ArticleMedical care2026
An Expert-Derived Data-Driven Approach to Identifying Medicaid Children at Risk of Out-of-Home Placement: Development, Application, and Data-Sharing Considerations.
Article in Medical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
7 authors.
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Abstract
backgroundOut-of-home placement (OOHP) through child welfare, residential substance treatment, or extended inpatient care has life-altering impacts on child health and generates an outsized proportion of pediatric Medicaid costs. Early risk identification may prevent OOHP but is challenged by the difficulty of navigating state and federal data sharing regulations and multiple disconnected data systems. Identifying ways to streamline and simplify this process are critical to OOHP prevention.
objectivesTo describe the development and implementation of an expert-derived data-driven algorithm to identify OOHP risk among Medicaid-enrolled children residing in 2 Ohio counties between 2022 and 2024 (n=27,000), discuss practical considerations for data sharing and linkage across multiple agencies, and identify lessons learned.
findingsA cross-sector team of government and academic partners developed a 3-category OOHP risk stratification algorithm incorporating data from Medicaid claims, child welfare information systems, area-level social determinants of health, and patient-reported health risk assessments as part of Ohio's Integrated Care for Kids Model. Substantial administrative and legal processes were required to link data sources. Lessons learned include: (1) understanding legal, political, and security structures associated with use of administrative data is critical; (2) strong working relationships with data partners can ensure success; (3) as administrative data are dynamic and may be retroactively updated, appropriate lookback periods are necessary for accuracy; and (4) while executing data use agreements can be challenging, the relationships built can have lasting benefits.
conclusionsData-driven risk stratification models have the potential to reduce cost, time, and redundancies in identifying families who would benefit from OOHP prevention supports. Balancing data-sharing challenges with the value gained from linking data is vital.
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