Manage Dual Eligible Beneficiaries
Failure to correctly manage individuals eligible for both Medicare and Medicaid benefits causes several problems for MA plans, including understated risk scores by 20% on average and underpayment of premium revenues by up to $600.
Pareto Intelligence’s Dual Eligibility Identification Model identifies the members in your plan likely to qualify for full or partial dual eligibility and predicts dual status with up to 95% accuracy on average. Our analytics consistently outperform the competition by identifying more members with higher precision and continuously monitoring enrollees to initiate timely recertification, resulting in more accurate reimbursements.
How it Works
- Identify: Apply Pareto’s proprietary suspecting model to analyze and predict a member’s dual eligible status with expert precision.
- Report: Deliver model outcomes on a secure, web-based platform with prioritized worklists to focus internal or Pareto-led outreach on the members most likely to be dual eligible, as well as those members requiring recertification in the near future.
- Engage: Outreach to members to assist with enrollment and reenrollment by using Pareto’s analytics to empower internal resources or by using Pareto’s skilled outreach experts to interface with the member and guide them through the enrollment and recertification processes.
Why Pareto?
Flexible
We offer multiple delivery models and present our results in a way that works best for you.
Member-Focused
Our processes are centered around ensuring members receive the benefits they are entitled to, which delivers value to members and health plans.
Unmatched Experience
20+ years of experience supporting premium-related initiatives with health plans makes Pareto the ideal partner.
A Powerful Foundation
Our Data Management Platform
Pareto’s Data Management Platform, the foundation to our solutions, is built around data lake, artificial intelligence, and open source big data technologies to allow us to effectively ingest data in all formats and types at scale, organize them with appropriate data governance mechanisms within our data catalog, and provide efficient configuration capabilities to democratize data access and analytics.
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