Healthcare Utilization, Chronic Condition Management, and Workplace Functioning Among Users of a Purpose-Built Mental Health AI (Ash): Cross-Sectional Study
TL;DR
Preliminary evidence is provided that use of purpose-built AI may be associated with positive symptom-based and utilization outcomes for those managing co-occurring mental and physical concerns.
Abstract
Mental health challenges can exacerbate physical symptoms and complicate management of chronic conditions. Purpose-built artificial intelligence (AI) tools may offer scalable support for co-occurring mental and physical health concerns. This cross-sectional study compared past-6-month healthcare utilization, chronic condition management, physical health behaviors, mental health change, and workplace functioning between active (n = 169) and non-users (n = 73) of a mental health AI (Ash). Participants had at least one chronic condition (e.g. hypertension, chronic pain). Binary outcomes were modeled as adjusted risk differences (RDs) using linear probability models and continuous outcomes were modeled with linear regression; all models were adjusted for hypertension. Relative to non-users, active users were more likely to report improved mental health (61.4% vs. 34.3%; RD = 0.27), higher medication adherence (91.7% vs. 76.4%, RD = 0.15), fewer skipped or delayed chronic-condition care activities (b = -0.44), and were less likely to report repeat urgent care visits (9.5% vs. 23.3%; RD = -0.15) and monthly-or-more absenteeism (24.2% vs. 45.2%; RD = -0.20, all ps<.05). Findings provide preliminary evidence that use of purpose-built AI may be associated with positive symptom-based and utilization outcomes for those managing co-occurring mental and physical concerns.
