Designing a Clinical Intelligence Layer for Complex Oncology Care: A Reference Architecture Illustrated by Sarcoma and SHAPEHub

Background: Healthcare information systems are effective at documenting encounters but remain less capable of representing evolving patient states, coordinating cross-disciplinary decisions, and reconnecting decisions with longitudinal outcomes and value. This Perspective derives a reference architecture for a clinical intelligence layer positioned between source systems and accountable care delivery. Methdology: Using a design-science approach, we combined requirements from learning health systems, semantic interoperability, clinical workflow modelling, value-based healthcare, and the sequence-sensitive characteristics of sarcoma care. We abstracted two complementary cross-domain design patterns—risk-aware common representation and ontology-driven workflow—and translated them into healthcare-specific requirements. Results: The resulting architecture contains seven layers: (1) source integration and provenance; (2) a canonical semantic model; (3) longitudinal patient-state representation; (4) analytics and scenario reasoning; (5) workflow-embedded decision support; (6) outcome and value feedback; and (7) network learning and governance. We additionally specify a minimal formal information model, a bitemporal knowledge-state model distinguishing clinical/effective time from information-availability time, provenance and contradiction semantics, analytic validation gates, and a synthetic architectural verification using four pre-specified patient trajectories comprising 18 synthetic records. All nine pre-specified architectural invariants were satisfied, including correct historical-state reconstruction, preservation of superseded versions, recommendation–patient decision–treatment separation, mixed temporal granularity, and zero retrospective information leakage. Conclusions: SHAPEHub is presented as an implementation-informed sarcoma exemplar rather than as a validated product. The proposed layer is intended to complement—not replace—electronic health records, interoperability standards, common data models, registries, and disease-specific applications. Further technical validation in production-like environments, together with workflow, safety, and clinical validation, remains necessary before claims of utility or transferability can be made.