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Doing it Better: Predictive Intelligence Helps Provide the Right Care for At-Risk Youth

“It takes a village to raise a child.” In the past, young people gained social and emotional intelligence from deep involvement within their families and community. But that same kind of village is not available to many of today’s youth. Youth now experience less social connection and more isolation, leading to their increased anxiety and decreased well-being.

According to Measure of America, about 1 in 9 teens and young adults are disconnected, meaning they are neither working nor in school, which increases their likelihood of becoming system-involved. Currently, more than 48,000 youth in the US are placed in facilities away from their home due to juvenile justice involvement.

How can we help these youth achieve more promising futures?

Prevention Strategies

State and local policymakers need to invest in effective prevention strategies for at-risk youth. Prevention services, such as those listed below, that offer the best opportunity to reduce the likelihood of youth becoming embedded in the child welfare and juvenile justice systems:

  • Community-based treatment and support
  • Mental Health and behavioral health services
  • School-based intervention programs
  • Substance abuse programs
  • Diversion programs

Among these prevention strategies, the best outcomes are achieved from evidence-based practices and programs. This approach involves utilizing different analysis models to assess the available evidence on program effectiveness that in turn guide protocols for best practices. Evidence-based practices also receive support from a broad research base that includes data around sociological, psychological and biological research from multiple studies.

But what happens when these prevention strategies aren’t enough?

Securing Better Outcomes for Kids within the System

Despite the proliferation of prevention strategies aimed at stemming deeper integration into the juvenile justice system, the recidivism rate for juvenile offenders is sometimes as high 76% within three years, and 84% within five years, according to data from the Council of State Governments Justice Center.

One area of promise is the ability to leverage predictive intelligence to secure better outcomes for kids who are system-involved. Currently, multiple data points related to behavioral, socio-economic, psychological and physical health factors are captured and available within the child welfare, juvenile justice and behavioral health systems. While these systems are rich in data, they are poor in analysis. As yet, most counties, states and NGO’s haven’t identified the best way to utilize data wealth at a high level to improve overall program and system effectiveness. What if these data points were leveraged for risk assessment, program matching, quality analysis and learning activity?

Fortunately, complex problems can be addressed by meaningful data models. A data-driven approach can provide the right care, placement and services for the right duration to improve the success rates of at-risk youth already within the system.

Benefits from predictive intelligence and a data-centric approach

Predictive intelligence offers the promise of more effective program decisions that are rooted in data and directed at achieving the best result for child and family. Within the child welfare and juvenile justice systems, different data analysis models include:

  • At-risk youth profiles – Tools that leverage broad demographic, prior placement, socio-economic and historical program data can aid practitioners in decision making around programming
  • Service/program match – Leveraging historical organizational outcomes can assist in predicting the right program for a youth and improving recidivism rates
  • Risk assessment – Data analysis models can assess a youth’s criminogenic risk factors to determine appropriate intervention or treatment

The monetary benefit of these predictive intelligence tools in preventing juvenile delinquency should not be underestimated. A study by Professor Mark A. Cohen of Vanderbilt University estimated the present value of saving a 14-year-old high-risk youth from involvement in the justice system ranged from $2.2 to $3 million.

Predictive intelligence is a key component of FirstMatch®. FirstMatch is a machine learning tool that uses data to match a youth with the most appropriate program for him or her the very first time, leading to less trauma and better outcomes. FirstMatch users can make an informed program or service choice by leveraging appropriate data already within their ecosystem.

To learn more, sign up for a demo of FirstMatch today.

Shawn Peck