Medicaid, the primary health insurance program for low-income and disabled individuals in the U.S., has struggled to deliver high-value care due to fragmented delivery, bureaucratic barriers, poor outcomes, and high avoidable costs. Recent efforts have focused on shifting to value-based care (VBC) models, though many of these models fail to significantly improve care delivery. Persistent data quality issues and insufficient integration of clinical and social risk factor data contribute to the ineffectiveness of VBC, but advances in data science hold promise for enabling high-value care. Click here for article.
Fragmented Care and High Costs: Medicaid faces challenges with fragmented care delivery, leading to poor health outcomes and high avoidable costs.
Shift to Value-Based Care: Efforts to implement VBC models like accountable care organizations (ACOs) often result in "value veneers" that don't significantly improve care.
Data Quality Issues: Poor data quality and lack of integration of clinical and social risk factors hinder effective risk prediction and targeted interventions in Medicaid.
Promise of New Data Science: Advances in data science, including new predictive modeling techniques, offer the potential to deliver more equitable, high-value care by accurately identifying and addressing the needs of at-risk patients.
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