Raising Health
Scaling Medicaid Innovation with Afia Asamoah, Rajaie Batniji, and Sanjay Basu
Jan 14, 2025
Rajaie Batniji and Sanjay Basu, co-founders of Waymark, blend medical expertise with innovation in Medicaid care. They discuss their pioneering use of machine learning to predict patient needs, which leads to a significant reduction in emergency visits. The duo highlights how community engagement and trust are vital in healthcare, emphasizing the success of community health workers. Listeners will learn about the balance of profit and purpose in healthcare, along with the importance of early interventions for improving health outcomes.
33:45
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Quick takeaways
- The innovative rising risk signal prediction framework utilizes machine learning to identify Medicaid patients at risk, enabling proactive community-based care interventions.
- Community health workers play a crucial role in this model by fostering trust, addressing social needs, and improving overall health outcomes for underserved populations.
Deep dives
Impact of Community-Based Care on Medicaid
About 40% of acute care visits by Medicaid patients are deemed avoidable, indicating a significant opportunity to prevent unnecessary hospitalizations within this population. A new approach to Medicaid care delivery combines advanced machine learning with community-focused interventions, reducing emergency room visits by nearly 25%. This model emphasizes meeting patients in their communities, rather than relying solely on traditional clinical settings, which has proven effective in enhancing care quality and patient access. By addressing social needs alongside medical care, the approach aims to significantly improve health outcomes for underserved populations while controlling costs.
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