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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.

Clinical pathways are designed to standardize inpatient care for common conditions while allowing clinically justified individualization. Deviations from these pathways are frequent and may reflect either appropriate adaptation to patient complexity or potentially harmful departure from evidence-informed practice. Current deviation detection often depends on retrospective audit, static compliance rules, or aggregate dashboards. These approaches can miss subtle temporal drift in care delivery and rarely explain why a specific patient trajectory diverged from the expected pathway. This article proposes an interpretable machine learning model for detecting clinical pathway deviations in hospitalized patients. The model focuses on order sequences, vital sign trends, laboratory monitoring frequency, and provider decision patterns as dynamic indicators of care-process variation. Conceptually, the model would compare each patient’s evolving care trajectory with learned expected pathways using sequence-comparison and outlier-detection logic. SHAP-based or attention-informed explanations would identify the specific features responsible for a deviation flag, such as delayed monitoring, omitted follow-up testing, or unusual ordering behavior. The proposed model could detect when a patient’s care trajectory diverges from an expected pathway and provide a transparent rationale for review. For example, it could flag a missing repeat troponin, a delayed antibiotic escalation, or a laboratory monitoring pattern inconsistent with the patient’s clinical state. An interpretable pathway-deviation model could shift quality monitoring from manual, sample-based review toward continuous and transparent pathway surveillance. Such a system would support real-time clinical awareness, structured audit, and organizational learning.