Smarter Outreach Helps a Health Plan Focus on Members Who Need It Most
A mid-sized health plan serving a large Medicaid population recognized that their traditional outreach approach was inefficient, resulting in wasted resources on members who were unlikely to respond or benefit from the outreach. The plan's quality and analytics teams collaborated to develop a machine learning-driven outreach platform that analyzed a wide range of factors, including demographic data, medical history, and behavioral patterns.
Using this platform, the plan identified and prioritized members who were most likely to benefit from targeted outreach. The platform's sophisticated algorithms identified subtle clues that indicated a member's risk of non-adherence, such as a history of skipped appointments or medications that were only partially filled.
With this new approach, the health plan was able to concentrate its outreach efforts on members who needed it most, focusing on closing care gaps and improving health outcomes. The result was a more targeted and effective outreach strategy that not only improved member engagement but also reduced the overall administrative burden of outreach efforts.
This innovative approach to risk stratification allowed the health plan to allocate its resources more efficiently, directing its outreach efforts to the members who were most likely to benefit from the interventions. By adopting this smarter approach, the plan demonstrated its commitment to delivering high-quality, personalized care to its Medicaid members, while also driving meaningful improvements in member health and well-being.
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