The Power of Predictive Analytics in Healthcare, Told through a Netflix Lens

Dan Ward is VP of revenue integrity at MethodCare, now part of ZirMed.

To better understand the fundamentals of predictive analytics — and why it has the potential to transform healthcare — it can be helpful to use Netflix as an illustrative example.

Let’s say, for example, you’re sitting around the house one rainy October Saturday and decide to view a few movies using Netflix’s streaming service. First you watched Field of Dreams then you decided, hey, this rain isn’t letting up any time soon, and that dog doesn’t want to go out in it any more than I do. So, after a brief backyard sojourn during which you and the dog confirmed that 38 degrees and rainy is in fact unpleasant — you reconvened your Netflixing and ended up watching Bull Durham, as well. Further, let us also assume that you enjoyed both movies and watched them all the way through.

As the credits rolled on Bull Durham, the critical question for Netflix was the same it always is: What would you enjoy watching next — specifically, what should Netflix recommend? Based upon the day’s viewing you may have a soft spot for baseball movies. Though it could just as easily be the case that you’re Kevin Costner’s biggest fan and the fact that you queued up two of his baseball movies was pure coincidence.

Given the uncertainty orbiting these pieces of information, maybe the best prediction would be Dances with Wolves, starring Kevin Costner. Or maybe the right pick would be Moneyball, the story of how the Oakland A’s leveraged data-driven, evidence-based sabermetrics to remain competitive against much more highly capitalized MLB teams. But what if neither Kevin Costner nor baseball is the most important correlation—what if the best predictor of whether you’ll like a film is simply whether it’s a sports movie from the late 80s?

As with all forms of predictive analytics, the question of what to recommend multiplies in complexity as overlapping variables (often in the form of unstructured data) are added and subsequently considered within algorithmic equations that power, in this case, Netflix’s recommendation engine. Further complicating the matter, it’s likely that you’re not the only person to have watched both of these films in close proximity and there are likely to be numerous “motivations” for such viewings across the population. It becomes apparent rather quickly the inherent challenge of something that seems, on the surface, as straightforward as a recommendation engine.

In healthcare we face these same kinds of challenges, just in a different form. The questions we ask are which gaps in care create the greatest risk for the patient, or which specific combinations of gaps in care correlate with readmissions—so that clinical outreach coordinators and other staff can prioritize whom to contact right away. We ask which types of claims are most likely to be under-coded or missing charges—so that organizations can make best use of finite resources like staff time and ensure the greatest positive impact on overall financial performance.

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