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Patient-reported outcomes (PROs) represent a critical dimension in modern healthcare analytics, capturing subjective patient experiences through longitudinal self-reported signals. However, these signals are susceptible to drift—gradual shifts in data distribution, response patterns, or interpretative biases—that can undermine the reliability of AI-driven clinical decision support systems. This conceptual manuscript introduces a novel framework for assessing stability and bias in PROs within AI-integrated healthcare infrastructures. Drawing on theoretical principles from clinical AI governance and data interoperability models, we propose the longitudinal signal integrity network (LSIN), a multi-layered architecture designed to monitor, evaluate, and mitigate drift in self-reported data streams. LSIN incorporates adaptive monitoring nodes, bias quantification protocols, and feedback loops to ensure sustained signal fidelity across deployment lifecycles. Through a synthesis of recent literature on AI system architectures and healthcare analytics, we explore the theoretical implications of drift on clinical workflows, emphasizing interoperability challenges and governance requirements. Conceptual formulas are presented to interpret drift sensitivity, bias propagation, and assessment resource demands. This work advances conceptual understanding by outlining infrastructural strategies for robust PRO integration, fostering resilient AI ecosystems in healthcare without relying on empirical evaluations or performance metrics. Ultimately, LSIN provides a theoretical blueprint for enhancing the trustworthiness of longitudinal self-reported signals in clinical AI pipelines.