Big Data, Definitions and Population Health

What's the likelihood of diabetes?

Utter the term "big data" at any ACO, care management or managed care meeting, and one of two things will happen:

1) Your colleagues will admire your population health chops and your boss will be reminded that you deserve a raise, or

2) Your colleagues will tire of your faddism and your boss will wonder, once again, just what "big data" means

Either way, you may want to refer your colleagues and boss to this readable "on-line first" article appearing in JAMA.

Here's a handy PHB summary:

"Big data" can be defined as the linking of disparate large data sets to provide insight at the individual level.

It's been used by political campaigns (swing voters), business (expectant mothers) and the NSA (potential terrorists). Once they are identified, amenable voters can be individually lobbied, expectant mothers can be sent personalized coupons and evil-doers can be visited by Jack Bauer.

According to Weber and his co-authors, how should health care providers approach big data?

1) Inventory the available data sets.  Traditional examples include electronic health records, insurance claims and pharmacy data.  Big data architects should also be aware of non-traditional examples including social media, census records and credit card purchases (such as grocery store purchases, fitness club memberships or over-the-counter meds).

2) Anticipate "probabilistic matching," since two or more individuals may fulfill criteria.  This will involve trade-offs between accuracy and feasibility, since two individuals matching "John Smith" in a single zip code may appear to have the same risk. 

3) Worry about HIPAA. Unfortunately, while medical data sets are disparate, they're also walled off by privacy concerns and special regulations that govern genetic and mental health data. It's not insurmountable. The health care industry should also participate in the public square to and help shape evolving societal and legislative standards over privacy.

Fortunately, the population health industry (here's a modest example) is already engaged. They understand that big data can be used to estimate individual risk which can, in turn, guide outreach to individual patients.

Image from Wikipedia