治疗师的反馈及其对在线癌症支持小组采用基于人工智能的协同主持人的影响:混合方法单臂可用性研究。

IF 3.3 Q2 ONCOLOGY JMIR Cancer Pub Date : 2023-06-09 DOI:10.2196/40113
Yvonne W Leung, Steve Ng, Lauren Duan, Claire Lam, Kenith Chan, Mathew Gancarz, Heather Rennie, Lianne Trachtenberg, Kai P Chan, Achini Adikari, Lin Fang, David Gratzer, Graeme Hirst, Jiahui Wong, Mary Jane Esplen
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引用次数: 0

摘要

背景:最近出现的 COVID-19 大流行病和社会疏远的要求增加了对虚拟支持计划的需求。人工智能(AI)的进步可能会为虚拟团体干预中缺乏情感联系等管理难题提供新的解决方案。利用在线支持小组的输入文本,人工智能可以帮助识别心理健康问题的潜在风险,提醒小组主持人,并自动推荐量身定制的资源,同时监测患者的治疗效果:这项混合方法、单臂研究旨在评估基于人工智能的共同主持人(AICF)在加拿大癌症聊天室治疗师和参与者中的可行性、可接受性、有效性和可靠性,以便通过实时分析支持小组会议期间发布的文本来监控在线支持小组参与者的困扰。具体来说,AICF(1)生成与会者档案,其中包括每次会议的讨论主题摘要和情绪轨迹;(2)识别有可能增加情绪困扰的与会者,并提醒治疗师进行跟进;(3)根据与会者的需求自动提出有针对性的建议。在线支持小组的参与者包括各种类型的癌症患者,治疗师是经过临床培训的社会工作者:我们的研究报告采用混合方法对 AICF 进行了评估,包括治疗师的意见和定量测量。通过患者的实时表情签到、语言探究和字数统计软件以及事件影响量表(修订版)对 AICF 检测痛苦的能力进行了评估:虽然定量结果表明 AICF 检测困扰的能力仅具有一定的有效性,但定性结果表明 AICF 能够实时检测出适合治疗的问题,从而使治疗师能够更加积极主动地为每个小组成员提供个性化支持。不过,治疗师对 AICF 的困扰检测功能的道德责任表示担忧:未来的工作将通过视频会议研究可穿戴传感器和面部提示,以克服与基于文本的在线支持小组相关的障碍:RR2-10.2196/21453。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Therapist Feedback and Implications on Adoption of an Artificial Intelligence-Based Co-Facilitator for Online Cancer Support Groups: Mixed Methods Single-Arm Usability Study.

Background: The recent onset of the COVID-19 pandemic and the social distancing requirement have created an increased demand for virtual support programs. Advances in artificial intelligence (AI) may offer novel solutions to management challenges such as the lack of emotional connections within virtual group interventions. Using typed text from online support groups, AI can help identify the potential risk of mental health concerns, alert group facilitator(s), and automatically recommend tailored resources while monitoring patient outcomes.

Objective: The aim of this mixed methods, single-arm study was to evaluate the feasibility, acceptability, validity, and reliability of an AI-based co-facilitator (AICF) among CancerChatCanada therapists and participants to monitor online support group participants' distress through a real-time analysis of texts posted during the support group sessions. Specifically, AICF (1) generated participant profiles with discussion topic summaries and emotion trajectories for each session, (2) identified participant(s) at risk for increased emotional distress and alerted the therapist for follow-up, and (3) automatically suggested tailored recommendations based on participant needs. Online support group participants consisted of patients with various types of cancer, and the therapists were clinically trained social workers.

Methods: Our study reports on the mixed methods evaluation of AICF, including therapists' opinions as well as quantitative measures. AICF's ability to detect distress was evaluated by the patient's real-time emoji check-in, the Linguistic Inquiry and Word Count software, and the Impact of Event Scale-Revised.

Results: Although quantitative results showed only some validity of AICF's ability in detecting distress, the qualitative results showed that AICF was able to detect real-time issues that are amenable to treatment, thus allowing therapists to be more proactive in supporting every group member on an individual basis. However, therapists are concerned about the ethical liability of AICF's distress detection function.

Conclusions: Future works will look into wearable sensors and facial cues by using videoconferencing to overcome the barriers associated with text-based online support groups.

International registered report identifier (irrid): RR2-10.2196/21453.

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来源期刊
JMIR Cancer
JMIR Cancer ONCOLOGY-
CiteScore
4.10
自引率
0.00%
发文量
64
审稿时长
12 weeks
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