Case Study: Saving Employers $6M Annually Through Machine Learning + Value-Based Care Management
A Recent Analysis Shows Our Predictive Analytics Result in Savings of Over $200 PMPY
Working with national insurers and PBMs, Certilytics deploys its sophisticated predictive analytics in support of value-based care management programs at dozens of large, self-funded employers.
A recent analysis of those programs showed they save participating employers an average of over $200 per member, per year (PMPY), relative to previous care-management strategies. For a typical employer—with about 30,000 participating beneficiaries—this translates to annual savings of more than $6 million.
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To achieve these savings, we help our customers move from retrospective reporting to prospective predictive analytics—identifying members for outreach before they’ve incurred significant costs.
This includes identifying members at risk for high-cost events, such as an emergency room visits, along with members at risk for developing chronic disease, such as diabetes. In the case of emerging clinical risk, we group members into risk buckets based on their likelihood of developing chronic diseases, enabling our clients to track progress in using care management strategies to move members from high risk to medium risk, or medium risk to low risk. We also predict a member’s likelihood of engagement, maximizing the chances that clinical interventions will be effective.
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Our advanced, predictive approach is not intended to replace an employer’s existing health benefit and care management programs. Instead, we seek to optimize those programs by acting as a centralized hub—the Nucleus Data Warehouse—that can be deployed with ease and integrates with existing resources. We help employers maximize the potential of their care management investments by analyzing utilization and engagement data to predict which beneficiaries will take advantage when directed to the services that will have the greatest impact on their health.
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