ReviewFrontiers in health services2025
Leveraging generative AI to simulate mental healthcare access and utilization.
Review in Frontiers in health services, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Exploring Nurses' Perspectives on the Use of Artificial Intelligence Chatbots for Mental Health Support: A Cross-Sectional Study in Greece.Nursing reports (Pavia, Italy) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.