Abstract
Introduction: This study investigated self-reported health status, health screenings, vision problems, and vaccination rates among veteran and non-veteran groups to uncover health disparities that are critical for informed health system planning for veteran populations. Methods: Using public-use data from the National Health Interview Survey (2015-2018), this study adopts an ecologic cross-sectional approach to conduct an in-depth analysis and visualization of the data assisted by Generative AI, specifically ChatGPT-4. This integration of advanced AI tools with traditional epidemiological principles enables systematic data management, analysis, and visualization, offering a nuanced understanding of health dynamics across demographic segments and highlighting disparities essential for veteran health system planning. Findings: Disparities in self-reports of health outcomes, health screenings, vision problems, and vaccination rates were identified, emphasizing the need for targeted interventions and policy adjustments. Conclusion: Insights from this study could inform health system planning, using epidemiological data assessment to suggest enhancements for veteran healthcare delivery. These findings highlight the value of integrating Generative AI with epidemiological analysis in shaping public health policy and health planning.
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