Tuesday, September 25, 2012

Event Spotlight: O’Reilly Strata Rx Conference

CHI proudly supports Strata Rx, a conference taking place Oct. 16-17 at the Hilton San Francisco Union Square that will bring together experts and innovators in data science and healthcare, crossing traditional industry boundaries and uncovering insights on the emerging analytic approaches that can help solve some of healthcare's biggest challenges. Click here for more information on the conference and to receive the CHI member discount. In today's CHI Blog interview, event co-chairs Colin Hill and Julie Steele discuss their expectations for the conference, and some of the most pressing issues surrounding Big Data.

Colin Hill
Colin Hill is the CEO and co-founder of GNS Healthcare, a pioneer in Big Data analytics in personalized medicine and healthcare. He writes the Healthcare 2020 blog for Forbes. In 2004, Hill was named to MIT Technology Review 's TR100 list of the top innovators in the world under age 35.

Julie Steele
Julie Steele is the content editor at O'Reilly Media and co-author of Beautiful Visualization and Designing Data Visualizations.


Q: Talk about how this conference was conceptualized, and what your intentions are for it this year and beyond?

Steele: This conference was borne out of a larger conference called Strata. In accordance with Tim O’Reilly’s mission to work on things that matter, we looked at healthcare, specifically, as an area in which data science really has the potential to have significant impact for the better. This conference is designed to start some of the conversations between people who, traditionally, are in different silos of the healthcare industry. They may be computer scientists or they may be coming from a data science background and we want to get those conversations started so that collaboration and cooperation can happen.

Hill: Healthcare represents almost 20 percent of U.S. GDP. It’s the single largest category of the economy and is growing faster than anything else, while, at the same time, the U.S. is in the bottom half in terms of health outcomes. How can this be and what does this mean for technology and for data?

Q: Tell me who should attend the conference, and why.

Steele: It’s a mash-up of people from different corners of not just the health universe but the data and technology universe. You have health insurance executives but you also have data scientists participating. We really expect a wide audience of stakeholders including patients and providers. This conference gives them the opportunity to collaborate on some of today’s most pressing Big Data issues.

Q: Give me few examples of the kinds of healthcare organizations that have developed a strategic approach to Big Data.

Hill: In a way, healthcare is still in its infancy with Big Data because it is still a lot more about the research and the gathering of the data. We are beginning to see more interesting examples of data turned into decision making and interventions for patients. Stage 4 cancer patients, who would normally be left to die, now have a chance to find, outside of the standard of care, an intervention that works really well for them.

I think 23andMe and PatientsLikeMe, a community-based personalized medicine platform, are interesting examples of how organizations have been able to essentially reproduce research that took a very long time, find genetic associations for disease risk and use a crowd-sourcing-like approach.

Aetna just announced Aetna Innovation Labs, and, with that, a deal with GNS Healthcare around using data to manage metabolic syndrome to create a more personalized approach to disease management.

Another company is Foundation Medicine. They are doing some really innovative work, when it comes to cancer drug matching, leveraging DNA sequencing in a way that is giving patients a chance to respond beyond just the standard of care. In fact, we are using it on my father’s prostate cancer right now, so we have some intimate and personal knowledge of how Big Data is actually changing the paradigm.

Q: It is clear we don’t want to pay for ineffective treatment. What potential does Big Data have for outcome-based payment models?

Hill: That’s a great question. I think that’s absolutely critical. With all the hype and excitement about accountable care organizations and even value-based pricing that you're seeing with pharma, such as the Johnson & Johnson Velcade deal with the U.K. as of a few years ago, this really starts to force the use and the implementation of Big Data analytics to determine what’s actually going to work for which patient. I see Big Data at the core of answering these problems.

Q: Is there ever such thing as too much data?

Steele: I would frame it this way: The only data that is useful is the data that you can analyze and use to make better decisions. That’s the goal of all of this.

Hill: I would generally say no. We are always hungry for more data, but it absolutely has to be the right kind of data and we have to be able to find it and you can’t spend trillions of dollars storing it.

Q: From the O’Reilly report How Data Science is Transforming Health Care: “Data becomes infinitely more powerful when you can mix data from different sources: many doctor’s offices, hospital admission records, address databases, and even the rapidly increasing stream of data coming from personal fitness devices.” How far away is this?

Hill: On the drug development and biomedical research side, combining of data types is already happening, whether it is attempts to discover predictive markers to stratify patients in trials or to determine in a patient care setting who is going to respond to a different drug.

We are already seeing a lot of studies that are combining multiple data modalities where it is DNA sequencing, either single-nucleotide polymorphism or full-genome sequencing, combined with gene expression and metabolite profiling and clinical outcomes. It’s starting to become somewhat standard for certain diseases. In terms of healthcare data, when you’re talking about electronic medical records, claims data, clinical outcomes and such, you’re starting to see more combinations of companies, such as Humedica and Explorys, combining these data types. There’s also some focus on extracting data, closer to real-time, out of hospitals, combining that with longitudinal claims data that the hospitals process in order to get paid.

It’s going to take a bit longer for mobile health data to become real. We are still on the fringes of what really matters and what’s worth paying for. In my opinion, we are three to five years away from that becoming real. I think within five to 10 years it really becomes game changing.

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