The Problem
Over 400,000 children are in the U.S. foster care system at any given time, and nearly 38% of placements end in disruption. Each move compounds trauma, breaks attachments, and interrupts the stability children need to heal. Social workers face high caseloads, fragmented records, and reactive systems that don't provide predictive tools.
Our Approach
CareAssist centralizes case information, integrates an AI-driven early warning system, and provides an interactive dashboard. Our ensemble machine learning model predicts which children are at high risk of placement disruption, with transparent explanations for each prediction. The platform also includes an AI chat assistant to support caseworkers in understanding and acting on each case.
Impact
By surfacing urgent cases and providing actionable insights, CareAssist empowers social workers to intervene proactively, reduces information loss during placements, and helps families and children achieve better stability and outcomes.
CareAssist helps social workers prioritize high-risk cases across large caseloads by surfacing real-time risk scores and key contributing factors. Instead of relying on fragmented notes and infrequent check-ins, caseworkers can quickly identify which children need immediate attention and understand why.
Foster parents gain a structured way to share updates, documents, and observations, ensuring important information is captured and accessible. This improves communication with caseworkers and helps maintain continuity of care across placements.
For youth transitioning out of the foster care system, stability and continuity of information are critical. CareAssist supports better long-term outcomes by ensuring their history, needs, and risk indicators are consistently tracked and not lost across system transitions.
Supervisors can monitor caseload trends and risk distribution across their teams, enabling more effective oversight and resource allocation. Aggregate insights help identify systemic patterns and support proactive decision-making at a higher level.
Data Analysis, Data Engineering, Model Evaluation
Subject Matter Expert, Project Manager, Infrastructure & Data Engineering
Data Analysis, ML Engineer, UI Design
Product Manager, ML Engineer, UI Design
Feel free to reach out to us with any questions!
