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)，例如认知疗法 然而，目前存在用于治疗精神和行为健康问题的行为疗法（CBT）。 在研究环境中，没有可扩展的方法来评估心理治疗服务的质量。 使用行为编码方法，但这些方法耗时、成本高，并且在现实世界中很少使用 因此，EBP 的质量和有效性目前是无法衡量和未知的。 STTR 提案将开发和评估基于人工智能的软件系统（LyssnCBT），该系统将自动 重要的是，当前的工作是基于 CBT 会话的录音来估计 CBT 保真度的。 Lyssn 之前在开发评估动机访谈的自动化系统方面取得了成功 (MI)，之前的研究表明人工智能算法可以准确地估计 CBT 保真度。 Lyssn.io, Inc.（“Lyssn”）是一家初创公司，开发基于人工智能的技术，以支持培训、监督、 我们的目标是开发创新的健康技术。 Lyssn 提供客观、可扩展且经济高效的解决方案，提供符合 HIPAA 的基于云的平台。 用于安全记录、共享和审查治疗疗程，其中包括人工智能生成的 MI 指标。 拟议的 LyssnCBT 工具将从 Lyssn 合作的这个核心平台构建并集成到其中。 Torrey Creed 博士和 Penn Collaborative 拥有 14 年以上黄金标准 CBT 培训记录 和监督，包括 100 多个社区机构和近 900 个提供者。 关系和积累的数据——超过 8,000 个记录的会话和超过 3,000 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 原型开始开发 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， 监督者和管理员，这将为 LyssnCBT 的设计和开发提供信息。 在第一阶段的最后阶段评估可用性和准备实施情况。第二阶段将进行 基于现场的可用性试验和阶梯楔形、混合实施效果随机试验（N = 1,850 名 CMH 客户）评估 LyssnCBT 在提高治疗师 CBT 技能和客户方面的有效性 分析还将检查所探索的行动机制。 底层 LyssnCBT。 该研究与 NIMH 的 2020 年战略计划及其对计算的强调高度一致 扩大治疗提供和监测的方法的成功执行将提供自动化， 有史以来第一次可扩展的 CBT 保真度反馈，支持高质量的培训、监督和质量 保证，并提供可以支持未来一系列EBP的核心技术基础。

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.