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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.

 In the evolving landscape of artificial intelligence (AI) for healthcare, patient-centered approaches are essential to balance preventive care benefits against potential burdens. This conceptual manuscript introduces a novel framework for generating preventive care recommendations through explicit benefit–burden trade-offs, prioritizing individual patient utilities. Drawing from clinical AI architectures, healthcare analytics infrastructures, and electronic health record (EHR) intelligence ecosystems, we propose the patient utility trade-off architecture (PUTA). This multi-layered system integrates decision support pipelines with AI governance and interoperability frameworks. PUTA employs utility-based modeling to quantify benefits such as improved health outcomes and burdens like treatment side effects or resource demands, facilitating personalized recommendations in preventive settings. Theoretical formulas capture decision confidence and burden propagation, ensuring interpretive insights into system dynamics without empirical validation. We synthesize recent literature on clinical workflow integration and monitoring systems, highlighting how PUTA addresses gaps in patient-centered AI deployment. By emphasizing infrastructural uniqueness, including adaptive feedback topologies, this framework advances equitable preventive care. Implications for governance in diverse clinical environments underscore the need for robust data exchange and ethical monitoring, positioning PUTA as a foundational tool for future AI-driven healthcare systems.