HEALTHTECH: What are common roadblocks for healthcare organizations in setting up the right infrastructure to turn their data into insights? How did Mayo Clinic identify what worked?
GOYAL: If you look at the journey that we took, each step had a set of obstacles that allowed us to put the building blocks in place. The first was almost 15 years ago, when we adopted an electronic health records system. That allowed us to provide our patients with a consistent Mayo Clinic experience at all our destination sites and Mayo Clinic Health System locations.
Two, we recognized that the movement from maintaining data and infrastructure on-premises to the cloud was necessary if we were going to create use cases that we hadn’t even contemplated. We made the leap well before the industry did.
The next step was to get the organization to abide by certain principles when it comes to data. One was that it is an enterprise capability or asset that we are stewarding on behalf of our patients. That concept is very important, because in most organizations, data sits in silos and is guarded just within an area of the enterprise. We took the approach that we’re organizing patient data and making it available such that our entire patient population benefits.
The next step was pushing the de-identified data into the cloud so we could do a lot more with it.
After that, we said, “Let’s make sure that our consent model is built for maximal value extraction,” and for us, value extraction means our patients getting better care driven off that data instead of it just sitting there and collecting dust. But we can only do so much. There’s a lot of change happening across the globe spurred by innovators, and we needed to figure out a mechanism to engage them. So, we created this model called “Data Behind Glass” and invited the world’s innovators under a safety, privacy-protected construct to give access. We made sure that every person engaging abides by certain rules, and they’re focused on delivering value to our patients, meaning novel solutions that are focused on quality improvement, earlier disease identification or new cures. Where we continue the journey is, how do we make that an industry calling and invite others into the collaboration?
DISCOVER: Is your data governance actually AI-ready?
HEALTHTECH: How did Mayo Clinic develop its data-driven culture? What worked in encouraging stakeholders to become more data-minded?
GOYAL: Mayo Clinic is a 160-year-old organization founded on the principles that it’s not until we have multiple specialties — what we call a union of forces — come together that we get the best outcome. With other healthcare systems, typically, a patient can get bounced from one specialist to another. Our approach is, let’s put all of that in house and let those clinicians and departments confer to make the right diagnosis. That’s an important concept. So, we break apart those silos to start with.
The second concept is that we have an integrated research and clinical practice. That is probably one of the secrets to Mayo Clinic; we’re constantly pushing the envelope on care because our researchers are moving the care models and working with our clinicians. They’re not in silos. They’re integrated into each department.
Last, and it goes to why we’ve launched a platform, is that the founding Mayo brothers made it a point to teach others, but they also took what they learned from outside and brought it back and made it a core part of what Mayo Clinic is. When you think about platform models, that’s what you do: You create a common asset, and there are other people contributing and taking from it. That is the culture of Mayo Clinic. It’s a necessary component when you think about being data-centric, because it’s more of a learning organization. Data is a means — just like AI is a means — to an end. They just provide scalability. Mayo Clinic has always had these values, but the data and technologies enable us to move at a much faster pace.
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