AI Chatbots for Mental Health Medication Management in Pharmacy Care

Abstract

Medication non-adherence remains a stubborn driver of relapse, hospitalizations, and preventable costs in mental health care. Pharmacies—often the most frequent touchpoint in a patient’s care journey—are uniquely positioned to coordinate medication management, but capacity constraints limit the reach of human-delivered counseling. Advances in conversational artificial intelligence (AI) now enable pharmacy-embedded chatbots that can deliver psychoeducation, side-effect triage, refill support, and adherence nudges at scale, while keeping pharmacists “in the loop.” This manuscript synthesizes current evidence on mental-health chatbots, clarifies how pharmacy chatbots can be designed to complement clinical workflows, and proposes a pragmatic, pharmacy-centered trial protocol to evaluate impact on adherence and symptoms. The introduction outlines the global mental-health burden and the adherence gap; the literature review summarizes efficacy signals from randomized and observational chatbot studies and the expanding role of psychiatric pharmacists.

The methodology details a mixed-methods randomized controlled trial (RCT) comparing usual pharmacist care versus pharmacist-supervised AI chatbot augmentation across community and hospital pharmacies. The study protocol specifies eligibility, intervention content (adherence coaching, side-effect triage with safety escalation, and refill automation), data governance (HIPAA/GDPR-aligned), primary outcomes (Proportion of Days Covered, PHQ-9/GAD-7 change), and analysis plans. We include a feasibility “pilot illustration” using synthetic data to demonstrate the kinds of process and clinical improvements that are plausible and how to interpret them safely. We conclude that pharmacy-embedded chatbots, if designed with rigorous human oversight, privacy by design, and regulatory awareness (e.g., FDA CDS guidance; EU AI Act), could meaningfully extend the reach of mental-health medication management and free pharmacist time for high-complexity tasks. Future research should test long-term durability, equity impacts, and generalizability across diagnoses, languages, and health systems. (World Health Organization, PMC, U.S. Food and Drug Administration, Public Health)