Enhancing the quality of CBT in community mental health through AI-generated fidelity feedback

通过人工智能生成的保真度反馈提高社区心理健康领域 CBT 的质量

• 批准号: 10674481
• 负责人: David Charles Atkins
• 金额: $ 61万
• 依托单位: LYSSN.IO, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-05 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10674481
• 关键词: Administrator; Adult; American; Behavioral; Client; Clinic; Clinical; Clinical Research; Code; Cognitive Therapy; Collaborations; Communities; Community Practice; Complex; Computer software; Consumption; Counseling; Data; Development; Dropout; Educational process of instructing; Effectiveness; Engineering; Evaluation; Feedback; Focus Groups; Foundations; Future; Goals; Health Insurance Portability and Accountability Act; Health Technology; Healthcare Systems; Human; Implementation readiness; Institution; Interview; Investments; Machine Learning; Major Depressive Disorder; Mental Health; Mental Health Services; Mental disorders; Methodology; Methods; Monitor; National Institute of Mental Health; Outcome; Patients; Performance; Persons; Phase; Pilot Projects; Policies; Practice Guidelines; Professional Practice; Protocols documentation; Provider; Psychotherapy; Qualifying; Randomized; Readiness; Reporting; Research; Resources; Science; Secure; Services; Small Business Technology Transfer Research; Software Tools; Speech; Standardization; Strategic Planning; Supervision; System; Technology; Testing; Time; Training; Training Programs; Training Support; Treatment outcome; Universities; Vision; Work; addiction; artificial intelligence algorithm; behavioral health; cloud based; cloud platform; cognitive enhancement; commercialization; community setting; cost; cost efficient; dashboard; design; digital tool; disability; effectiveness evaluation; effectiveness-implementation randomized trial; effectiveness/implementation hybrid; evidence base; implementation science; improved; innovation; motivational enhancement therapy; phase II trial; practice setting; prototype; quality assurance; scale up; service delivery; services as usual; signal processing; skills; software as a service; software development; software systems; speech processing; symptomatic improvement; technology development; telehealth; therapeutic development; tool; usability; user centered design; web platform

Each year, millions of Americans receive evidence-based psychotherapies (EBPs) such as cognitive behavioral therapy (CBT) for the treatment of mental and behavioral health problems. Yet, at present, there is no scalable method for evaluating the quality of psychotherapy services. In research settings, human-based behavioral coding methods are used, but these are time consuming, costly, and rarely used in real-world clinical settings. Thus, EBP quality and effectiveness is unmeasured and unknown. The current, fast-track STTR proposal will develop and evaluate an AI-based software system (LyssnCBT) that will automatically estimate CBT fidelity from an audio recording of a CBT session. Importantly, the current work builds from Lyssn’s previous, successful work in developing an automated system for evaluating motivational interviewing (MI), and previous research showing that AI algorithms can accurately estimate CBT fidelity. Lyssn.io, Inc., (“Lyssn”) is a start-up developing AI-based technologies to support training, supervision, and quality assurance of evidence-based counseling. Our goal is to develop innovative health technology solutions that are objective, scalable, and cost efficient. Lyssn offers a HIPAA-compliant, cloud-based platform for secure recording, sharing, and reviewing of therapy sessions, which includes AI-generated metrics for MI. The proposed LyssnCBT tool will build from and be integrated into this core platform. Lyssn is partnering with Dr. Torrey Creed and the Penn Collaborative, which has a 14+ year track record of gold-standard CBT training and supervision, including more than 100 community agencies with almost 900 providers. The expertise, relationships, and amassed data -- more than 8,000 recorded sessions and more than 3,000 rated for CBT fidelity -- form the clinical foundation for the current research. Phase I will work from an existing AI-CBT prototype to develop LyssnCBT. Core activities include user-centered design focus groups and interviews with community mental health (CMH) therapists, supervisors, and administrators, which will inform the design and development of LyssnCBT. LyssnCBT will be evaluated for usability and implementation readiness in a final stage of Phase I. Phase II will conduct a field-based usability trial and a stepped-wedge, hybrid implementation-effectiveness randomized trial (N = 1,850 CMH clients) to evaluate the effectiveness of LyssnCBT to improve therapist CBT skills and client outcomes, and to reduce client drop-out. Analyses will also examine the hypothesized mechanism of action underlying LyssnCBT. The research is strongly aligned with NIMH’s 2020 Strategic Plan and its emphasis on a computational approach to scaling up treatment delivery and monitoring. Successful execution will provide automated, scalable CBT fidelity feedback for the first time ever, supporting high-quality training, supervision, and quality assurance, and providing a core technology foundation that could support a range of EBPs in the future.

每年，数百万美国人接受循证心理治疗（EBP），例如认知 行为疗法（CBT）治疗精神和行为健康问题。但是，目前有 没有评估心理治疗服务质量的可扩展方法。在研究环境中，基于人类 使用了行为编码方法，但是这些方法是耗时，昂贵且很少在现实世界中使用的 临床环境。那是EBP质量和有效性未被计算和未知。当前的快速轨道 STTR建议将开发和评估基于AI的软件系统（LYSSNCBT），该系统将自动 从CBT会话的音频记录中估算CBT保真度。重要的是，当前的工作是由 Lyssn以前成功地开发自动化系统来评估动机面试的工作 （MI）和先前的研究表明，AI算法可以准确估计CBT保真度。 Lyssn.io，Inc。（“ Lyssn”）是一家开发基于AI的技术，以支持培训，监督， 以及基于证据的咨询的质量保证。我们的目标是开发创新的卫生技术 客观，可扩展且具有成本效益的解决方案。 Lyssn提供了符合HIPAA的基于云的平台 用于对治疗课程的安全记录，共享和审查，其中包括AI生成的MI指标。 拟议的LyssnCBT工具将根据并集成到此核心平台中。 Lyssn正在与 托里·克里德（Torrey Creed）博士和宾夕法尼亚（Penn）合作公司，其金标准CBT培训有14年以上的记录 和监督，包括100多个拥有近900个提供商的社区机构。专业知识， 关系和积累数据 - 有8,000多个记录的会话和3,000多个CBT评级 保真度 - 构成当前研究的临床基础。 第一阶段将从现有的AI-CBT原型中工作以开发Lyssncbt。核心活动包括 以用户为中心的设计焦点小组和社区心理健康（CMH）治疗师的访谈， 主管和管理人员将为Lyssncbt的设计和开发提供信息。 Lyssncbt会 在第一阶段的最后阶段评估可用性和实施​​准备就绪 基于现场的可用性试验和阶梯式wedge，混合实施效应随机试验（n = 1,850 CMH客户端）评估Lyssncbt的有效性，以提高治疗师CBT技能和客户 结果，并减少客户辍学。分析还将检查假设的作用机理 基础lyssncbt。 这项研究与NIMH的2020年战略计划密切相符，并强调计算 扩展治疗交付和监测的方法。成功执行将提供自动化， 有史以来首次可扩展CBT Fidelity反馈，支持高质量的培训，监督和质量 保证，并提供核心技术基金会，以支持将来一系列EBP。

项目成果

Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol.

• DOI: 10.1186/s12913-022-08519-9
• 发表时间: 2022-09-20
• 期刊: BMC HEALTH SERVICES RESEARCH
• 影响因子: 2.8
• 作者: Creed, Torrey A.;Salama, Leah;Slevin, Roisin;Tanana, Michael;Imel, Zac;Narayanan, Shrikanth;Atkins, David C.
• 通讯作者: Atkins, David C.