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

NCT06834763 | Enrolling By Invitation

• 2 other identifiers: 2R44MH125486-02A1-Aim 2B2R44MH125486-02A1
• Study Type: observational
• Target: 700
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Adolescent Well-Being; Mental Health Wellness

Outcome Measures

Primary Outcomes (1)

• Mean difference on average z-scores across raters within two weeks on the Clinical Scale of the Treatment Outcome Package (TOP-CS) between a) the beginning of the study (between February and October 2025) and b) approximately 3 months later.

  TOP is a comprehensive well-being assessment that is used in behavioral health and child welfare settings. The Child TOP Clinical Scale (TOP-CS) is a 58-item scale for children (ages 3 - 18) that assesses 13 domains. The Adolescent TOP Clinical Scale (TOP-CS) is a 48-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. The z-scores (standard deviation units relative to the general population mean for each domain) will be averaged together to create one summary score. Higher scores suggest higher severity/lower behavioral well-being.

  At baseline and every 30 days up to approximately 3 months

Study Arms (2)

In PSP-Recommended Placement

Clients in placement with PSP results within one standard deviation of the highest predicted likelihood of success for that client at follow up

Other: Clinical team access to Placement Success Predictor (PSP) results

Not In PSP-Recommended Placement

Clients not in placement with PSP result within one standard deviation of the highest predicted likelihood of success for that client at follow up

Other: Clinical team access to Placement Success Predictor (PSP) results

Interventions

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.

In PSP-Recommended Placement; Not In PSP-Recommended Placement

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Clients at Children's Hope Alliance

You may qualify if:

• Completed TOP CS assessment

You may not qualify if:

• None

Sponsors & Collaborators

• Outcome Referrals, Inc.: lead
• National Institutes of Health (NIH): collaborator
• Children's Hope Alliance: collaborator

Study Sites (1)

Outcome Referrals, Inc.

Framingham, Massachusetts, 01701, United States

Location

Related Publications (2)

• Kraus DR, Seligman DA, Jordan JR. Validation of a behavioral health treatment outcome and assessment tool designed for naturalistic settings: The Treatment Outcome Package. J Clin Psychol. 2005 Mar;61(3):285-314. doi: 10.1002/jclp.20084.

  PMID: 15546147 BACKGROUND

• Trudeau KJ, Yang J, Di J, Lu Y, Kraus DR. Predicting Successful Placements for Youth in Child Welfare with Machine Learning. Child Youth Serv Rev. 2023 Oct;153:107117. doi: 10.1016/j.childyouth.2023.107117. Epub 2023 Aug 4.

  PMID: 37841819 BACKGROUND

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 13, 2025
• First Posted: February 19, 2025
• Study Start: February 3, 2025
• Primary Completion: December 31, 2025
• Study Completion: December 31, 2025
• Last Updated: February 19, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)