Mental health professionals’ perspectives on dynamic learning of individual-level trajectories in youth mental health care: A qualitative study

Background: Assessment of suicidal ideation in youth mental health remains largely static, subjective, and inaccurate. Digital tools present a possible solution. This research examines a novel digital mental health tool, the Dynamic Learning Tool, which applies a machine learning model for individual-level continuous-time predictive trajectories of clients’ suicidal ideation to inform clinical care and was developed for an existing platform used in Australian and Canadian clinical practice. This work aims to explore professionals’ attitudes towards machine learning of predictive trajectories and artificial intelligence and thereafter determine the Dynamic Learning Tool’s design and content requirements. Methods: Following a co-design methodology, semi-structured interviews and usability testing were conducted with 21 mental health professionals in Australia, Canada, and the United States. Data analysis employed inductive reflexive thematic analysis. Results: Findings indicate that professionals are open to using predictive trajectories strictly to enhance their decision-making but not replace clinical judgement. Furthermore, they emphasised the need for greater machine learning model transparency, incorporation of real-world contextual data, clear guidelines for use, and overt adherence to privacy and accountability standards. Conclusions: The findings presented here have broader implications for the ethics, design, development, implementation, governance, and evaluation of machine learning of predictive trajectories and artificial intelligence in youth mental health.