Attributes of Provider Referrals for Digital Mental Health Applications in an Integrated Health System, 2019-2021.

ABSTRACT

OBJECTIVE: This article describes trends and attributes associated with digital mental health application (DMHA) referrals from December 2019 through December 2021. METHODS: In total, 43,842 DMHA referrals for 25,213 unique patients were extracted from the electronic health record of a large, diverse, integrated health system. DMHAs were aggregated by type (cognitive-behavioral therapy [CBT] or mindfulness and meditation [MM]). Monthly referral patterns were described and categorized into mutually exclusive clusters (MM, CBT, or MM and CBT). Multinomial logistic regression and post hoc predicted probabilities were used to profile patient, clinical, and encounter attributes among referral clusters. RESULTS: DMHA referrals increased, reached equilibrium, and then began to decline over the 25-month observation period. Compared with the referral cluster average, MM-alone referrals were more likely to occur for patients who were ages ≥65, who were Hispanic or Asian, whose reason for visit concerned mental health, and who had a primary diagnosis of other anxiety disorders. CBT-alone referrals were more likely to occur for patients with a primary diagnosis of depression and less likely to occur for Hispanic patients. Combined MM and CBT referrals were more likely to occur for patients who were ages 18-30, whose reason for visit was "other," and who had a primary diagnosis of depression and were less likely to occur for Hispanic patients and those ages ≥65. CONCLUSIONS: Although this study demonstrates readiness to integrate DMHA referral into clinical workflows, observed variations in attributes of referral clusters support the need to further investigate provider decision making and whether referral patterns are optimal and sustainable.