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

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

doi:10.2196/40113

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

JMIR Cancer. 2023 Jun 9:9:e40113. doi: 10.2196/40113.

Authors

Yvonne W Leung^{1

2

3}, Steve Ng¹, Lauren Duan¹, Claire Lam¹, Kenith Chan⁴, Mathew Gancarz¹, Heather Rennie^{1

5}, Lianne Trachtenberg^{1

6}, Kai P Chan¹, Achini Adikari⁷, Lin Fang⁸, David Gratzer^{2

9}, Graeme Hirst¹⁰, Jiahui Wong^{1

2}, Mary Jane Esplen²

Affiliations

¹ de Souza Institute, University Health Network, Toronto, ON, Canada.
² Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
³ College of Professional Studies, Northeastern University, Toronto, ON, Canada.
⁴ Department of Psychology, University of Toronto, Toronto, ON, Canada.
⁵ BC Cancer Agency, Vancouver, BC, Canada.
⁶ Centre for Psychology and Emotional Health, Toronto, ON, Canada.
⁷ Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.
⁸ Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada.
⁹ Centre for Addiction and Mental Health, Toronto, ON, Canada.
¹⁰ Department of Computer Science, University of Toronto, Toronto, ON, Canada.

PMID: 37294610
PMCID: PMC10334721
DOI: 10.2196/40113

Abstract

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.

Keywords: LIWC; cancer; emotion analysis; legal implications of AI; natural language processing; recommender system; therapist adoption; therapist attitudes; therapist liability.

©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. Originally published in JMIR Cancer (https://cancer.jmir.org), 09.06.2023.