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

NCT ID: NCT06815562

Last Updated: 2025-02-17

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ENROLLING_BY_INVITATION

Clinical Phase

NA

Total Enrollment

300 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-02-03

Study Completion Date

2025-12-31

Brief Summary

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The purpose of this randomized clinical trial is to test the efficacy of a new clinical decision support tool, Placement Success Predictor (PSP). PSP will provide placement-specific predictions about the likelihood of a youth having a good outcome in each placement type using machine learning algorithms.

The primary hypothesis is that if clinical team members have access to PSP results for youth in the experimental group, these youth will have better outcomes at the 3-month follow-up compared to youth in the control group.

Detailed Description

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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 study is an effort to develop and test a science-based clinical decision support tool using behavioral health data collected through standard clinical practice.

A randomized controlled trial (RCT) design will be used to assess efficacy of clinical team access to Placement Success Predictor (PSP) on child welfare clients' well-being outcomes and healthcare costs.

Sample. Clients at the State of Iowa Department of Health and Human Services (Iowa HHS) are the sample for this efficacy study.

Randomization. The outcome of a single coin toss was applied to an undisclosed algorithm for the client's record number to determine who gets assigned to the experimental group (i.e., client has PSP results).

Methods. The Treatment Outcome Package (TOP), a behavioral health assessment, is a standard part of care delivered in Iowa and its completion is required by the state. Iowa HHS clinical teams will be provided PSP results for clients in the experimental condition. A request for a waiver of consent for this study was approved by the WCG IRB.

Conditions

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Adolescent Well-Being Mental Health Wellness

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Efficacy (randomized clinical trial) study
Primary Study Purpose

HEALTH_SERVICES_RESEARCH

Blinding Strategy

NONE

Study Groups

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Access to PSP site-specific placement prediction scores for that youth

The PSP system will provide site-specific placement success prediction scores \[i.e., client's likelihood of success per placement based on machine learning models\] for each youth randomized to this condition in the efficacy study.

Group Type EXPERIMENTAL

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.

No PSP site-specific placement prediction scores for that youth

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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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.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Completed TOP CS assessment

Exclusion Criteria

* None
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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National Institute of Mental Health (NIMH)

NIH

Sponsor Role collaborator

State of Iowa Department of Health and Human Services

UNKNOWN

Sponsor Role collaborator

Outcome Referrals, Inc.

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Outcome Referrals, Inc.

Framingham, Massachusetts, United States

Site Status

Countries

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United States

References

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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)

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)

Other Identifiers

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2R44MH125486-02A1

Identifier Type: NIH

Identifier Source: secondary_id

View Link

2R44MH125486-02A1-Aim 2A

Identifier Type: -

Identifier Source: org_study_id

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