# Automated Mental Health Referral System

> **NIH NIH R42** · MIRESOURCE, INC. · 2023 · $499,858

## Abstract

This proposal addresses a significant barrier to obtaining treatment for college-age 
youth with mental disorders. Many college-age youth with impairing mental disorders 
remain untreated because of concerns about stigma and privacy, inconvenience and wait times, and 
because universities are often unable to service all such students. Also, of critical importance, 
when referral for treatment is implemented, it is without regard to the person's pathology, 
because of the erroneous assumption that treatment need not be tailored to the 
individual. This proposal aims to address this critical clinical issue. We advance that a 
sophisticated automated online referral system would resolve all of these problems, but there is no 
expert-trained system for psychiatric referrals. We propose to automate the referral process, 
designed for college-age youth, by bridging online, mental health assessments and curated, 
up-to-date, mental health provider networks. To this end, the non-profit Child Mind 
1nstitute is partnering with the for-profit MiResource. Assessment expertise is provided 
by the Child Mind Institute, which treats children and adolescents with mental health 
disorders, conducts mental health research, has acquired large assessment datasets, has in-house 
expertise in mental health assessment, and through its MATTER lab has developed novel assessment 
technologies such as the Mindlogger data collection and assessment platform. Referral 
infrastructure is provided by MiResource, a software-as-a-service solution designed to help 
universities connect students to local mental health providers. The MATTER lab and MiResource 
will develop an automated online assessment and referral platform that uses expert-trained 
machine learning to provide users with personalized referrals for mental health care. 
Expert referrals will be based on the six dimensions of the level of Care Utilization System (risk 
of harm, functional status, comorbidity, environment, treatment history, and attitude) 
applied to college students' responses to mental health assessments. 1n Phase 1, we will (1-1) 
build mental health assessments into the Mindlogger platform, (1-2) build an expert 
referral collection interface, and (1-3) set up a machine learning pipeline for training and 
testing an updatable classification model for automated clinically appropriate, personalized 
referrals. 1n Phase 11, we will build, refine, and clinically validate our 
product for commercialization. Specifically, we will (11-1) validate the Phase I framework on a 
university population, (11-2) integrate Mindlogger's assessments into MiResource, and (11-3) 
conduct usability and quality assurance tests of the new Mindlogger plus MiResource platform, 
 to get feedback about issues related to accessibility, relevance, accuracy, and esthetics, 
and incorporate solutions in response to this feedback into a final version.

## Key facts

- **NIH application ID:** 10685663
- **Project number:** 4R42MH125688-02
- **Recipient organization:** MIRESOURCE, INC.
- **Principal Investigator:** Arno Klein
- **Activity code:** R42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $499,858
- **Award type:** 4N
- **Project period:** 2021-04-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10685663

## Citation

> US National Institutes of Health, RePORTER application 10685663, Automated Mental Health Referral System (4R42MH125688-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10685663. Licensed CC0.

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