GNN-Based Risk Adjustment and Medical Coding for Interoperable Healthcare
Keywords:
Graph Neural Networks; risk adjustment; medical coding; interoperability; healthcare data; ontology; data standards; semantic interoperability.Abstract
Risk adjustment compensates healthcare payers for patient populations’ varying risk profiles. Existing techniques poorly capture diagnostic complexity and comorbidity patterns. Medical coding transposes clinical narratives into coding schemas but is data- and labor-intensive. Graph Neural Networks (GNNs) offer rigorous exploration of both tasks within an interoperable ecosystem augmented with data standards and semantic interoperability.
Risk adjustment and medical coding using GNNs explore patient complexity and comorbidity patterns in state-of-the-art risk adjustment methods and extract codes from clinical narratives in hospital-based billing. In the first application, diagnoses, procedures, and laboratory tests from discharge reports are represented as heterogeneous graphs. Patient complexity is encoded in node features, and GNN-based modeling is evaluated against multiple state-of-the-art risk adjustment baselines. In the second application, clinical narratives are automatically enriched with codes from the International Classification of Diseases and Current Procedural Terminology ontologies. Code extraction is aligned to the respective billing schemas, and GNNs are employed to map additional hospital categories. External information from ConceptNet and existing data are infused into the coding via data- and evidence-based articulation with allied domains.
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