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Contemporary healthcare delivery is characterized by frequent deviations from normative care pathways, driven by patient heterogeneity, resource variability, and real-time clinical judgment. Rather than viewing these deviations as noise to be minimized, the present conceptual work reframes them as structured knowledge artifacts amenable to systematic interpretation. We propose a sequence pattern language that encodes deviations as first-class clinical signals within AI-enabled healthcare analytics infrastructures. Building on established process-mining foundations and EHR intelligence ecosystems, the language formalizes deviation sequences into interpretable knowledge structures that can inform decision support pipelines without requiring empirical model training or performance benchmarking. Central to the contribution is the sequence pattern language for deviation knowledge (SPLiDeK) framework—an original architectural blueprint featuring a five-layer stack and a unique spiral governance topology. The framework integrates event-log normalization, temporal pattern discovery, deviation encoding, interpretive mapping, and adaptive feedback in a closed-loop design that maintains theoretical interoperability and governance compliance. Three interpretive formulas are introduced to conceptualize drift sensitivity, risk propagation, and governance load, providing architectural guidance for system designers. By treating care pathway deviations as the core substrate of clinical intelligence, SPLiDeK advances a new theoretical paradigm for resilient, interpretable AI orchestration in complex healthcare environments. The work contributes a conceptual systems architecture that bridges clinical workflow integration models, AI governance constraints, and data-exchange frameworks, offering a foundation for future infrastructural deployments.

Care pathways represent the temporal sequences of clinical events that define real-world patient journeys within complex healthcare systems. Recent advances in artificial intelligence have enabled the analysis of these pathways through sequence analytics, uncovering latent patterns beyond traditional guideline-based approaches. This narrative review synthesizes literature to examine three pillars of AI-enabled care pathway analytics: clustering methods that group similar patient trajectories, deviation detection techniques that identify meaningful variations from expected flows, and interpretability frameworks that support transparency and clinician trust.Drawing on process mining, sequence analysis, and explainable AI, the review highlights how electronic health record data can be transformed into actionable insights for clinical decision-making. Clustering approaches reveal hidden patient subgroups across domains such as oncology, cardiology, mental health, and critical care. Deviation detection methods expose bottlenecks, workarounds, and non-adherence associated with adverse outcomes and inefficiencies. Interpretability frameworks link algorithmic outputs to clinical logic, improving trust and adoption in healthcare settings.Cross-study evidence shows that while clustering and deviation detection methods have advanced significantly, their integration with interpretability remains limited, constraining large-scale implementation. The review proposes an integrative systems perspective that positions care pathway sequence analytics as a foundational component of AI-enabled healthcare infrastructure, encompassing data pipelines, model inference, intervention orchestration, and governance. Overall, AI-driven pathway analytics offers the potential to move healthcare from reactive, guideline-based care toward proactive, personalized, and continuously learning systems.