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Big data, algorithms, and Moneyball medicine - Part I | Opensource.com
Big data, algorithms, and Moneyball medicine - Part I
I finally got around to watching Moneyball this week. Great film. Roger Ebert points out that the film "isn't so much about sports as about the war between intuition and statistics." (I'll let you guess who wins if you haven't seen the movie). The main character was neither Billy Beane (Brad Pitt) nor Peter Brand (Jonah Hill) so much as a set of algorithms the Billy and Peter characters implemented to build a winning baseball team at low cost. I'll go out on a limb and declare it the greatest statistics movie ever. (It's a short list of great statistics movies.)
And it's timely.
Algorithms seem to be everywhere in the news. A few pieces really nailed the trend this week, driven by O'reilly's Strata conference.
Tim O'reilly aptly described the trend in the context of business intelligence in an interview. "The role of visualization, of BI (business intelligence), is to help someone design an algorithm…the end game here is the design of systems that do the right thing in response to data better than we can." (quote is approximately minute 20).
The key for healthcare will be defining the right contexts where algorithms can really help. We, and physicians, can use a lot of help, but we'll need to go after the low-hanging fruit first (there's a lot of it), and be very careful where we draw the line between intuition and algorithm.
Another great piece on the subject of big data was in Forbes from from Jerry Michalsky, also inspired by the Strata conference. He points out that we're heading toward big data and algorithms at a time when we're discovering how little we know about ourselves.
Notable quote about big data for Michalsky:
Hmm. What experts in health care might be seeing their culture eroding? The key here is to enable the experts with better #UX-centered tools, I think, like dashboards for docs and patients. Another key point: "big data is nurturing a culture of collaboration." To get big data, we need to pull from multiple sources, and that means collaboration among all players, participating patients, physicians and payers. This is much of what's driving meaningful use and accountable care, we need to get to big data to achieve the triple aim.
And finally a piece by Ezra Klein notes that in 25 years we'll be loading ongoing personal health metrics continuously up to the cloud.
This kind of monitoring may be the future of health care. It will pay for itself in real dollar terms, but how much of our privacy are we going to be willing to put up for it? This is a question we'll be answering for decades to come: who owns our data and what are we willing to sell it for?
I don't know who said it, but one of the quotes out of SXSW this week was that "data is the new oil". Indeed, it's driving a lot of industry right now.
We're going to start seeing a Moneyball/statistical approach to just about every arena. Moneyball, algorithms and big data (and oil) are becoming synonymous. We're looking at new ways to use data and connect data to find out what actually works in a wide variety of arenas. Human intuition is on the ropes, but we have to be very careful about how far we extend the reach of algorithms to make decisions.
I'll be writing a series on what that this all means for health care. I'll discuss where we need help with algorithms in health care (decision-support, patient engagement and more), what's the dark side of algorithms in health care, and how big data, energy policy and health care really are related. We'll need to begin to be very careful about what we mean by "patient-centricity" in this new realm, just as Michalsky warns about "customer-centricity".
Accountable care means BI for health care systems (something that's relatively new) and the need for a statistics-driven approach. As I've written before, it's no accident that Blues, Aetna and United are buying up health IT companies that focus on care beyond the clinic.
It's a whole new ballgame. More to come.
Read more on Leonard's blog:
- Ideas Are Cheap ...Until they're shared