Access to care affects electronic health record reliability and AI-driven disease prediction.

Despite well-documented healthcare access disparities, their impact on electronic health record (EHR) reliability and resulting clinical prediction models remains poorly understood. Analyzing 205,186 participants from the All of Us Research Program, we found that participants with cost-constrained or delayed care had worse EHR reliability for 73% of examined medical conditions as measured by participant self-reported conditions, driven in part by lower visit rates. In a type 2 diabetes prediction task, including participant self-reported conditions significantly improved the predictive performance for participants with lower access to care and improved targeting of low access patients who would go on to develop type 2 diabetes. This study demonstrates that healthcare access systematically affects both data quality and clinical prediction performance, suggesting that improving health equity requires addressing both data collection biases and algorithmic limitations. Our findings provide an empirical foundation for developing clinical prediction systems that work effectively for all patients regardless of barriers to access.