Where Can Risk Be Repaired? Evidence Acquisition and Escalation for Selective Clinical LLM Decision-Making

In clinical decision making under incomplete evidence, current methods mainly answer whether the current decision might be wrong, but they cannot tell whether to repair an error by asking for additional evidence or deferring the case to a stronger model. We propose a repairability-aware decision framework that separates current decision risk from two distinct forms of repairability. We also construct a frozen, deployable DECIDE/ASK/DEFER router. On the MIMIC-IV held-out test cohort, Always DECIDE achieves 36.39% accuracy, the deployable ASK-only policy reaches 59.02%, and the joint router reaches 62.69%, improving over ASK-only by 3.67 percentage points. In a cross-family robustness evaluation using Qwen3-8B and Qwen3-32B, we again observe the non-redundant structure of ASK and DEFER repair sets. However, the joint router does not show a statistically significant advantage over ASK-only. This suggests that the repair channel structure can generalize across model families, while the incremental gain from joint routing is model-pair dependent. We also find that increasing the ASK budget does not monotonically improve accuracy. Learned stopping reduces the mean number of questions from 5.00 to 3.18 without evidence of a decrease in accuracy.