Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs - Effectiveness Study
Placement Success Predictor: Using Site-Customized Machine Learning Models to Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs
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.
Study Overview
Status
Conditions
Intervention / Treatment
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.
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Massachusetts
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Framingham, Massachusetts, United States, 01701
- Outcome Referrals, Inc.
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Completed TOP CS assessment
Exclusion Criteria:
- None
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
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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
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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.
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Not In PSP-Recommended Placement
Clients not in placement with PSP results within one standard deviation of the highest predicted likelihood of success for that client at follow up
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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.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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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
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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). |
At baseline (within 2 weeks of study start) and approximately 60-120 days later
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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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
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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). |
At baseline (within 2 weeks of study start) and approximately 60-120 days later
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Collaborators and Investigators
Sponsor
Publications and helpful links
General Publications
- 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.
- 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.
- Baxter, E. E., Alexander, P. C., Kraus, D. R., Bentley, J. H., Boswell, J. F., & Castonguay, L. G. (2016). Concurrent validation of the Treatment Outcome Package (TOP) for children and adolescents. Journal of Child and Family Studies, 25, 2415-2422.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- 2R44MH125486-02A1-Aim 2B
- 2R44MH125486-02A1 (U.S. NIH Grant/Contract)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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