Enterprise AI Analysis: Framework Development

The introduction highlights the significant utility of Clinical Decision Support Systems (CDSS) in general diabetes care (glycemic control, medication, complication risk) but notes a critical gap in their application to offloading footwear prescription for Diabetic Foot Ulcers (DFUs). Prescribing such footwear is complex, involving multiple patient-specific factors categorized across five dimensions: patient-related, condition-related, therapy-related, socioeconomic, and healthcare system-related. This paper proposes the Adaptive Semantic Framework (ASF) to address this gap by extending existing diabetes CDSS models.

The framework design is driven by two key adaptation scenarios essential for effective CDSS deployment in offloading footwear prescription:

• Cross-provider Adaptation: Adapting CDSS from specialized diabetes wound care clinics (with rich data) for use in general podiatry/pedorthist clinics (with limited data). This involves handling exact feature matches (recalibration), partial feature overlaps (inferring missing values via ontologies/heuristics), and new/unobserved factors (causal modeling/feature mapping).
• Within-provider Dynamic Adaptation: Enabling real-time updates to footwear recommendations as patient conditions evolve (e.g., new ulcers, balance deterioration) or as new assessment data/devices become available, without requiring a full system redevelopment. This requires a semantic-driven mutator to integrate new data while preserving causal consistency.

The Adaptive Semantic Framework (ASF) is a hybrid architecture designed for intelligent adaptation of CDSS for DFU footwear prescription. It comprises two core components: the Modifier and the Validator. The ASF facilitates adapting a CDSS from an initial diabetes data ecosystem (E1) to an offloading footwear prescription environment (E2) by identifying mismatches, generating structurally adjusted CDSS candidates, and preserving clinical validity. This modular approach ensures the framework can accommodate evolving patient needs and diverse healthcare settings.

Future enhancements aim to make the ASF adaptation pipeline increasingly autonomous and generalizable, focusing on three core dimensions:

• Causal Monitoring for Longitudinal Adaptation: Integrating incremental causal learning to proactively adjust decision pathways as new variables emerge over time, ensuring sustained clinical relevance and safety.
• Personalized Adaptation via Patient Subtyping: Incorporating hierarchical patient subtyping and meta-learning to tailor CDSS recommendations to individual patient trajectories and behavioral/biomechanical phenotypes.
• Cross-institutional Knowledge Sharing: Supporting federated adaptation across different organizations by transferring decision logic without exposing sensitive patient-level data, using shared ontological layers and encrypted feature embeddings.