When under strain, healthcare workers (HCWs) may develop a state of sustained or high work-related stress. High exposure to potentially traumatic events coupled with work-related stress may trigger episodes of psychological distress (e.g., anxiety, depression, PTSD). As a result, HCWs may develop mental health disorders that increase sick leaves and turnover rates. Studies conducted in healthcare settings have often underlined the resource scarcity (e.g., human, material) prevalent in these environments before COVID-19, which has only been worsened by its impact. This resource scarcity may contribute to psychological distress among HCWs. Hence, the healthcare community needs interventions that work efficiently in targeting anxiety, depression, and PTSD in work settings. Identifying HCWs most likely to need assistance is therefore critical when implementing effective preventive interventions to avoid staff shortages.

One potential solution to this issue involves the use of machine learning algorithms to train novel models to screen for distress. Machine learning may reduce the burden of active monitoring by decreasing the number of questions asked to HCWs.The purpose of this study was then to use machine learning to develop and test preliminary models with fewer questions to screen for the risk of developing anxiety, depression, and PTSD in HCWs

The study included data from a prospective cohort study of 816 healthcare workers collected using a mobile application during the first two waves of COVID-19. Each week, the participants responded to 11 questions and completed three screening questionnaires (one for anxiety, one for depression, and one for post-traumatic stress disorder). Then, the research team selected two questions (out of the 11), which were used with biological sex to identify whether scores on each screening questionnaire would be positive or negative.

The findings indicated that the models derived from the two questions and biological sex accurately identified screening scores for anxiety, depression, and post-traumatic stress disorders in 70% to 80% of cases.  Our proof of concept demonstrates the feasibility of using machine learning to develop novel models to screen for psychological distress in at-risk healthcare workers. Developing models with fewer questions may reduce burdens of active monitoring in practical settings by decreasing the weekly assessment duration.

Authors : Steve Geoffrion, Catherine Morse, Marie‑Michèle Dufour, Nicolas Bergeron, Stéphane Guay, Marc J. Lanovaz. (2023)

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