Q&A: Dr. Atul Butte on the Evolution of Data Analytics in Healthcare

HEALTHTECH: Can you tell me about your career in data science?

BUTTE: My undergraduate degree is in computer science, and I still love writing code. I usually have a code window open on my laptop somewhere. Early in my career, I spent my summers working at Apple and Microsoft. But after all that, I went to medical school, and that combination of computer science and medicine was rare back then. I think it’s probably still kind of rare to be a software engineer and go to medical school.

I was lucky to do that, and I ended up training in pediatrics and, later, pediatric endocrinology, so I was taking care of kids with diabetes and growth problems. My mentor then convinced me to go back to get a Ph.D. in biomedical informatics. I ended up sticking around in the Boston ecosystem for a full 10 years, finished that Ph.D., and then moved to Stanford for 10 years to start my career as an assistant professor, making it all the way up to full professor. I've now been at the University of California for about seven years, and I love the day job; it’s amazing. I’m a professor, and I run my research group, lab and institute at UC San Francisco, but I’ve also got this operational role across the entire University of California Health System as the chief data scientist.

HEALTHTECH: How has the field of data analytics in healthcare evolved over the past 10 years?

BUTTE: It’s changed a lot, as you and your readers know. First, we have more data, and electronic health records are now essentially everywhere. And they’re not just everywhere, they cover more elements of care. Now, chemotherapy orders will be in there, and all the other kinds of complex cases covered. We now have enormous amounts of data captured on patients, including everything we’re doing and measuring, and now it’s the legal record for the patient. So compared with 10 years ago, when some of our health systems were just installing Epic, for example, this field has changed a lot, now that we all have this kind of data.

So, the new opportunity is to figure out what to do with all this data. I believe health data is the most expensive data in America. In many cases, we’re paying doctors to type it in. The narrative I want to make sure your readers know is that it will be a national tragedy if we don’t use this data to improve the practice of medicine, given how much we spent to collect it. And, of course, we’ve got to do it safely and responsibly.

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HEALTHTECH: What are some of the ways data analytics is already helping to improve patient outcomes and improve medicine?

BUTTE: I think many of us in the healthcare system understand the role of analytics when working with payers who ask us to document the quality of care we provide. For example, Medicaid is one such payer; in California, it’s called Medi-Cal. We have to use data analytics to ensure that we’re consistently reporting on the quality of care we’re delivering, and not just for the Medi-Cal patients, but for all patients. How do we use analytics to report on quality of care? That's a no-brainer, and most health systems get that. But to go further, how do we use it to improve quality of care? We don't want to just be looking in the rear-view mirror to see where we were, we want to find those patients that are, for example, in the hospital now, and improve quality of care now. I think that forward-looking direction for analytics is the future, to find those patients that are missing care now, that are getting substandard care now. Analytics is there for quality of care.

HEALTHTECH: How do artificial intelligence and machine learning improve overall population health?

BUTTE: AI and machine learning are exciting, and it’s beyond buzzwords. A lot of teams are still trying to figure out what they mean. In many cases, AI models don’t have to be super sophisticated, but when deployed and discovered in the right way, they can seem magical. For example, we’ve all seen instances where AI is now helping interpret radiological images. We have a team at UC San Diego, for example, that had a model that they were using for COVID, to see very early during the pandemic whether a chest X-ray could help determine if someone was positive for COVID-19. That was back when we didn't have a lot of tests that we could run, so the X-ray was all we had. There are now hundreds of FDA-approved AI tools in radiology. That’s up from three or four, back in 2017.

The use of our clinical data to help train, evaluate and use these models is certainly the future, if not even the present. I think our health systems and our doctors are going to want these tools to help them deliver better care. We are going to have to get our data harmonized and cleaned in a way that’s usable.

To get a better handle on what is going to happen with our patients and what’s happening with them now, we’ve got to figure out how to understand them, and that means sometimes knowing more about our patients. That’s AI, and machine learning, and predicting the future of patients.

Another way to better understand our patients is to consider the circumstances of their lives, or what’s called social determinants of health. For example, we can know a lot or intuit a lot from where they live. It’s not perfect, and it’s just the start, but if you can understand where a patient lives, you might have some clue as to their social circumstances. We can determine what kind of payer coverage they have, certainly, but also what kind of employment opportunities are in that neighborhood, and whether they have access to higher quality restaurants, food options and groceries. All of those are aspects of care might make a big difference for our patients who have diabetes, hypertension or other complicated disorders. And a lot of it can be reasoned by just knowing where a patient lives.

