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.

该提案解决了大学年龄获得治疗的重大障碍患有精神障碍的青少年。许多大学生患有精神障碍由于担心耻辱和隐私、不便和等待时间而仍未得到治疗，以及因为大学往往无法为所有此类学生提供服务。此外，至关重要的是，当实施转诊治疗时，不考虑该人的病理情况，因为错误的假设认为治疗不需要根据具体情况进行调整个人。该提案旨在解决这一关键的临床问题。我们提出，复杂的自动在线推荐系统可以解决所有这些问题，但目前还没有经过专家培训的精神病转诊系统。我们建议自动化推荐流程，专为大学年龄的青少年设计，通过在线、心理健康评估和策划，最新的心理健康提供者网络。为此，非营利组织Child Mind 1nstitute 正在与营利性 MiResource 合作。提供评估专业知识儿童心理研究所 (Child Mind Institute) 负责治疗儿童和青少年的心理健康问题疾病，进行心理健康研究，获得了大量的评估数据集，拥有内部心理健康评估方面的专业知识，并通过其物质实验室开发了新颖的评估 Mindlogger 数据收集和评估平台等技术。推荐基础设施由 MiResource 提供，这是一种软件即服务解决方案，旨在帮助大学将学生与当地的心理健康提供者联系起来。 MATTER 实验室和 MiResource 将开发一个自动化的在线评估和推荐平台，该平台使用经过专家培训的机器学习为用户提供个性化的心理健康护理推荐。专家推荐将基于护理利用系统水平的六个维度（风险危害、功能状态、合并症、环境、治疗史和态度）应用于大学生对心理健康评估的反应。 1n 第 1 阶段，我们将 (1-1) 将心理健康评估纳入 Mindlogger 平台，(1-2) 建立专家推荐收集接口，以及（1-3）建立机器学习管道以进行培训和测试可更新的分类模型，以实现自动化、临床适用、个性化推荐。 1n 第 11 阶段，我们将构建、完善并临床验证我们的产品用于商业化。具体来说，我们将 (11-1) 在大学人口，(11-2) 将 Mindlogger 的评估整合到 MiResource 中，以及 (11-3) 对新的 Mindlogger plus MiResource 平台进行可用性和质量保证测试，获取有关可访问性、相关性、准确性和美观问题的反馈，并将针对此反馈的解决方案纳入最终版本。