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INFORMATION FOR

    Walter S. Mathis, MD

    Associate Professor of Psychiatry
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    Titles

    Associate Professor of Psychiatry

    Biography

    Stan Mathis, MD is the Director of the Yale Fellowship in Public Psychiatry, Medical Director of the Assertive Community Treatment (ACT) team at the Connecticut Mental Health Center, and an Associate Professor in Yale’s Department of Psychiatry. Trained originally as an architect and urbanist, he brings a distinctive systems-oriented perspective to psychiatry that integrates clinical care, data science, and the built and social environments in which patients live.

    Clinically, Dr. Mathis provides community-based care to individuals with severe mental illness, working with patients in their homes and neighborhoods. In parallel, he has led substantial redesign of ACT team workflows and developed a suite of internally deployed digital tools - ranging from spatial and EHR-based analytics to telehealth access solutions - that have measurably improved clinical efficiency, outreach, and fidelity to evidence-based care. His clinical leadership has been repeatedly recognized with top evaluations from the Connecticut Department of Mental Health and Addiction Services.

    As an educator, Dr. Mathis directs Yale’s Fellowship in Public Psychiatry and has played a central role in the development of Yale Psychiatry’s structural competency curriculum within the Social Justice and Health Equity program. He has contributed to a multi-year didactic series that integrates experiential learning, geospatial analysis, and historical context, and has mentored fellows, residents, medical students, and interdisciplinary trainees across a range of scholarly and clinical projects.

    Dr. Mathis’s research focuses on applying advanced analytics and artificial intelligence to improve healthcare delivery, understand the factors that shape health outcomes, and support learning health systems. His work spans health services research, serious mental illness, first-episode psychosis, healthcare accessibility, geospatial analysis, machine learning, large language models, and clinical informatics. He has developed novel approaches for analyzing pathways to care, healthcare access, qualitative interview data, and population-level clinical outcomes, and his work has been published in leading peer-reviewed journals and presented nationally. He serves as Director of Informatics for a statewide first-episode psychosis learning health system, where he leads the development of data infrastructure, analytics, and visualization platforms that support continuous quality improvement across clinical programs.

    More recently, Dr. Mathis’s research has expanded to examine how people experience and respond to the built environment. In collaboration with colleagues in the Yale School of Architecture and the Department of Psychology, he is leading a newly funded interdisciplinary project that combines wearable physiological sensing, subjective experience measures, and artificial intelligence to model how architectural spaces influence human experience. This work seeks to develop novel predictive and generative AI tools that can help designers better understand and anticipate the human impact of the environments they create.

    Last Updated on May 29, 2026.

    Appointments

    • Psychiatry

      Associate Professor on Term
      Primary

    Other Departments & Organizations

    Education & Training

    Public Psychiatry Fellow
    Yale School of Medicine (2017)
    Resident
    University of Arkansas for Medical Sciences (2016)
    MD
    University of Arkansas for Medical Sciences (2012)
    BA
    Yale College, Architecture

    Research

    Research at a Glance

    Yale Co-Authors

    Frequent collaborators of Walter S. Mathis's published research.

    Publications

    2025

    Academic Achievements & Community Involvement

    Patents

    • Machine learning system and method for attendance risk mitigation

      Application#
      18/360,231
      Country
      United States
      Date Issued
      06/04/2024
    • MACHINE LEARNING SYSTEM AND METHODS FOR INCREASING APPOINTMENT COMPLIANCE

      Application#
      18/660,594
      Country
      United States
      Date Issued
      01/30/2025

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