Now, it’s not perfect. Of course, it’s better to directly ask and work with patients to understand their social circumstances, than try to guess those social determinants of health from their zip code. But it is just the start.

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HEALTHTECH: Do you have any best practices to offer healthcare organizations looking to implement more data analytics in their ecosystem?

BUTTE: Well, I'm blessed because I’m at an academic medical center, and I'm also surrounded by researchers and am one myself. It’s easy here to get the idea that clinical research and clinical trials would benefit from having access to clinical data. But I think one key thing we did differently was to go after operational uses of our systemwide data first. Where could we use our data to demonstrate quality of care? Where could we see and improve any flaws in our care before we have to report them to others? Where can we save money?

At the University of California Health System, we have six medical schools, 12 hospitals and hundreds of care delivery sites. We have started to use some of this data to help what we call our Leveraging Scale for Value Program. If we use analytics to figure out which biologic or which pharmaceutical drugs we’re using, we can start to buy them together to get a better price. I think when these kinds of projects generate enough savings for a health system, then the whole thing gets paid for. The researchers, research use, clinical trials and all of that can still come after. But we worked on our operational use cases first, and I think I am happy with that decision.

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I do want to get back to patients, though, and I can never forget that we’re doing all of this because we’re trying to improve the lives and the livelihoods of our patients. Sometimes that means even just giving them the raw data. We use federal standards like FHIR for the data we give data back to patients. Giving data back to patients shouldn’t be just an option or even a question anymore. I can see a new world where patients get analytical tools to analyze their own medical record data. I can’t wait to see that world.

In summary, I think one easy recommendation is to go after those operational use cases first, get the data infrastructure paid for in that way, and then you get to try all these newer cutting-edge AI tools and help with clinical research.

HEALTHTECH: What do you think needs to change in the way data analytics is used or implemented for it to evolve in healthcare?

BUTTE: It’s not going to be about the technology. I say this all the time: We can have all the data in the world, we can have it in the best format, but it doesn’t do anything unless there are teams ready to act on that data. If those teams, whether they’re strategy officers or clinicians, commonly made their decisions from the gut or from the heart, they might not be ready to deal with making decisions using data no matter how cleaned up it is.

Part of our role is to showcase these uses of data that really make a difference, to encourage more data-driven thinking for data-driven decisions in the healthcare enterprise. Finding those good cases is always hard; it’s beyond just solving the technical part of our work. It’s about understanding what those unmet needs are in our enterprises, and how data and data analytics can help solve them.

HEALTHTECH: How do you see data analytics being used in healthcare in the future?

BUTTE: I think we’re going to have a lot more companies out there, and tools to help us do our work. We still barely have tools to really analyze our own clinical data, whether studying patients one by one or an entire population. Some of these tools will be rolled out by the electronic health record vendors themselves, while some will be separate. I welcome seeing these new tools, and I even hope my team will create some of them. I also see a future where patients have their own set of tools to help them interpret their own data. I see many more tools and companies out there to help us along in this journey.

HEALTHTECH: Do you think there’s going to be more regulation put in place around healthcare and data analytics in the future?

BUTTE: Absolutely, regulation will be there, but it’s not going to be new. Data analytics in general might not have a lot of regulatory burden to overcome, but analytics in certain contexts will. For example, one use of clinical data is in partnerships with pharmaceutical companies, biotech companies and device manufacturers to help us all know whether their drugs or devices are working or not. The FDA calls this use of clinical data “real-world evidence.” I see a strong future with the FDA having more governance over real-world evidence, and most of the parties involved want this to happen so we all have a better idea of what the quality of data should be and how this data is going to be used to help all the parties in healthcare.

The FDA now even has a regulatory category called “software as a medical device,” which is an amazing concept. And then newer digital apps themselves can be prescribed, and that will be regulated. Our data and analytics will be used to help create those apps. I think the FDA is going to be all over this, but in a great way. Then we will have clarity as to what we need to build in terms of these products, and companies will have clarity and can create more offerings in the market.

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HEALTHTECH: Is there anything else you’d like to add?

BUTTE: There’s endless depth in data and analytics. Some folks will think installing SlicerDicer from Epic gets you there, and that’s a great start, but there is a lot more out there to go deeper. I would recommend to readers, don’t hesitate, just start. If you have no analytics team as a health system, start thinking about how you need that team or partners to get to better data-driven decisions. It’s still an exciting time, and we’re just at the beginning.