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The escalating complexity of multimorbidity in aging populations necessitates advanced analytical frameworks for real-time patient stratification. This conceptual manuscript introduces a novel longitudinal network modeling approach centered on dynamic comorbidity graphs (DCGs), which enable continual population stratification through adaptive graph-based representations of electronic health records (EHRs). By integrating temporal disease trajectories, the framework facilitates proactive clinical decision-making without relying on empirical datasets or model training. Key components include graph construction algorithms that evolve with patient cohorts, comorbidity linkage mechanisms for risk propagation, and stratification pipelines that support interoperability across healthcare systems. Theoretical formulas are proposed to interpret risk propagation dynamics, decision confidence thresholds, and governance loads in deployment environments. The architecture emphasizes clinical workflow integration, addressing challenges in data modality heterogeneity and governance constraints. Through literature synthesis, we highlight synergies with existing AI governance systems, EHR intelligence ecosystems, and decision support pipelines. This framework advances healthcare analytics infrastructures by providing a scalable, theoretical foundation for managing longitudinal multimorbidity patterns, ultimately enhancing population health management in diverse clinical settings. Potential implications include improved resource allocation and reduced monitoring burdens in AI-assisted healthcare delivery.

Surgical site infections (SSIs) affect 2–20% of surgical procedures and are a major source of postoperative morbidity, prolonged hospitalization, readmission, mortality, and healthcare costs, making prevention a key priority. Existing prediction tools such as the NNIS index and SENIC score depend on a limited set of clinical variables including wound class, ASA status, and operative duration, while failing to capture complex interactions among patients, surgeons, and comorbidities. To address this limitation, we propose a graph attention network (GAT) framework that represents each surgical case as a heterogeneous graph composed of patient, surgeon, and comorbidity nodes, with intraoperative variables included as features and attention mechanisms used to learn the most influential relationships. This approach models relational dependencies such as the interaction between surgeon experience, patient conditions, and comorbidity combinations, enabling more accurate and context-aware SSI risk prediction to support personalized preventive interventions.