Clinical trial NCT06834763: Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs - Effectiveness Study
Official 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
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Adolescent Well-Being
Mental Health Wellness
Lead sponsor: Outcome Referrals, Inc.
Sponsor class: INDUSTRY
Collaborators: name: National Institutes of Health (NIH)
name: Children's Hope Alliance
First posted: 2025-02-19
Enrollment: 700
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Clients at Children's Hope Alliance

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.

Eligibility criteria: Inclusion Criteria:

* Completed TOP CS assessment

Exclusion Criteria:

* None

Primary outcomes:
measure: 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.
timeFrame: At baseline and every 30 days up to approximately 3 months
Secondary outcomes:
Other outcomes:
Locations:
city: Framingham
country: United States
facility: Outcome Referrals, Inc.
state: Massachusetts
Study officials:
References:
citation: 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.
citation: 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.