Integrated AI-Driven Triage and Referral for Newcomer Mental Health in Ontario: Addressing Anxiety and Depression

Abstract

Systemic barriers, including language, navigation complexity, and long specialist wait-times, result in the under-utilization of mental health services by newcomers and significant gaps in timely care for anxiety and depression. This paper proposes an integrated, hybrid-federated AI triage and referral architecture that bridges system silos, connecting primary care, settlement and community resources, while maintaining privacy and regulatory compliance. Newcomers navigate a multilingual digital intake where an AI captures cultural context and psychometric test scores, enabling automated triage and directing them to self-help, community resources, or urgency-based specialist referrals, supported by continuous feedback processes. The system is co-designed with newcomer communities to ensure patient-centered, culturally relevant, and equity-driven care.

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