Precision Approaches for Scalable Digital and Clinic-Based Interventions in Mental Health

Common mental health conditions such as depression, anxiety, and substance use disorders are important contributors to disability and reduced quality of life. Efforts to address these conditions have been hindered by an inadequate clinician workforce capacity. Furthermore, first-line treatments (medications and clinician-delivered counseling) have modest efficacy, and there is a paucity of data to guide treatment decisions. As a result, it takes years for many patients to find a treatment that works, and the large and growing proportion of patients needing care face long wait times. To overcome these challenges, we need scalable, innovative solutions that both increase access and tailor care to the unique needs of each patient at a specific point in time. Because of their low cost and scalability, digital interventions are a potential tool to increase treatment capacity. However, these interventions, typically delivered by apps, have not achieved robust user engagement and have produced only modest effects across a range of mental health symptoms and conditions, and as a result they have not meaningfully closed the treatment gap. Here, we outline the potential for precision approaches for the delivery of mental health interventions, both digital and conventional, to improve population-level outcomes. Mobile technology, genetics, and electronic health records provide data that capture constructs central to mental health. These data sources provide key inputs for modern data science methods that have the potential to effectively match patients to treatments as well as tailor the timing, dosage, and content within specific digital interventions.