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Geneia AI-Based Model Improves Personalization and Precision of Health Risk Scores

Geneia AI-Based Model Improves Personalization and Precision of Health Risk Scores

Geneia, a healthcare analytic solutions and services company that is simplifying the evolution to value-based care, announced today that the Geneia Data Intelligence Lab (GDI Lab) used leading-edge machine learning techniques to create a risk score model that is poised to outperform traditional actuarial practices. Traditionally, health plans have used actuarial models that determine risk and future costs at the population level. The GDI Lab model creates a dynamic risk score for each member, which improves its precision and personalization as well as enables health plans to act on the insights to prevent health deterioration and future costs.

“The Geneia Data Intelligence Lab model uses the latest machine learning techniques to outperform the actuarial models typically used by health plans,” said Fred Rahmanian, Geneia’s chief analytics and technology officer. “Our dynamic risk model creates a personalized risk score for each member, enabling health plans and their network physicians to better predict and mitigate future health costs and ultimately fare better in value-based care reimbursement arrangements.”

The GDI Lab, a team of carefully vetted and trained PhD- and masters-level data scientists, analysts and engineers, uses data science to help drive lower costs for health plans, physicians and employers. The lab creates elegant, refined and novel predictive and increasingly prescriptive models that are easy to use, faster than, and as accurate as or more accurate than traditional approaches.

“Take the Geneia Data Intelligence Lab’s Diabetes Complications model, for example. Preliminary results show that a health plan with one million members using this model to predict and intervene with those diabetics determined to be at-risk for a diabetes-related complication may potentially realize an annual savings of approximately $1.5 million,” said Rahmanian.