Even mid-size regional payers, however, can usually afford to hire nurses or other clinicians – either internally or by outsourcing the function – to take what the predictive analytics have revealed and close the loop. These payer-sponsored clinicians can monitor adherence to plans of care, contact members who are trending toward increasing risk, and work with social services agencies to overcome SDoH challenges. The top payers will even have clinicians visit members in their homes, when required, to drive real change.
Solving the puzzle
While it may seem that having too much data about members is a first-world problem, the reality is that it can be overwhelming. And, like a Rubik’s Cube, sometimes the more organizations work to make sense of it the further away they get from their desired outcome.
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. Be sure you’re not the last health payer to discover it.
About the author: Mayur Yermaneni is chief strategy and growth officer of eQHealth Solutions, a population health management and healthcare IT solutions company that touches millions of lives each year. The organization has more than 30 years of experience working with payers, providers, and government entities on increasing quality outcomes and optimizing payer and provider networks. He can be reached at myermaneni@eqhs.org.
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