Solving the Rubik’s Cube of payer data

Solving the Rubik’s Cube of payer data

March 21, 2019
Health IT
By Mayur Yermaneni

For many health payers, making sense of their data is like trying to solve a Rubik’s Cube.

They have all of these individual data points. But the more they twist and turn them with their analytics, the further away from the goal they seem to get – and the more frustrated they get with the process.

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Anyone who has learned to solve an actual Rubik’s Cube with regularity, however, knows the key is to understand and recognize the patterns that lead to success.

The Rubik’s Cube health payers are currently facing is the mountain of incredibly rich data they’re sitting on right now. America’s Health Insurance Plans (AHIP) says the typical regional payer processes $8 billion in claims each year. Each of those claims houses a wealth of interesting data. Yet the challenge they face is how to aggregate and parse it in ways that enable them to take actions that will improve health outcomes and reduce costs.

But it isn’t just the volume of data that makes it so valuable. It’s the unique view it offers into member/patient health.

Even today, in the electronic age, providers, for the most part, only see the clinical, laboratory, and pharmaceutical data captured by their own office, facility, or health system. Any care that occurs outside their boundaries is often a mystery (even though by now it shouldn’t be), leaving holes in their understanding of the member’s health.

The same is even more true for laboratories and pharmaceutical companies. They see tests given or prescriptions issued, but have no real data on why those tests were ordered or prescriptions written. It’s like knowing there are multiple colors on the Rubik’s Cube but only being able to distinguish one of them. The puzzle will be awfully difficult to solve.

Payers, however, can see all the “colors” because each of those entities submits claims to them. By incorporating all of those data points, along with additional information such as demographics, psychographics, and social determinants of health (SDoH) into their analytics, payers have a far greater ability to manipulate the data to drive more reliable, and more actionable, conclusions.

Deeper predictions
With the right analytics at their disposal, payers can take this breadth and depth of data and use it not just to identify members/patients who are already in the high-risk category but also predict those who are trending toward it but not there yet. The “trendings” are a very important group to identify because payers still have time to change their destiny, improving their health and lowering their own benefit costs.

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