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Curv® Risk Adjustment
Easily identify new ACA health plan members with likely uncoded HCCs.

Risk adjustment coding professionals who use our Curv predictive model optimize ACA reimbursement on new members in their first year and see significant benefit-to-cost results.

Curv Risk Adjustment is a predictive model purpose-built for ACA plan carriers. It identifies new members with likely hierarchical condition categories and sorts them into tiers based on HCC revenue potential.

The model leverages our patented deidentification and tokenization technology—and the same Milliman-sourced prescription and medical claims data that has proven its value in group health underwriting for more than a decade—to infer relevant conditions and severity often missed by other sources. With this superior data-driven insight, you can better allocate your outreach resources and more urgently connect with high-risk members in need of reimbursement-critical coding and treatment.

Curv uses deidentified data. All you need is a simple group census.

What you may be missing without
Curv Risk Adjustment

Curv Risk Adjustment quickly sorts new members into custom-calibrated risk tiers, predicting their likelihood for HCC revenue. Without its AI-powered edge, you could be missing:

  • Reimbursement for HCCs that would otherwise go uncoded in new members
  • Members who clearly justify the cost of outreach for accurate coding
  • Likely HCCs only found with sophisticated machine-learning algorithms
  • Opportunity to allocate resources to highest-risk members
  • Operational savings from more efficient, targeted outreach

See more risk adjustment value and higher ROI with

Every ACA plan has new members with uncoded HCCs, but you don’t have to leave their risk adjustment value on the table. Schedule a demo to learn how you can use Curv’s superior AI to turn high-risk enrollees into high-reward opportunities in their very first year.

Milliman’s Curv® products are protected by one or more of U.S. Patents: 9,118,641; 9,323,892; 9,665,685; 9,965,651; 10,109,375 and 10,886,012.


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