UCSF's Atul Butte is looking for proposals from California's top hospitals (Susan Merrell / UCSF)
"Precision medicine" may seem like a vague and futuristic term. But for President Obama and other policymakers, it represents the future of cancer treatment and care.
For decades, doctors would prescribe treatments that work for some or most people -- a "one sized fits all" approach. But precision medicine proposes that care providers treat patients on an individual basis.
This week, the state of California stepped up its efforts to deliver more targeted health care by setting aside $3 million for precision medicine.
The program relies on support from patients, caregivers and researchers, including doctors and nurses. Many health experts are already on board, as precision medicine could dramatically improve how we treat serious diseases, like cancer and diabetes.
Atul Butte, a physician and computational health buff, has stepped up to lead California's $3 million initiative. KQED's Future of You discussed with Butte the goals for the program,called the "California Initiative To Advance Precision Medicine," a few of the challenges, and the real benefits for people. This interview has been edited and condensed for brevity.
Do patients understand this "Precision Medicine" idea? How will you communicate this initiative to the general public?
Honestly, I don't think most people do. We are still trying to define the phrase, and figure out how best to describe it to the public. President Obama mentioned it earlier this year in a speech, and positioned it as a way for the country to try to solve cancer. I use phrases like "imagine a Google Maps for health" or "a new molecular approach to thinking about disease" -- and I tell people that now we're in a position to really do something.
The analogy of "Google Maps for health" is catchy. Did you come up with it? And what does it mean?
I actually borrowed the term from a 2011 report from the Institute of Medicine. Susan Desmond-Hellman and Keith Yamamoto and others from UCSF were part of a committee to decide what the future of medical research would be. The committee collected health data in all sorts of layers, including a layer for cost of care, for genomics, or for medications. But we are not connecting the dots between these layers. The analogy the committee used is with Google Maps, which pulls together in its visualization traffic data, restaurant ratings, building data and more. You need that kind of data integration for Life Sciences and medicine also.
Well, our plans date back to well before your visit in March. Governor Brown mentioned 'Precision Medicine' in the 2014 State of the Union Address. A pot of money -- $3 million -- was put into the state budget. We've been thinking since then about how to leverage that money.
We now know that we're planning to use it in two ways. We will start by collecting data that is necessary for us to provide Precision Medicine. We will determine questions like: Who has the assets? Is it groups of patients or genomics companies?
The bulk of the money, however, will go to two research pilots. These pilots will be run at multiple sites across the UC Health System.
How will you pick the two pilots? Will you setup a committee to evaluate all the proposals? And which diseases are you focusing on first?
We envision that all ten of the UC campuses will put in proposals. We may also get entries from Stanford and other California medical schools, as well as private biotech companies. We'll put together a committee in the next few weeks to decide on the two that will receive funding.
We're hoping that different campuses will work together and exchange relevant data. For instance, if two of the UC schools are exceptionally great at taking care of patients with breast cancer, they could pool data to develop specific drug therapies.
Cancer is an obvious target for Precision Medicine, and a lot of people are talking about that. We could also help kids with rare diseases.
How will you deal with some of the challenges of sharing data across different medical institutions? As recent articles (including a two-part series from KQED) have demonstrated, much of this data isn't stored in a format that computers can ingest. And as you gather this sensitive data, how will you protect it?
A lot of this data is structured, which makes it easier for a computer to sort. But some of it, like imaging studies and text-based medical reports, are very difficult to process. There are some tools we can use, but we envision that many more tools will be developed in the next few years, whether it's from academia or private companies.
Another thing to highlight is that we want to make sure patients are involved in the process. We will make sure the data is anonymized and de-identified. We don't need to know who these patients are. We'll also be adding ethics and privacy advocates on the committee.
$3 million is a drop in the bucket when it comes to health care. Are you disappointed that there aren't more funds for the initiative?
Frankly, I am thrilled there is any money at all. Money for science from state budgets is hard to come by! But there may be more money in the future, potentially from private investors.
Speaking of patients, how will people benefit from this initiative in real and tangible ways? The initiative seems quite vague in its scope.
Let's be clear: This is a modest sum of money that will be spent on short-term trials. Some patients might enroll in these studies, particularly as existing studies expand to more UC centers. Those who participate at the early stages can learn about genetics. But the experience will not change for the average Californian.
How did you get involved in this field of "computational health sciences"? Will there be a hot market for jobs like this, which combine computer science and medicine skill-sets?
I started my career as a computer scientist, and used to work as a contractor for big companies like Apple. I later attended medical school and trained as a pediatrician. My advisors convinced me to go to The Massachusetts Institute of Technology (MIT) to earn a PhD. After graduating, I set up a lab to figure out how to better use medical data.
I just moved over to UCSF from Stanford at the beginning of April to build out the Institute for Computational Health Science. We'll be recruiting heavily.
The field of bioinformatics has been important for some time, but the significance is new. We're not looking at tiny bits of human DNA anymore, but the whole genome. The scope is much bigger. Bioinformatics is absolutely the career of the future.
What brought you to UCSF after over a decade at Stanford?
I loved Stanford, but UCSF offered two things I couldn't get there. It's a major medical center with large patient populations and resources that are required to build out a whole program in computational health. We're going to build a new building -- you need a large set of resources for that. Also, I have this physician link across all the UC health campuses. That's 13 million patients! We can really learn a lot from them, while we work to improve the consistency of care across the UC system.
Do you have any plans for new research that would reduce inefficiencies and help keep health care costs under control? And how about pricing, which can wildly differ between California hospitals?
Yes, there are a lot of inefficiencies in our health care system. I think data might shed light on where we can improve. I am hopeful we can reduce health care costs. How can we remove the piece that isn't needed?
Pricing depends on a complicated system of players, including payers. If I can even partially solve the cost problem, I'll see that as a success. Pricing is a different story.
How will the private sector get involved with this initiative? And are you looking at involving digital health tools from Silicon Valley's startups?
Yes, we expect that private industry will want to get involved. I have been getting tons of emails already from companies.
We envision that these pilots will draw on resources from the tech world, like Apple's ResearchKit, which offers an interesting way to recruit patients. We would also encourage research teams to use new tools and partner with companies like Apple or Samsung to get more outcomes data on their patients. What are patients like at home? How healthy are their habits? We need more real-world data.
I am definitely optimistic about the role of technology in health care. Computers and digitization really has helped. We just need to make sure people aren't being left behind as we move into this digital age, and that everyone makes these transition away from paper-based systems in a safe and effective way.
Get the best of KQED’s science coverage in your inbox weekly.