Data & Analytics Spotlight: A Q&A with Creative Powerhouse Chad Kanick

From biomedical engineering to AI-powered health apps, Chad Kanick’s journey is anything but ordinary. I sat down with him to explore how his diverse background fuels innovation in healthcare data and analytics.

Our conversation has been edited for length and clarity.

Chris: You have a B.S., M.S., and Ph.D. in chemical engineering. How did you transition from engineering to data and analytics? Give us the short version of your life story.

Chad: I grew up in a small town in West Virginia, studied at WVU, and eventually made my way to graduate school in Pittsburgh. Much of my focus was on mass transport and fluid dynamics, which led me into the biomedical side of things and the study of how gases and fluids move through the body. I got into optics and medical devices, and then did a post-doc in Rotterdam, where I worked in a clinical theater for the first time. That experience was amazing, and it showed me how my work could directly help people.

After that, I became a professor in biomedical engineering at Dartmouth, working on surgical devices like cameras and fiber optics. But over time, I realized academia wasn’t where I could make the biggest impact. I didn’t want to spend my career refining the same tool or idea over and over. I wanted to solve real-world problems.

So, I joined a startup.

Chris: Your career path reminds me of the book Range: Why Generalists Triumph in a Specialized World, about how groundbreaking innovation often comes from combining knowledge across seemingly unrelated domains.

Chad: Exactly. In academia, I collaborated across disciplines, which some people found unusual. But I saw value in applying knowledge from one area to another. At the startup, we developed a wearable with an injectable component to sense body chemistry. It was my first time in a business and startup scenario, and I had to learn everything, from real world clinical data analysis to product development, to FDA clearance.

Later, I joined a digital health company that built an AI-based app for chronic disease management. That’s where I really got into the intersection of data, product, and customer success. I ended up leading the data team and learned a ton through domain-switching.

Chris: What did you learn by leading a data team?

Chad: How to extract meaningful insights—not just noise—across legacy programs and different populations and then use those insights to improve the product.

Chris: Range distinguishes between “kind” and “wicked” domains. Learning to play a musical instrument, for example, while not necessarily easy, is a kind learning environment with clear rules. The field of data and analytics (not to mention AI) is a wicked learning environment with many unknowns. How do you navigate this at Surescripts?

Chad: We’re in the middle of a transformation as we build a new product line that leverages our network data to solve information gaps. We’re navigating that by focusing on our core at Surescripts: secure transactions built on trusted and reliable infrastructure. Now we’re using that same infrastructure to create data products that identify and address unmet needs, and these data products must be clear, accurate and reliable, so organizations can make informed decisions. That’s our goal.

Chris: What’s the best question to ask organizations who need our data products?

Chad: “What problem are you trying to solve with this data?” That question will often reshape the data we provide.

One example is identifying when patients abandon prescriptions. Pharmacists and care managers at health plans often don’t have timely access to that data. We’re working on solutions that notify care managers quickly—within days—so they can reach out to the patient much sooner than usual. But even that raises the question: Do we need data on health systems, medical specialties, drug type, some combination of these, or something else entirely?

Chris: This sounds like endless configurability.

Chad: That’s the tricky part about doing this well. It’s about asking the right questions. It’s where product thinking meets analytics. If you fail to understand the problem that the customer is trying to solve, or to understand how our network functions or how our data is recorded, then you’ll miss the connection between problems and solutions. You need those different perspectives to deliver helpful data products.

Chris: What are you learning or excited about right now?

Chad: I’m excited about the scale of impact we can have in healthcare. The quality of the data and the size of the problems we’re tackling … it’s why we show up every day.

If you’re talking outside of work, it’s basketball. I grew up playing, and coached before, and just recently coached my daughter’s team for the first time this year and loved it. Coaching lets me connect with people and develop their talents and skillsets, and I bring that same mindset to building and leading our data team at work.

Chris: Who’s your pick to take the 2025 NBA Championship?

Chad: So many good teams in the NBA right now that it could be several of them. But I think the Oklahoma City Thunder will take it.

Postscript: As of Game 5, Oklahoma City leads 3-2.