R42MH125688

This proposal addresses a significant barrier to obtaining treatment for college-ageyouth with mental disorders. Many college-age youth with impairing mental disordersremain untreated because of concerns about stigma and privacy, inconvenience and wait times, andbecause 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 personandapos;s pathology,because of the erroneous assumption that treatment need not be tailored to theindividual. This proposal aims to address this critical clinical issue. We advance that asophisticated automated online referral system would resolve all of these problems, but there is noexpert-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 Mind1nstitute is partnering with the for-profit MiResource. Assessment expertise is providedby the Child Mind Institute, which treats children and adolescents with mental healthdisorders, conducts mental health research, has acquired large assessment datasets, has in-houseexpertise in mental health assessment, and through its MATTER lab has developed novel assessmenttechnologies such as the Mindlogger data collection and assessment platform. Referralinfrastructure is provided by MiResource, a software-as-a-service solution designed to helpuniversities connect students to local mental health providers. The MATTER lab and MiResourcewill develop an automated online assessment and referral platform that uses expert-trainedmachine 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 (riskof harm, functional status, comorbidity, environment, treatment history, and attitude)applied to college studentsandapos; responses to mental health assessments. 1n Phase 1, we will (1-1)build mental health assessments into the Mindlogger platform, (1-2) build an expertreferral collection interface, and (1-3) set up a machine learning pipeline for training andtesting an updatable classification model for automated clinically appropriate, personalizedreferrals. 1n Phase 11, we will build, refine, and clinically validate ourproduct for commercialization. Specifically, we will (11-1) validate the Phase I framework on auniversity population, (11-2) integrate Mindloggerandapos;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.College-age youth do not have easy access to expert referral coordinators to getreferrals to appropriate mental health care, due to the stigma of seeing a referral coordinatorin-person, long wait times to get an intake appointment in the university counseling center,or limited or no referral counselors available in their higher-education institution. Wepropose to automate the referral process, focusing on college-age youth, by bridging onlineassessments and curated, up-to-date mental health provider networks. Our automated online referralplatform will use expert-trained machine learning to provide users with personalizedreferrals for mental health care.