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In the evolving landscape of artificial intelligence integration within healthcare systems, ensuring fairness in clinical prediction models remains a critical challenge, particularly under distribution shifts that can exacerbate biases in decision support pipelines. This conceptual manuscript proposes a novel evaluation protocol centered on fairness stress testing, designed to assess the robustness of AI-driven clinical models against data drifts in electronic health record (EHR) intelligence ecosystems. We introduce the distribution-shift fairness evaluation network (DSFEN), a layered architectural framework that incorporates governance mechanisms, interoperability standards, and workflow integration to simulate theoretical stress scenarios without empirical data. The protocol emphasizes pre-deployment monitoring and post-integration surveillance, drawing on theoretical models of risk propagation and decision confidence to mitigate inequities in healthcare analytics infrastructures. By synthesizing recent literature on AI governance and clinical workflow models, we outline how DSFEN facilitates a proactive approach to fairness, addressing gaps in current interoperability frameworks. This work contributes to the discourse on ethical AI deployment in medicine, advocating for distribution-shift–aware protocols that enhance equity in clinical decision-making. Ultimately, the proposed system aims to foster resilient AI ecosystems capable of adapting to dynamic clinical environments, ensuring that prediction models uphold fairness principles across diverse patient populations and shifting data landscapes.