Randomized Controlled Trial of an Online Machine Learning-Driven Risk Assessment and Intervention Platform for Increasing the Use of Crisis Services

Objective: Mental illness is a leading cause of disease burden; however, many barriers prevent people from seeking mental health services. Technological innovations may improve the ability to reach under-served populations by overcoming many existing barriers. The investigators evaluated a brief, automated risk assessment and intervention platform designed to increase the use of crisis resources provided to individuals who were online and in crisis. Hypothesis: The investigators hypothesized that individuals assigned to the intervention condition would report using crisis resources at higher rates than individuals in the control condition. Method: Participants, users of the digital mental health app Koko, were randomly assigned to treatment or control conditions upon accessing the app and were included in the study after their posts were identified by machine learning classifiers as signaling a current mental health crisis. Participants in the treatment condition received a brief Barrier Reduction Intervention (BRI) designed to increase the use of crisis service referrals provided on the app. Participants were followed-up several hours later to assess the use of crisis services.