High-performing mental-health services do not happen by accident—they are built through iterative learning, reliable workflows, and data that clinicians trust. Quality Improvement in Psychiatry turns abstract “quality” into concrete practice: safer prescribing, faster access, fewer readmissions, tighter follow-up, and better patient-reported outcomes. We start with what matters to patients—function, sleep, cravings, safety—and then design processes that deliver those results consistently across clinics, wards, and virtual care. You’ll learn how to define a problem with operational precision, create SMART aims, map a care pathway, and select measures that reflect outcomes, processes, and balancing risks (e.g., time burden, equity impact). We compare QI approaches—Model for Improvement, Lean, and human-centered design—and show how to run disciplined PDSA cycles: small tests, learning reviews, and scale-up without burnout. The session covers run charts and SPC to separate signal from noise, along with practical data plumbing—EHR flowsheets, structured fields, registries, and patient-generated data. We’ll practice designing reliable handoffs, no-show recapture loops, and safety-net triggers for suicidality or withdrawal. Equity is integral: stratify results by language, income, age, and geography; co-design with peers and community members; measure what access really feels like for patients. Leadership and culture matter too—psychological safety, transparent goals, and sustained huddles turn QI from a “project” into how the team works every day. By aligning Quality Improvement in Psychiatry, discoverability via psychiatry quality improvement conference, and field standards like quality metrics in mental health, participants can transform evidence into routines that last.