CoREL: Conditional Relative Evidence Learning for Multi-Institutional Medication Recommendation

Medication recommendation in real-world clinical settings must account for heterogeneity in prescribing practices across healthcare institutions. Existing methods typically fit observed prescriptions directly, potentially conflating patient-dependent prescribing evidence with institution-specific prescribing tendencies. We propose CoREL, a multi-institutional medication recommendation framework that separates shared evidence learning from institution-specific prescription calibration. Under an additive decomposition of prescription decisions, we introduce conditional relative evidence learning to eliminate institution–medication tendencies while enabling shared patient-dependent prescribing evidence to be learned across institutions. Dual-level calibration then establishes a global prescribing reference and estimates institution-specific tendencies, recovering institution-conditioned prescription probabilities. An inference-time refinement procedure further balances prescription support against known harmful drug–drug interactions (DDIs). Experiments on eICU, MIMIC-IV, and a real-world two-center inpatient dataset demonstrate improved recommendation accuracy and lower DDI rates, with particularly pronounced gains in multi-institutional settings. Code is available at https://anonymous.4open.science/r/CoREL-01C5.