Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol.
Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol.
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DOI：
10.1186/s12913-022-08519-9
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发表时间：
2022-09-20
影响因子：
2.8
通讯作者：
Atkins, David C.
中科院分区：
文献类型：
--
作者：
Creed, Torrey A.;Salama, Leah;Slevin, Roisin;Tanana, Michael;Imel, Zac;Narayanan, Shrikanth;Atkins, David C.
关键词：
每年，数百万美国人接受循证心理疗法（EBP），如认知行为疗法（CBT），用于治疗心理和行为健康问题。然而，目前还没有可扩展的方法来评估心理治疗服务的质量，使EBP的质量和有效性在很大程度上无法测量和未知。项目AFFECT将开发和评估一个基于AI的软件系统，以自动从CBT会话的记录中估计CBT保真度。项目AFFECT是NIMH资助的研究伙伴关系之间的宾夕法尼亚协作CBT和实施科学和Lyssn.io，公司。（“Lyssn”）是一家开发基于人工智能的技术的初创公司，这些技术具有客观性、可扩展性和成本效益，以支持EBP的培训、监督和质量保证。Lyssn提供符合HIPAA的基于云的软件，用于安全记录、共享和审查治疗会话，其中包括AI生成的CBT指标。拟议的工具将建立在这一核心平台之上，并纳入其中。第一阶段将从现有的软件原型开发一个LyssnCBT用户界面，适合社区精神卫生（CMH）机构的需求。核心活动包括一个以用户为中心的设计焦点小组，并与社区心理健康治疗师，主管和管理人员进行访谈，以告知LyssnCBT的设计和开发。将在第一阶段的最后阶段评估LyssnCBT的可用性和实施准备情况。第二阶段将进行一项阶梯楔形，混合实施有效性随机试验（N = 1，875名客户），以评估LyssnCBT的有效性，以提高治疗师CBT技能和客户结果，并减少客户脱落。分析还将检查LyssnCBT的假设作用机制。成功的执行将首次提供自动化、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，支持未来一系列EBP的高质量交付。ClinicalTrials.gov; NCT 05340738; 2022年4月21日批准。
Each year, millions of Americans receive evidence-based psychotherapies (EBPs) like 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, leaving EBP quality and effectiveness largely unmeasured and unknown. Project AFFECT will develop and evaluate an AI-based software system to automatically estimate CBT fidelity from a recording of a CBT session. Project AFFECT is an NIMH-funded research partnership between the Penn Collaborative for CBT and Implementation Science and Lyssn.io, Inc. (“Lyssn”) a start-up developing AI-based technologies that are objective, scalable, and cost efficient, to support training, supervision, and quality assurance of EBPs. Lyssn provides HIPAA-compliant, cloud-based software for secure recording, sharing, and reviewing of therapy sessions, which includes AI-generated metrics for CBT. The proposed tool will build from and be integrated into this core platform. Phase I will work from an existing software prototype to develop a LyssnCBT user interface geared to the needs of community mental health (CMH) agencies. Core activities include a user-centered design focus group and interviews with community mental health therapists, supervisors, and administrators to 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 stepped-wedge, hybrid implementation-effectiveness randomized trial (N = 1,875 clients) to evaluate the effectiveness of LyssnCBT to improve therapist CBT skills and client outcomes and reduce client drop-out. Analyses will also examine the hypothesized mechanism of action underlying LyssnCBT. 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 the quality delivery of a range of EBPs in the future. ClinicalTrials.gov; NCT05340738; approved 4/21/2022.
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影响因子：
2.7
作者：
Atkins, DC
通讯作者：
Atkins, DC
影响因子：
3.4
作者：
Atkins, David C.;Baldwin, Scott A.;Neighbors, Clayton
通讯作者：
Neighbors, Clayton
DOI：
10.1007/s10488-014-0618-5
发表时间：
2016-01-01
影响因子：
2.6
作者：
Creed, Torrey A.;Wolk, Courtney Benjamin;Beck, Aaron T.
通讯作者：
Beck, Aaron T.
影响因子：
5.9
作者：
Baldwin, Scott A.;Imel, Zac E.;Atkins, David C.
通讯作者：
Atkins, David C.
影响因子：
7.2
作者：
Atkins, David C.;Steyvers, Mark;Smyth, Padhraic
通讯作者：
Smyth, Padhraic