Cross-Domain Embedding Models for Unified Patient–Financial Risk Profiling in Healthcare Ecosystems
Keywords:
cross-domain embeddings, unified risk profiling, electronic health records, healthcare analytics, revenue cycle management, actuarial forecastingAbstract
Clinical, behavioral, and financial data fragmentation in healthcare ecosystems hinders risk assessment and revenue cycle management. This study uses cross-domain embedding to generate a patient–financial risk profile latent representation using electronic health records, patient behavioral signals, and financial data. Probabilistic risk aggregation, multimodal embeddings, and joint latent space alignment predict clinical severity, use tendency, and financial exposure. Domain-specific semantics and cross-domain interactions improve actuarial forecasting, claim delinquency prediction, and reimbursement optimization. United embeddings increase risk classification, reduce clinical-financial information asymmetry, and enable data-driven hospital revenue cycle decision-making in unpredictable conditions. Scalable deployment, governance, and model interpretability in complex socio-technical healthcare systems are addressed by a robust theoretical framework for machine learning–driven embedding models in healthcare financial analytics.
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