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This paper introduces Case2Flow, a novel task aimed at retrieving the most relevant guideline flowchart for specific patient cases from a comprehensive collection of medical guidelines. By constructing FlowAtlas, a curated corpus of flowcharts and aligned case-flowchart pairs, the authors address the challenge of locating actionable clinical pathways within extensive medical guidelines. The proposed CRISP method enhances retrieval performance by up to 18.71 percentage points in Recall@1, demonstrating its effectiveness in improving decision support for clinicians.
CRISP significantly boosts retrieval accuracy for clinical decision-making by effectively filtering out uninformative flowchart regions.
Medical guidelines encode rich, evidence-based decision logic, yet the specific decision artifact a clinician needs is hard to locate within a guideline, let alone across guidelines covering plausible diseases and treatments. While guideline passages have supported end-to-end question answering, flowcharts remain largely underused in decision support despite their ability to encode actionable clinical pathways. We therefore introduce Case2Flow, a task designed to retrieve the most relevant guideline flowchart for a given patient case from a collection of guideline documents. To support it, we construct FlowAtlas, a curated corpus of 202 flowcharts extracted from 2,080 medical guidelines, together with a pipeline that synthesises 1,911 aligned case-flowchart pairs. Our evaluation of multimodal retrieval methods reveals systematic failure modes, including overreliance on keywords and spurious token-patch matches induced by uninformative background regions in flowcharts. Motivated by this, we propose CRISP, a training-free scoring method that sharpens late-interaction retrieval by suppressing uninformative patches, discounting ambiguous token matches, and incorporating bidirectional query-image alignment. CRISP improves Recall@1 by up to 18.71 percentage points, while a blinded physician assessment on published case narratives provides preliminary feasibility evidence beyond synthetic queries.
