GuideTree: Guideline-Induced Review Trees for Long Medical Records

Chengze Ge, Ruiqing Zhang, Yining Wang, Shengping Liu, Liang Jiaen, Weihuang, Weihua Chen

Keywords: medical document review, guideline compliance, evidence-grounded reasoning, efficient LLM workflows

TL;DR: GuideTree compiles textual medical guidelines into a fixed, evidence-grounded review tree that delivers accurate, fast, and reproducible decisions on long medical records.

Abstract: Reviewing medical records for clinical and insurance decisions must handle long, heterogeneous documents while producing consistent, traceable, guideline-compliant outcomes under strict latency and cost constraints. We propose GuideTree, which compiles textual guidelines into a fixed review tree of evidence-grounded verification primitives. GuideTree uses short per-document summaries only for routing each check to a minimal set of document types and candidates; final verification always reads full document text and returns structured evidence. The tree is induced offline via a cost-aware split-and-prune search and updated safely through regression-tested, versioned patches. Across 1,000 cases from four industrial review scenarios and four LLM backbones, GuideTree achieves 84.5--92.8 Macro-F1, outperforming the strongest non-expert baselines by 3.3--7.6 points and matching ExpertTree within 0.2--0.6 points (avg. 0.38). On chronic disease with Qwen3-235B-A22B-Instruct, GuideTree reduces average I/O volume to 74K input+output characters (-82% vs. long-context prompting) and average latency to 22s (-83% vs. long-context prompting), while reaching 99% decision consistency over K=5 reruns.

Submission Type: Deployed

Submission Number: 392