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，数以百万计的美国人接受循证心理疗法(EBP)，如认知疗法 行为疗法(CBT)用于治疗精神和行为健康问题。然而，目前，有 没有可扩展的方法来评估心理治疗服务的质量。在研究环境中，以人为本 虽然使用了行为编码方法，但这些方法既耗时又昂贵，而且很少在现实世界中使用 临床环境。因此，EBP的质量和有效性是无法测量和未知的。当前的快车道 STTR提案将开发和评估一个基于人工智能的软件系统(LyssnCBT)，该系统将自动 从CBT会话的录音中估计CBT保真度。重要的是，当前的工作建立在 莱森之前成功地开发了一个用于评估激励性面试的自动化系统 (MI)，以及之前的研究表明，人工智能算法可以准确地估计CBT保真度。 Lyssn.io，Inc.是一家初创公司，开发基于人工智能的技术来支持培训、监督、 循证咨询的质量保证。我们的目标是发展创新的健康技术 客观、可扩展且经济高效的解决方案。Lyssn提供符合HIPAA标准的基于云的平台 用于安全地记录、共享和审查治疗过程，其中包括人工智能生成的MI指标。 拟议的LyssnCBT工具将在该核心平台上构建并集成到该平台中。莱森正在与 Torrey Creed博士和宾夕法尼亚大学合作项目，该项目拥有14年以上的黄金标准CBT培训记录 和监督，包括100多个社区机构和近900个提供者。专业知识， 关系和积累的数据--超过8,000次录音会话和超过3,000次CBT评级 保真度--构成了当前研究的临床基础。 第一阶段将在现有AI-CBT原型的基础上开发LyssnCBT。核心活动包括 以用户为中心的设计焦点小组和对社区心理健康(CMH)治疗师的采访， 主管和管理员，他们将为LyssnCBT的设计和开发提供信息。LyssnCBT将成为 在第一阶段的最后阶段评估可用性和实施就绪性第二阶段将进行 基于现场的可用性试验和阶梯式、混合实施-有效性随机试验(N= 1,850名CMH客户)评估LyssnCBT对提高治疗师CBT技能和客户的有效性 结果，并减少客户流失。分析还将检验假设的作用机制。 潜在的LyssnCBT。 这项研究与NIMH的2020年战略计划及其对计算能力的重视密切相关 扩大治疗提供和监测的方法。成功的执行将提供自动化、 首次提供可扩展的CBT保真度反馈，支持高质量的培训、监督和质量 保证，并提供核心技术基础，可在未来支持一系列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.