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The integration of social determinants of health (SDOH) into artificial intelligence (AI) models for healthcare systems presents a critical challenge in preserving equity while avoiding proxy leakage, where sensitive attributes inadvertently influence predictions through correlated variables. This conceptual manuscript proposes a novel causal design pattern that enables the seamless incorporation of SDOH data into clinical AI architectures without compromising fairness. By leveraging causal inference principles, the pattern mitigates leakage pathways in decision support pipelines, ensuring that equity-preserving modeling aligns with governance frameworks in electronic health record (EHR) intelligence ecosystems. We outline a unique architectural framework, the causal equity orchestrator (CEO), which features layered causal nodes, feedback loops for drift detection, and interpretive formulas for risk propagation and decision confidence. Drawing on a synthesis of recent literature from clinical AI system architectures and healthcare analytics infrastructures, this work emphasizes theoretical implications for interoperability in diverse clinical workflows. The design promotes robust, bias-resistant integration, fostering equitable outcomes in population health analytics without empirical validation. Ultimately, this pattern offers a blueprint for AI developers and health informatics specialists to construct systems that uphold ethical standards in SDOH-driven modeling, addressing disparities in underserved communities through principled causal mechanisms.