An analytics framework for risk-controlled outpatient medication recommendation using conformal calibration

Medication recommendation systems generate either fixed top- ω medication lists or threshold-based multilabel predictions. However, these approaches do not control for the risk of omitting medications from true prescription set. This study proposes Coverage-Aware Medication Recommendation (CAMR), a framework that applies Conformal Risk Control (CRC) to a multilabel classifier to generate patient-specific set-valued medication recommendations. CAMR calibrates thresholds for a user-defined False Negative Rate (FNR)-based risk target. The framework was evaluated using a seven-year retrospective outpatient cohort from a tertiary hospital. The baseline classifier achieved an empirical coverage of 0.7962, an F1-score of 0.8276, and an average set size of 2.65 medications. At target risk levels of 0.20, 0.15, and 0.10, corresponding to nominal coverage levels of 80%, 85%, and 90%, CAMR achieved pooled empirical coverage rates of 0.8029 (95% CI 0.8010–0.8048), 0.8493 (0.8476–0.8509), and 0.8961 (0.8947–0.8973), with F1-scores of 0.8275, 0.8063, and 0.7137 and average set sizes of 2.70, 3.17, and 4.33 medications. A Top-5 strategy achieved similar coverage (0.8019) only with five medications and a lower F1-score of 0.5844. Conventional conformal prediction baselines required larger sets to achieve comparable coverage. Performance degradation was concentrated in infrequent medication classes and visits involving larger medication combinations. Sensitivity analyses showed that class-weighted and group-conditional calibration corrected under-coverage in rare classes and in departments with long prescriptions, while coverage remained close to the target over the test period. Overall, pooled empirical coverage approached nominal targets. The theoretical guarantee remains conditional on exchangeability, which may be affected by repeated visits and temporal distribution shift.