Child welfare continues to be a growing concern in the United States, with the number of children in foster care having increased steadily since 2012. According to the Administration for Children and Families (ACF), approximately 437,500 children were reported to be in the foster care system in 2016. Whereas, in 2012, the number of youth in foster care was estimated at 397,000.

Adding to the problem is America’s escalating opioid crisis. More and more children are entering foster care as a result of parental drug abuse, placing a strain on an already overtaxed child welfare system. Data from the federal Adoption and Foster Care Analysis and Reporting System (AFCARS) shows drug abuse by a parent as one of the leading causes for a child to be removed from their home, with more than 92,000 children entering foster care between 2015 to 2016 for this reason alone. This influx of children in the system exacerbates existing challenges around child welfare and deepens the need to address issues surrounding foster care placement, caseworker capacity, and screening and risk assessment for neglect and abuse, in order to improve the safety and security of foster youth and support their transition into adulthood.

There is great potential for data and technology to be used to help tackle the complex problems faced by the sector, ensure the well-being of foster youth, and even reduce the number of children who end up in foster care in the first place. In recent years, many child welfare agencies have begun to explore the possibilities of using data science and predictive technology to identify ways to work more efficiently, improve safety and risk assessment processes, and better serve youth.

This month, in partnership with the Microsoft Cities Team – Civic Engagement, we will be launching two new projects with organizations serving foster youth – Think of Us (TOU) and Community Based Care of Central Florida (CBCCF). With available open data on child welfare along with data collected by these organizations, teams of DataKind’s expert pro bono data scientists will use machine learning to help both organizations improve the safety and well-being of foster youth and develop solutions that can be shared and benefit the sector as a whole.

In addition to problems faced by youth while in foster care, there is evidence showing that these children are at a greater risk as adults for substance use and abuse, homelessness, unemployment and incarceration. TOU strives to help foster youth successfully transition into healthy, stable, and thriving adults by providing the resources and support they need to do so. We’ll work closely with TOU to design classification models that will lay the foundation for building risk profiles that will allow TOU to identify and better serve at-risk foster youth by providing more targeted services and support to help them successfully transition into adulthood.

Another factor affecting the safety and well-being of foster youth, and a positive transition into adulthood, is found to be the time a child spends with his or her assigned caseworker. There is anecdotal evidence showing that child outcomes are impacted negatively by caseworker turnover. As such, caseworker retention is a top priority for organizations like CBCCF, a national leader in progressive child welfare systems, that provides fostering and adoption services for youth. Our team will help CBCCF gain a greater understanding of the complexity of workers’ caseloads and address operational and logistical challenges by optimizing caseworker activities in order to improve outcomes for children in care.

As we discovered from an initial DataDive in December 2016 with the Annie E. Casey Foundation, that focused on related topics around improving and supporting youth in America, the sharing of data among youth service programs and agencies is crucial as it can show a more comprehensive picture of youth and youth in multiple systems. Such data collaboration could better inform future research, help unearth additional insights and provide the information necessary to develop tools that could aid in improving the lives of millions of foster children at risk of poor educational, economic, social and health outcomes.

This new collaboration offers an opportunity for DataKind and Microsoft to further build on these initial learnings and apply data science and emerging capabilities in machine learning to gain aditional insights and help optimize processes. Bringing together multiple project partners to share their data, expertise and resources, will show how working side by side to co-design data-driven solutions can help tackle critical social issues like child welfare.

We look forward to diving into these projects with our partners and helping work toward developing a brighter future for foster children across the country.