Solving the Rubik’s Cube of Payer Data





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Premiered Jun 8, 2019

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

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 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.

Mayur Yermaneni, Chief Strategy and Growth Officer at eQHealth Solutions, alongside Marina Brown, Vice President of Clinical Programs at eQHealth Solutions, will discuss why it isn’t the volume of payer data that makes it so valuable – it’s the unique view it offers into member/patient health.

Predictive and prescriptive analytics, especially when supported by AI and machine learning, can help take those maddening twists and turns of data and create a complete, clear picture that helps drive healthcare quality and member/patient satisfaction up while driving benefit costs down.


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