An Expert-Derived Data-Driven Approach to Identifying Medicaid Children at Risk of Out-of-Home Placement: Development, Application, and Data-Sharing Considerations.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	An Expert-Derived Data-Driven Approach to Identifying Medicaid Children at Risk of Out-of-Home Placement: Development, Application, and Data-Sharing Considerations.
Συγγραφείς:	Hardy RY; The Abigail Wexner Research Institute, Nationwide Children's Hospital.; Department of Pediatrics, The Ohio State University College of Medicine., Ciccone DP; The Abigail Wexner Research Institute, Nationwide Children's Hospital., Bailey E; The Ohio Colleges of Medicine Government Resource Center, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH., Bozada S; The Ohio Colleges of Medicine Government Resource Center, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH., Donnelly E; The Abigail Wexner Research Institute, Nationwide Children's Hospital., Tossone K; The Ohio Colleges of Medicine Government Resource Center, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH., Chisolm DJ; The Abigail Wexner Research Institute, Nationwide Children's Hospital.; Department of Pediatrics, The Ohio State University College of Medicine.
Πηγή:	Medical care [Med Care] 2026 Oct 01; Vol. 64 (10), pp. 696-704. Date of Electronic Publication: 2026 Sep 08.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 0230027 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1537-1948 (Electronic) Linking ISSN: 00257079 NLM ISO Abbreviation: Med Care Subsets: MEDLINE
Imprint Name(s):	Publication: Hagerstown, MD : Lippincott Williams & Wilkins Original Publication: Philadelphia, etc., Lippincott.
Ιατρικοί όροι (MeSH):	Medicaid/statistics & numerical data , Information Dissemination/methods , Child Welfare/statistics & numerical data , Foster Home Care/statistics & numerical data, Risk Assessment/methods ; Humans ; United States ; Child ; Algorithms ; Ohio ; Child, Preschool
Περίληψη:	Background: Out-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. Objectives: To 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. Findings: A 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. Conclusions: Data-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. (Copyright © 2026 The Author(s). Published by Wolters Kluwer Health, LLC.)
Competing Interests:	The authors declare no conflict of interest.
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Contributed Indexing:	Keywords: Medicaid; administrative data; claims data; data linkage; data sharing; patient-centered outcomes
Entry Date(s):	Date Created: 20260908 Date Completed: 20260908 Latest Revision: 20260910
Update Code:	20260910
PubMed Central ID:	PMC13552560
DOI:	10.1097/MLR.0000000000002307
PMID:	42709622
Βάση Δεδομένων:	MEDLINE

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ISSN:	1537-1948
DOI:	10.1097/MLR.0000000000002307