Predictive Analytics: Saving Lives

At present the healthcare system knows very little about us till we show up at the hospital or in a doctor’s office sick.  In order to become preventive, the healthcare system will have to begin tracking our vitals decades before it currently does and listening in ways it currently does not.

I find it hard to imagine that Accountable Care Organizations (ACO) are going to be as wildly successful as projected without an ability to actively use the data they are obligated to collect. The Centers for Medicare & Medicaid Services (CMS) require the average ACO to compile data on their patients and then use that same data to demonstrate that they’ve met the CMS requirements of data use, reporting and improving quality. If those requirements aren’t met… well let’s just say that ACO might not be around for much longer.

Using predictive analytics and modeling tools is becoming a common and often daily occurrence in the work of caring for patients and populations in the healthcare industry.  But for someone who may be new to the field or has had limited exposure and/or experience with predictive analytics, the concept of mining data to predict the future may seem not only foreign, but a bit intimidating.

Predictive Analytics  are a powerful tool; of that there can be no doubt. However, like any tool, it only serves a purpose if it fulfills a useful function and almost as importantly is easy–to-use in fulfilling that purpose. For example, I can use a rock to drive a nail into a board, but the rock is hard to hold, difficult to use gently, easily breakable, etc. Ease of use for any tool means being able to complete a  task as easily as possible, with minimum frustration, every single time.  The same concept applies to the user interface (UI) of a seriously powerful tool like predictive analytics. The UI has to be effortless and intuitive, particularly if the end user is not a “super-user” and instead is a mere mortal who has  many other things to do in their busy life (like most of us!).