Repairing Clinical-Agent Plans with Data-Induced Process Constraints

Tool-using clinical agents can place laboratory, imaging, and medication orders, yet their free-text plans make the care process difficult to verify. Hand-crafted checklists can support auditing, but they transfer poorly across settings and risk circular evaluation. Here, we induce machine-checkable process constraints from a held-out split of MIMIC-IV admissions. The method applies Wilson-bounded prevalence mining to identify required actions, clinical concepts, and temporal precedence, incorporating modality-ordered payloads. A deterministic minimal-edit operator then repairs structured plans using four atomic edits and disease-agnostic logic. Across four cohorts, repair raised hard-constraint satisfaction from 0.55–0.82 to 0.91–1.00 (appendicitis, pulmonary embolism, urinary tract infection, and cholecystitis). By contrast, Reflexion, self-consistency, and an LLM critic recovered little of these gains. Replacing the induced specifications with size-matched donor-cohort specifications reduced macro satisfaction by 0.115 and inserted appendectomies into 38 of 40 pulmonary embolism trajectories. LLM-generated specifications covered less than half of the mined requirements and reduced satisfaction by up to 0.307. Induction requires only tens of admissions and is robust to most threshold settings. These results indicate that verifiable clinical agent behavior can be derived from observed care processes rather than from another language model.