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Automated Mental Health Referral System

自动心理健康转介系统

基本信息

批准号：
10155997
负责人：
Arno Klein
金额：
$ 14.78万
依托单位：
MIRESOURCE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10155997
关键词：
Address Adolescent Age Agreement Algorithms Appointment Attitude Caring Chi-Square Tests Child Classification Clinical Collection Computer software Counseling Data Data Collection Data Set Dimensions Environment Esthetics Feedback Female Focus Groups Future Gender Health Personnel Health Resources Healthcare Human Impairment Individual Infrastructure Institutes Institution Insurance Intake Internet Knowledge Learning Disorders Machine Learning Measures Mental Health Mental disorders Mind Modeling Pathology Patients Persons Phase Population Privacy Process Professional counselor Protocols documentation Provider Psychiatrist Psychologist Recording of previous events Research Risk Services Small Business Technology Transfer Research Social Workers Source Students System Technology Assessment Testing Time Training Universities Update Visit Wait Time Youth base biomedical referral center college commercialization comorbidity design functional status health assessment higher education improved innovation machine learning algorithm male novel provider networks quality assurance response satisfaction social stigma software as a service success telehealth tool university student usability young adult

项目摘要

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.

这项提议解决了大学生获得治疗的一个重大障碍