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Completed

NCT Number: NCT06815562

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

The purpose of this randomized clinical trial is to test the efficacy of a new clinical decision support tool, Placement Success Predictor (PSP). PSP will provide placement-specific predictions about the likelihood of a youth having a good outcome in each placement type using machine learning algorithms.

The primary hypothesis is that if clinical team members have access to PSP results for youth in the experimental group, these youth will have better outcomes at the 3-month follow-up compared to youth in the control group.

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Key information

Conditions

Adolescent Well-Being Mental Health Wellness

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

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 study is an effort to develop and test a science-based clinical decision support tool using behavioral health data collected through standard clinical practice.

A randomized controlled trial (RCT) design will be used to assess efficacy of clinical team access to Placement Success Predictor (PSP) on child welfare clients' well-being outcomes and healthcare costs.

Sample. Clients at the State of Iowa Department of Health and Human Services (Iowa HHS) are the sample for this efficacy study.

Randomization. The outcome of a single coin toss was applied to an undisclosed algorithm for the client's record number to determine who gets assigned to the experimental group (i.e., client has PSP results).

Methods. The Treatment Outcome Package (TOP), a behavioral health assessment, is a standard part of care delivered in Iowa and its completion is required by the state. Iowa HHS clinical teams were provided PSP results for clients in the experimental condition. A request for a waiver of consent for this study was approved by the WCG IRB.

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.

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

  • National Institute of Mental Health (NIMH)
  • State of Iowa Department of Health and Human Services

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 7, 2025
Registry last updated
Jul 8, 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.

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