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Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs - Effectiveness Study
Sponsor: Outcome Referrals, Inc.
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.
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
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
700
Start Date
2025-02-03
Completion Date
2025-12-31
Last Updated
2025-02-19
Healthy Volunteers
No
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.
Locations (1)
Outcome Referrals, Inc.
Framingham, Massachusetts, United States