NCT06834763

Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs - Effectiveness Study

Placement Success Predictor: Using Site-Customized Machine Learning Models to Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs

• Status: Enrolling By Invitation
• Phase: —
• Study type: Observational
• Enrollment: 700 (estimated)
• Sponsor: Outcome Referrals, Inc. · Industry
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

The purpose of this study is to test the effectiveness of a new clinical decision support tool, Placement Success Predictor (PSP), in a naturalistic setting. PSP will provide placement-specific predictions about the likelihood of a youth having a good outcome in each placement type at a behavioral health center using machine learning algorithms. The primary hypothesis is that clients in at least one placement within one standard deviation of the placement with the highest predicted likelihood of success will have better outcomes than the clients who were not. The secondary hypothesis is that clients' level of improvement over time will be positively correlated with the number of days they are in at least one placement within one standard deviation of the placement with the highest predicted likelihood of success.

Detailed description

In 2017, a total of 669,799 children were confirmed victims of maltreatment in the United States; of the 442,733 children in foster care, 34% have been in more than one placement and 11% are in a group home or institution. Stakes are extremely high for making the best out-of-home placement choice per child because some placement types and multiple placements are associated with poor outcomes. In the past few years, legislation has been created to guide placement decisions for children. Federal law 42 U.S. Code 675 requires that children in the care of the state are placed "in a safe setting that is the least restrictive (most family like)." In addition, the Family First Prevention Services Act signed into law by the U.S. Congress in 2018 includes measures to reduce the number of children in long-term residential settings. This effectiveness study is to assess and improve the usage of PSP in a behavioral health setting. Sample. Clients at Children's Hope Alliance (CHA) who completed the TOP, CHA's standard behavioral health assessment. The target recruitment goal is 700 clients. Methods. PSP results will be available for all clients with recent behavioral health assessment data.

Conditions

• Adolescent Well-Being
• Mental Health Wellness

Interventions

Type	Name	Description
OTHER	Clinical team access to Placement Success Predictor (PSP) results	PSP is a machine-learning based clinical decision support tool that is designed to assist clinical team members in making placement decisions for youth. PSP provides site-specific placement success prediction scores \[i.e., client's likelihood of success per placement based on machine learning models\] for each youth.

Timeline

• Start date: 2025-02-03
• Primary completion: 2025-12-31
• Completion: 2025-12-31
• First posted: 2025-02-19
• Last updated: 2025-02-19

Locations

1 site across 1 country: United States