# Placement Success Predictor: Using Site-Customized Machine Learning Models to Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs

> **NIH NIH R43** · OUTCOME REFERRALS INC · 2021 · $220,720

## Abstract

ABSTRACT
 Hundreds of thousands of children (669,799) were confirmed victims of maltreatment in the United States
in 2017; in that same year, of the 442,733 children in foster care, 34% had been in more than one placement
and 11% were in a group home or institution. Out-of-home placement decisions have extremely high stakes
for the present and future well-being of these children because some placement types, and multiple
placements, are associated with poor outcomes. With the Family First Prevention Act, states will be required to
pay the average $88,000 per year to keep a child in residential care if that high level of care is not authorized.
But which children require --and, more importantly, would benefit-- from a placement in residential care?
Decision-making support tools currently used by states to recommend specific level of care (LOC) placements
for children do not maximize the rich data and innovative methodological approaches that are being explored in
other fields like medicine. In addition, structured decision making (SDM) has been used to guide decisions
about risk in child welfare settings but, in comparison to predictive modelling, SDM is limited by the use of a
smaller group of factors to make recommendations. Outcome Referrals, Inc. has employed sophisticated
machine learning techniques over the past 10 years to risk-adjust behavioral health outcome data for clients
using baseline characteristics. Initial models predicted more than 30% of the outcome variance (i.e., it was
possible to predict 30% of the variance in how depressed a client would be at follow-up). The next model
improved that prediction to more than 50%, and our latest model has increased this to an average of 71%.
With the assistance of Phase I NIH SBIR funding, we plan to improve the success rates of children in the child
welfare system with an innovative, scientifically-derived product called “Placement Success Predictor.” To
guide level-of-care decision-making, this product will use site-customized, machine learning algorithms to
predict the likelihood of an adolescent having a good outcome in a particular placement type in a specific
community. We have preliminary evidence supporting the feasibility of developing these models based on
work supported by the Duke Endowment Foundation. During this six-month Phase I project, we propose to 1)
validate these preliminary machine learning models by applying them to new client data from our partner
behavioral health organization, 2) explore options for sharing results of these models to facilitate their use in
practice (e.g., aggregate predictions across different domains in a weighted way), 3) assess key stakeholder
satisfaction with a new prototype, and 4) develop and test customized models for multiple placement types with
a state-wide child welfare and juvenile justice dataset.

## Key facts

- **NIH application ID:** 10138072
- **Project number:** 1R43MH125486-01
- **Recipient organization:** OUTCOME REFERRALS INC
- **Principal Investigator:** Kimberlee Jean Trudeau
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $220,720
- **Award type:** 1
- **Project period:** 2021-03-15 → 2022-09-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10138072

## Citation

> US National Institutes of Health, RePORTER application 10138072, Placement Success Predictor: Using Site-Customized Machine Learning Models to Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs (1R43MH125486-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10138072. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*