Heterogeneous Graph Neural Networks for Drug Reimbursement Forecasting in National Health Systems
Keywords:
heterogeneous graph neural networks, drug reimbursement forecasting, national health systems, pharmaceutical expenditure, healthcare finance, reimbursement policy modelingAbstract
Complex, multi-stakeholder, multi-data source, interdependent entity, and financial risk national health system drug reimbursement decision-making. Nonlinear pharmaceutical consumption, pricing negotiations, clinical efficacy, and regulatory limits may be missed by classic econometric and statistical forecasting models. Heterogeneous graph neural network forecasting mimics system-level drug reimbursement probabilities, use patterns, and downstream budgetary effects. The proposed clinical, economic, and administrative learning model treats drugs, patients, providers, manufacturers, regulatory bodies, and therapeutic classes as distinct nodes connected by semantically meaningful relationships. Real-world evidence, claims data, prescription behavior, and policy aspects predict higher-order interactions and temporal propagation effects that influence reimbursement and financial sustainability. Heterogeneous GNN predicts pharma reimbursement approvals, expenditure growth, and risk concentration better than machine learning and graph baselines. Graph-based representation learning may enhance budget impact assessments, evidence-based reimbursement planning, and national drug finance transparency and robustness.
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