Lyssn.io, Seattle, Washington.
Talkspace, New York, New York.
JAMA Netw Open. 2024 Jan 2;7(1):e2352590. doi: 10.1001/jamanetworkopen.2023.52590.
Use of asynchronous text-based counseling is rapidly growing as an easy-to-access approach to behavioral health care. Similar to in-person treatment, it is challenging to reliably assess as measures of process and content do not scale.
To use machine learning to evaluate clinical content and client-reported outcomes in a large sample of text-based counseling episodes of care.
DESIGN, SETTING, AND PARTICIPANTS: In this quality improvement study, participants received text-based counseling between 2014 and 2019; data analysis was conducted from September 22, 2022, to November 28, 2023. The deidentified content of messages was retained as a part of ongoing quality assurance. Treatment was asynchronous text-based counseling via an online and mobile therapy app (Talkspace). Therapists were licensed to provide mental health treatment and were either independent contractors or employees of the product company. Participants were self-referred via online sign-up and received services via their insurance or self-pay and were assigned a diagnosis from their health care professional.
All clients received counseling services from a licensed mental health clinician.
The primary outcomes were client engagement in counseling (number of weeks), treatment satisfaction, and changes in client symptoms, measured via the 8-item version of Patient Health Questionnaire (PHQ-8). A previously trained, transformer-based, deep learning model automatically categorized messages into types of therapist interventions and summaries of clinical content.
The total sample included 166 644 clients treated by 4973 therapists (20 600 274 messages). Participating clients were predominantly female (75.23%), aged 26 to 35 years (55.4%), single (37.88%), earned a bachelor's degree (59.13%), and were White (61.8%). There was substantial variability in intervention use and treatment content across therapists. A series of mixed-effects regressions indicated that collectively, interventions and clinical content were associated with key outcomes: engagement (multiple R = 0.43), satisfaction (multiple R = 0.46), and change in PHQ-8 score (multiple R = 0.13).
This quality improvement study found associations between therapist interventions, clinical content, and client-reported outcomes. Consistent with traditional forms of counseling, higher amounts of supportive counseling were associated with improved outcomes. These findings suggest that machine learning-based evaluations of content may increase the scale and specificity of psychotherapy research.
异步文本咨询作为一种易于获取的行为健康护理方式，其使用正在迅速增长。与面对面治疗类似，由于缺乏可靠的评估方法，其过程和内容的衡量标准难以确定。
使用机器学习评估大量基于文本的咨询护理案例中的临床内容和客户报告的结果。
设计、设置和参与者：在这项质量改进研究中，参与者在 2014 年至 2019 年间接受了基于文本的咨询；数据分析于 2022 年 9 月 22 日至 2023 年 11 月 28 日进行。消息的已识别内容作为持续质量保证的一部分被保留。治疗是通过在线和移动治疗应用程序（Talkspace）进行的异步文本咨询。治疗师获得提供心理健康治疗的许可，他们是独立承包商或产品公司的员工。参与者通过在线注册自行报名，并通过他们的保险或自付费获得服务，并由他们的医疗保健专业人员分配诊断。
所有客户都接受了有执照的心理健康临床医生的咨询服务。
主要结果是客户参与咨询的周数（客户治疗的周数）、治疗满意度以及客户症状的变化，通过 8 项患者健康问卷（PHQ-8）来衡量。一个经过预先训练的、基于转换器的深度学习模型自动将消息分类为治疗师干预类型和临床内容摘要。
总样本包括 166444 名客户，由 4973 名治疗师治疗（20600274 条消息）。参与的客户主要是女性（75.23%），年龄在 26 至 35 岁之间（55.4%），单身（37.88%），拥有学士学位（59.13%），并且是白人（61.8%）。治疗师的干预措施和治疗内容存在很大差异。一系列混合效应回归表明，干预措施和临床内容共同与关键结果相关：参与度（多元 R = 0.43）、满意度（多元 R = 0.46）和 PHQ-8 评分的变化（多元 R = 0.13）。
这项质量改进研究发现治疗师干预、临床内容和客户报告的结果之间存在关联。与传统形式的咨询一致，更多的支持性咨询与改善的结果相关。这些发现表明，基于机器学习的内容评估可能会提高心理治疗研究的规模和特异性。
