Leveraging generative AI to simulate mental healthcare access and utilization.

Abstract

Purpose: This article examines how generative artificial intelligence (AI) can simulate, analyze, and enhance mental health care journeys for individuals from diverse backgrounds, supporting improved access, personalization, and outcomes.

Design/methodology/approach: An AI-generated case study of Marcus Johnson, a 24-year-old Black software developer in Atlanta, models the interplay of personal, cultural, and systemic factors affecting mental health care access. The analysis integrates Andersen's Behavioral Model, Penchansky and Thomas's Dimensions of Access, and Measurement Based Care (MBC) to systematically identify barriers, facilitators, and opportunities for data-driven intervention and tailored care.

Findings: The case study demonstrates that generative AI simulations, especially when combined with MBC, can replicate real-world complexities, inform clinical decision-making, and personalize interventions through ongoing assessment, symptom monitoring, and collaborative planning. Telehealth, flexible scheduling, and cultural competence are highlighted as critical for bridging access gaps and improving outcomes.

Originality/value: This work is among the first to synthesize leading access-to-care models, MBC, and generative AI to simulate and improve mental health care pathways. The approach offers a novel framework for educators, clinicians, and system designers to address the full spectrum of access challenges and clinical needs in contemporary populations.

Practical implications: Generative AI, anchored in evidence-based frameworks, enables mental health professionals and trainees to test and refine care strategies in a risk-free environment, promoting more equitable, responsive, and effective mental health systems for all.