Quantifying Structural Healthcare Disparities through Fairness-Aware Large Language Models Integrating Multi-Modal Electronic Health Records and Socioeconomic Determinants
DOI:
https://doi.org/10.66280/cis.v1i1.233Keywords:
Healthcare Disparities, Fairness-Aware AI, Large Language Models, Electronic Health Records, Social Determinants of Health, Algorithmic Governance, Socio-Technical InfrastructureAbstract
Structural healthcare disparities remain a persistent challenge within global health systems, often exacerbated by the historical biases embedded in clinical data and medical decision-making processes. As Large Language Models (LLMs) increasingly permeate clinical workflows, there is an urgent need to ensure these systems do not merely replicate existing inequities but actively work to quantify and mitigate them. This paper proposes a systemic framework for integrating fairness-aware LLMs with multi-modal Electronic Health Records (EHRs) and Social Determinants of Health (SDoH) to provide a granular quantification of structural disparities. By synthesizing unstructured clinical notes, longitudinal diagnostic data, and socioeconomic indicators—such as housing stability, transportation access, and neighborhood-level deprivation indices—the proposed architecture identifies latent patterns of systemic neglect and diagnostic bias. We provide a deep analytical discussion on the structural trade-offs between model interpretability, predictive accuracy, and algorithmic fairness. Furthermore, the research explores the socio-technical dimensions of deployment, emphasizing the role of algorithmic governance, data sovereignty for marginalized communities, and the policy implications of using AI as a tool for institutional audit. Our findings suggest that while LLMs possess the potential to uncover deep-seated disparities, their implementation must be grounded in a robust infrastructure of fairness-aware constraints and cross-disciplinary oversight. This paper provides a comprehensive blueprint for leveraging advanced artificial intelligence to foster a more equitable, transparent, and resilient healthcare infrastructure.
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