Skip to main content
OpenTrials
Completed

NCT Number: NCT06834763

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

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.

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Adolescent Well-Being Mental Health Wellness

Sex eligibility

All sexes

Study type

Observational

Primary location

Outcome Referrals, Inc.

Framingham, Massachusetts, 01701, United States

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Completed TOP CS assessment

Exclusion criteria

  • None

Treatment and study plan

Clinical team access to Placement Success Predictor (PSP) results

Other

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.

Primary outcomes

  1. Mean Difference in Average Domain Z-scores Across Raters Within Two Weeks on the Clinical Scale of the Treatment Outcome Package (TOP-CS) Between a) Baseline and b) Follow-up

    Time frame: At baseline (within 2 weeks of study start) and approximately 60-120 days later

    The Child Treatment Outcome Package (TOP-CS) is a 48-item scale for children (ages 3 - 18) that assesses 13 domains. The Adolescent TOP-CS is a 58-item scale for adolescents (ages 11 - 21) that assesses 12 domains. TOP-CS assesses the client's past 2-week experience on domains including Depression, Violence, and Suicidality (scores are risk-adjusted for case mix variables assessed via 37 items on the companion TOP-Case Mix form regarding stressful life events, comorbidity). Participants answer "All" to "None of the Time" for each item on a 6-point Likert scale.

    A domain z-score of 0 represents the general population mean. Domain z-scores are averaged into a summary score per participant. Higher (more positive) average z-scores indicate greater symptom severity and lower behavioral well-being (a worse outcome). The value reported is the mean difference in this average z-score between baseline and follow-up; a negative mean difference indicates improvement (reduced severity).

Secondary outcomes

  1. Mean Difference Between the Average Risk-adjusted Predicted TOP-CS Total Score Across Raters at Study Baseline and the Actual Average TOP-CS Total Score Across Raters at Follow-up

    Time frame: At baseline (within 2 weeks of study start) and approximately 60-120 days later

    The Child Treatment Outcome Package (TOP-CS) is a 48-item scale for children (ages 3 - 18) that assesses 13 domains. The Adolescent TOP-CS is a 58-item scale for adolescents (ages 11 - 21) that assesses 12 behavioral health domains. Participants answer "All" to "None of the Time" for each item on a 6-point Likert scale.

    The TOP-CS Total Score is computed by averaging item responses. The range is 48 to 288 for the Child TOP-CS and 56 to 336 for the Adolescent TOP-CS. Higher Total Scores represent better behavioral well-being (a better outcome). Total Scores are averaged across raters per participant. The value reported is the mean difference between each participant's model-predicted, risk-adjusted Total Score (the outcome expected given their baseline profile) and their actual observed Total Score at follow-up. A positive mean difference indicates that the actual follow-up outcome exceeded the model-predicted outcome (i.e., the participant did better than predicted).

Sponsors and collaborators

Lead sponsor

Outcome Referrals, Inc.

Industry

Collaborators

  • Children's Hope Alliance
  • National Institutes of Health (NIH)

Registry information

Official study title

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

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Feb 19, 2025
Registry last updated
Jul 9, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.