Leveraging Predictive Analytics to Improve Placement Stability in U.S. Foster Care Systems

ABSTRACT : This study examined the role of predictive analytics in improving placement stability in U.S. foster care systems, with specific objectives focused on how predictive risk assessment and machine learningbased decision support reduce placement disruptions. An exploratory research design was adopted to explore existing knowledge on the subject and identify key patterns from prior studies. The study relied on secondary data obtained from published academic journals, peer-reviewed articles, conference papers, and policy reports relating to foster care systems and predictive analytics. Data were analysed using thematic analysis. Findings revealed that: predictive risk assessment improves placement stability by enabling early and more accurate identification of children at risk of placement disruption through behavioural, emotional, and demographic risk profiling; machine learning-based placement decision support reduces placement disruptions by improving placement matching accuracy and enabling data driven decisions that align children with more suitable and stable foster care environments. In conclusion, foster care practice is moving toward a more coordinated and information rich approach, where stability is influenced by the ability to interpret complex data patterns and apply them to real world placement decisions in a more systematic and consistent manner. The study recommended that state child welfare agencies in the United States should integrate structured predictive risk assessment tools into their routine case management systems so that social workers can identify children at high risk of placement disruption earlier and respond with timely support before instability occurs.

KEYWORDS: Predictive Analytics, Placement Stability, Foster Care System