TY  - JOUR
AU  - Leung, Yvonne W
AU  - Ng, Steve
AU  - Duan, Lauren
AU  - Lam, Claire
AU  - Chan, Kenith
AU  - Gancarz, Mathew
AU  - Rennie, Heather
AU  - Trachtenberg, Lianne
AU  - Chan, Kai P
AU  - Adikari, Achini
AU  - Fang, Lin
AU  - Gratzer, David
AU  - Hirst, Graeme
AU  - Wong, Jiahui
AU  - Esplen, Mary Jane
PY  - 2023
DA  - 2023/6/9
TI  - Therapist Feedback and Implications on Adoption of an Artificial Intelligence–Based Co-Facilitator for Online Cancer Support Groups: Mixed Methods Single-Arm Usability Study
JO  - JMIR Cancer
SP  - e40113
VL  - 9
KW  - cancer
KW  - recommender system
KW  - natural language processing
KW  - LIWC
KW  - emotion analysis
KW  - therapist adoption
KW  - therapist attitudes
KW  - legal implications of AI
KW  - therapist liability
AB  - 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 
SN  - 2369-1999
UR  - https://cancer.jmir.org/2023/1/e40113
UR  - https://doi.org/10.2196/40113
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37294610
DO  - 10.2196/40113
ID  - info:doi/10.2196/40113
ER  -