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Points of Light 2025 Case Study 23
Relieving Financial Pressures & Increasing Member Engagement Using Machine Learning
Author
Boyd Stewart
Author
Aurene Wilford
Payer Organization 23 wanted to decrease costs and improve clinical care by engaging Medicare-Medicaid Plan (MMP) members who had historically been unengaged in their health. Additionally, the payer needed accurate risk adjustment and CMS metrics in order to receive higher CMS reimbursements. Identifying and prioritizing the right members required time-intensive, manual processes that were difficult for both Payer and Healthcare Organization 23, so they partnered with N1 Health to utilize an AI offering that helped automate the process. The stakeholders identified the members most likely to engage and the methods of care that would be the most effective, using that information to optimize outreach. Ultimately, the stakeholders increased member engagement and CMS reimbursements through adjusted HCC scores.
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Key Findings:
- Executive Summary
- The Collaborators
- Points of Friction—Challenges to Be Solved
- Action Plan—How the Collaborators Worked Together to Reduce Friction
- Points of Light—Outcomes Achieved Through Collaboration
- Lessons Learned—What Best Practices Can Other Organizations Replicate?
- What's Next?—Vision for the Future
Writer
Natalie Hopkins
Designer
Jess Wallace-Simpson
Project Manager
Joel Sanchez
This material is copyrighted. Any organization gaining unauthorized access to this report will be liable to compensate KLAS for the full retail price. Please see the KLAS DATA USE POLICY for information regarding use of this report. © 2025 KLAS Research, LLC. All Rights Reserved. NOTE: Performance scores may change significantly when including newly interviewed provider organizations, especially when added to a smaller sample size like in emerging markets with a small number of live clients. The findings presented are not meant to be conclusive data for an entire client base.