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疗法（EBP），如认知疗法， 行为疗法（CBT）用于治疗心理和行为健康问题。然而，目前， 没有可衡量的方法来评估心理治疗服务的质量。在研究环境中， 使用行为编码方法，但是这些方法耗时、昂贵，并且很少在现实世界中使用 临床环境。因此，EBP的质量和有效性是不可测量和未知的。目前，快速通道 STTR提案将开发和评估一个基于AI的软件系统（LyssnCBT），该系统将自动 根据CBT会话的音频记录来估计CBT保真度。重要的是，目前的工作建立在 Lyssn以前成功开发了一个自动化系统，用于评估动机面试， (MI)之前的研究表明，AI算法可以准确地估计CBT保真度。 Lyssn.io，Inc.，美国专利申请公开号：（“Lyssn”）是一家开发基于人工智能的技术的初创公司，以支持培训，监督， 和循证咨询的质量保证。我们的目标是开发创新的健康技术 客观、可扩展且经济高效的解决方案。Lyssn提供符合HIPAA的云平台 用于安全记录、共享和审查治疗会话，其中包括AI生成的MI指标。 拟议的LyssnCBT工具将建立在这一核心平台之上，并将融入其中。Lyssn正在与 博士Torrey Creed和Penn Collaborative，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个供应商。专业知识， 关系，并积累了数据-超过8,000个记录的会话和超过3,000个CBT评级 忠诚度--构成了当前研究的临床基础。 第一阶段将从现有的AI-CBT原型开发LyssnCBT。核心活动包括 以用户为中心的设计焦点小组和与社区心理健康（CMH）治疗师的访谈， 监督员和管理员，这将通知设计和开发的LyssnCBT。LyssnCBT将是 在第一阶段的最后阶段评估可用性和实施准备情况。第二阶段将进行一次 基于现场的可用性试验和一项阶梯楔形混合实施有效性随机试验（N = 1，850名CMH客户），以评估LyssnCBT对改善治疗师CBT技能和客户的有效性 结果，并减少客户辍学。分析还将检查假设的作用机制 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强调非常一致。 扩大治疗提供和监测的方法。成功的执行将提供自动化， 有史以来第一次提供可扩展的CBT保真度反馈，支持高质量的培训，监督和质量 保证，并提供一个核心技术基础，可以支持一系列的电子商务在未来。

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.