Decision-Informed Online Conformal Prediction for ICU Resource Allocation

Hospitals allocate scarce ICU resources—nurses, beds, discharge slots—using predictions of patient length of stay (LOS), but prediction errors under shifting patient populations lead to costly misallocations. Online conformal prediction provides adaptive uncertainty sets that track a target coverage rate (e.g., 90%) without distributional assumptions, but the resulting robust decisions incur a Price of Coverage—the excess cost of hedging against uncertainty. We observe that standard conformal methods distribute uncertainty budgets uniformly, including on resources the optimizer assigns to minimum levels. We propose Decision-Informed Conformal Adaptation (DICA), which uses the downstream optimization’s resource allocation as feedback to reshape uncertainty margins: tighter where allocation is minimal (saving cost), wider where allocation is high (strengthening protection). DICA preserves the same adaptive quantile update and scalar coverage tracking as standard online conformal; the reshaped radii do not carry a formal per-component guarantee, but decision-level analysis shows that coverage misses are confined to lower-bound dimensions where their clinical cost is negligible. Across 328K patient stays from three real clinical datasets (MIMIC-IV, eICU, and a general hospital cohort), DICA reduces the Price of Coverage by 43–54% relative to uniform conformal while maintaining approximately 90% coverage. Under chronological patient ordering, static calibration methods degrade to 78–86% coverage, while online conformal methods remain near the target rate.