# 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 R44** · OUTCOME REFERRALS INC · 2024 · $1,110,421

## Abstract

ABSTRACT
Hundreds of thousands of children (669,799) were confirmed victims of maltreatment in the United States in
2017. In that same year, 34% of the 442,733 children in foster care 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. 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 their current characteristics. We have evidence of the predictive validity of this
approach for generating risk-adjusted scores via the PCORI-funded RCT in which clients were matched with
clinicians who had previous clients who did well in treatment. With the assistance of NIH SBIR funding, we
plan to improve the success rates of children in the child welfare system with an innovative, scientifically-
derived tool called “Placement Success Predictor” to guide level-of-care decision-making. This product will use
machine learning algorithms to predict individualized outcomes for children and adolescents in a particular
placement type. During the Phase I project, we developed and validated machine learning models to predict
the probability of each youth’s success within placement types and conducted usability testing of a prototype to
assess potential barriers to implementation. The likelihood of success recommendations provided by the
machine learning models developed for the “Placement Success Predictor” clinical decision-making support
tool were associated with improved youth well-being (i.e., those children that were placed in the Placement
Success Predictor-recommended placement had four times higher success rates). Potential future users found
the prototype of this tool easy to use. The machine learning models appear to distinguish clients who will do
well in various placement types and suggest that only 10% of clients would be successful in higher-cost level of
care. With the Family First Prevention Act, states are now 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. We submit this tool could help
improve overall outcomes and reduce healthcare costs when and i...

## Key facts

- **NIH application ID:** 10819864
- **Project number:** 2R44MH125486-02A1
- **Recipient organization:** OUTCOME REFERRALS INC
- **Principal Investigator:** Kimberlee Jean Trudeau
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,110,421
- **Award type:** 2
- **Project period:** 2021-03-15 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10819864

## Citation

> US National Institutes of Health, RePORTER application 10819864, Placement Success Predictor: Using Site-Customized Machine Learning Models to Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs (2R44MH125486-02A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10819864. Licensed CC0.

---

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