AI Case Study

Foster Care Tech (FCT) finds better matching foster care families for children using machine learning

Foster care system has remained the same throughout and experiences several challenges. Foster Care Tech is using machine learning to develop a system that matches characteristics of a child with that of a family to find better matches improving outcomes for everyone. Children placed using the system experienced fewer disruptions with 22.5% better placement stability.

Industry

Public And Social Sector

Ngo

Project Overview

"FCT is a team of social workers, software developers, and business people, committed to the vision of creating placement stability for all foster children and helping them achieve permanency more quickly. Placement stability is linked with positive outcomes in all areas of children’s lives, including emotional, medical, academic, and relational domains."

Reported Results

According to the company:

* Children faced fewer disruptions, experiencing 22.5% better placement stability
* Children spent 12% less time in foster care as they were reunited with their families or were adopted quickly
* Cost savings due to improved placement outcomes

Technology

Function

R And D

Product Development

Background

"Foster care system has remained the same throughout. FCT seeks to use AI to bring stability to the system and better outcomes for everyone."

Benefits

Data

"Studied over 22,000 past placement records to identify how certain characteristics affect placement stability"