treatment path is.

The problem that we’re trying to solve is how do we use data to identify the probability of when images are read inaccurately. Advanced imaging is very complex; the radiologist is looking through hundreds of images back-to-back. We use the power of data science to identify quality of diagnostic imaging and help route patients to the most appropriate doctor so that they can receive the most accurate diagnosis.

On the topic of data science, why do you think this field is so up-and-coming currently? Rice University is adding a data science minor next semester, and big data is a buzzword that gets thrown around a lot. What’s your personal take on data science and its trend right now?

I think data science should be a requirement. One of the biggest things I regret not studying in college is taking more advanced statistics and computer science classes. I recently completed online courses on basic coding and applications in data science because I think it’s extremely useful in this day and age. I can’t have a tangible discussion with data scientists at my company if I don’t speak their language. I really think people make it harder than it actually is to learn the basics: I truly believe that the hardest part is to be able to think logically. Coding is like learning a new language; it takes some time, but it’s not hard if you know how to approach it. I think the ability to think in a structured manner with data is super important. That’s why I think it should be mandatory.

I think the reason why this trend is happening right now is because one, we’re turning a corner where we have so much data in the world, whether it’s in healthcare, social media, banking, transportation, et cetera. Every single Uber ride is a data collection point. Every single thing you do on your phone is a data collection point. We have these new means of collecting data at an unprecedented pace. I think there’s a crazy statistic, something like the data collected last year alone is more data than the data collected the last 30 years put together, or something like that. That’s ridiculous. The ability to collect data and store it is really changing the way we’re thinking about the world.

Number two, the data we’re collecting is being used to train algorithms to do certain things. Even though the concepts and the math behind these algorithms may not be new, we’ve basically been able to process the data at a rate that we’ve never been able to do before. That’s really shifting how the world works.

Interview excerpts have been lightly edited for clarity and readability and approved by the interviewee.