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

NCT ID: NCT06834763

Last Updated: 2025-02-19

Basic Information

• Recruitment Status: ENROLLING_BY_INVITATION
• Total Enrollment: 700 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-02-03
• Study Completion Date: 2025-12-31

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.

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

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

Clinical team access to Placement Success Predictor (PSP) results

Intervention Type: 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.

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

Clinical team access to Placement Success Predictor (PSP) results

Intervention Type: 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.

Interventions

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.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Completed TOP CS assessment

Exclusion Criteria

• None

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institutes of Health (NIH)

NIH

Sponsor Role: collaborator

Children's Hope Alliance

UNKNOWN

Sponsor Role: collaborator

Outcome Referrals, Inc.

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Outcome Referrals, Inc.

Framingham, Massachusetts, United States

Countries

United States

References

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.

Reference Type: BACKGROUND

PMID: 15546147 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 37841819 (View on PubMed)

Other Identifiers

2R44MH125486-02A1

Identifier Type: NIH

Identifier Source: secondary_id

View Link

2R44MH125486-02A1-Aim 2B

Identifier Type: -

Identifier Source: org_study_id