ReCIPE: Collaborative Agents Learning Reusable Procedures for Clinical Information Processing and Extraction

Preparing structured clinical data for healthcare agents requires extracting predefined fields from heterogeneous notes and tables. This process is hindered by conflicting evidence, costly source discovery, and limited reuse of extraction experience across patients. We introduce ReCIPE ($\text{Re}$usable $\text{C}$linical $\text{I}$nformation $\text{P}$rocessing and $\text{E}$xtraction), a multi-agent system that learns reusable extraction procedures represented in an evolving typed dependency graph, RouteGraph. Field-semantic retrieval guides a compiler agent in proposing executable routes, while a verifier agent checks their trial outputs against patient evidence. GraphIE, our extraction algorithm, executes these routes with evidence propagation and consistency checks, supporting jointly required inputs and alternative routes. Accepted procedures accumulate across training patients without accessing target reference values or retaining patient-specific answers. We also introduce MIMIC-IV-Graph, a new benchmark pairing patient-specific source values with explicit source and target field definitions, covering 43 targets across four types. Starting from learned graph snapshots with admission-local route discovery, ReCIPE outperforms eight baselines in overall extraction quality on unseen patients under a shared language model backbone. Compared with the direct usage of LLM, it improves the overall score from 0.213 to 0.967. Per admission, it reduces test-time token consumption by 99.7% and LLM calls by 96.9%. In a blinded review by five physicians, 396 of 400 reviewed output differences with an oncology workflow combining manual abstraction and simple regular expressions are judged correct only for ReCIPE. These results support evolving, evidence-grounded procedures for accurate and efficient clinical data preparation.