Consumer perspectives on the use of Artificial Intelligence technology and automation in crisis support services: Mixed methods study

Jennifer Ma, Megan O'Riordan, Kelly Mazzer, Philip J. Batterham, Sally Bradford, Kairi Kõlves, Nickolai Titov, Britt Klein, Debra Rickwood

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Emerging technologies, such as Artificial Intelligence (AI), have the potential to enhance service responsiveness and quality, improve reach to underserved groups, and help address lack of workforce capacity in health and mental health care. Yet, there has been little research on the acceptability of AI, particularly in mental health and crisis support, and how this may inform the development of responsible and responsive innovation in the area. Objective: This study aimed to explore: (1) the level of support for the use of technology and automation, such as AI, in Lifeline’s crisis support services in Australia, (2) the likelihood of service use if technology and automation were implemented, (3) the impact of demographic characteristics on the level of support and likelihood of service use, and (4) reasons for not using Lifeline’s crisis support services if technology and automation was implemented in the future. Methods: A mixed methods study involving a computer assisted telephone interview and an online survey was undertaken from 2019 to 2020 to explore expectations and anticipated outcomes of Lifeline’s crisis support services in a nationally representative community sample (n=1,300) and Lifeline help-seeker sample (n=553). Participants were aged from 18 to 93 years. Quantitative descriptive analysis, binary logistic regression models, and qualitative thematic analysis were undertaken to address the research aims. Results: One-third of community and help-seeker participants did not support the collection of information about service users through technology and automation (i.e., via AI), and about half reported that they would be less likely to use the service if automation was introduced. Significant demographic differences were found between the community and help-seeker samples. Of the demographics, only older age predicted being less likely to endorse technology and automation to tailor Lifeline’s crisis support service and use such services (OR=1.48-1.66, P<.002-.005). The most common reason for reluctance, reported by both samples, was that respondents wanted to speak to a real person, with the assumption that human counsellors would be replaced with automated robots or machine services. Conclusions: Even though Lifeline plans to always have a real person providing its crisis support, help-seekers automatically fear this will not be the case if new technology and automation like AI are introduced. Consequently, incorporating innovative use of technology to improve help-seeker outcomes in such services will require careful messaging and assurances that the human connection will continue.
Original languageEnglish
Article numbere34514
Pages (from-to)1-17
Number of pages17
JournalJMIR Human Factors
Volume9
Issue number3
DOIs
Publication statusPublished - 5 Aug 2022

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