Leveraging generative AI to simulate mental healthcare access and utilization

Area of Science:

• Health Services Research
• Artificial Intelligence in Healthcare
• Mental Health Informatics

Background:

• Access to mental health care is complex, influenced by personal, cultural, and systemic factors.
• Existing models for assessing access and care delivery have limitations in capturing real-world nuances.
• Measurement Based Care (MBC) offers a data-driven approach to personalize mental health treatment.

Purpose of the Study:

• To examine how generative artificial intelligence (AI) can simulate and enhance mental health care journeys.
• To support improved access, personalization, and outcomes in mental health services for diverse individuals.
• To integrate AI with established access models and MBC for a novel care pathway framework.

Main Methods:

• An AI-generated case study of a diverse individual (Marcus Johnson) was developed.
• Integrated Andersen's Behavioral Model, Penchansky and Thomas's Dimensions of Access, and MBC.
• Simulated the interplay of factors affecting mental health care access and identified intervention opportunities.

Main Results:

• Generative AI simulations, combined with MBC, can replicate complex real-world scenarios in mental health care.
• AI simulations inform clinical decision-making, enabling personalized interventions through continuous assessment and planning.
• Telehealth, flexible scheduling, and cultural competence are crucial for overcoming access barriers and improving outcomes.

Conclusions:

• Generative AI offers a novel framework for understanding and improving mental health care access and delivery.
• This approach enables educators, clinicians, and designers to address multifaceted access challenges.
• AI facilitates risk-free testing of care strategies, promoting more equitable and effective mental health systems.