用大模型自动发现病历中的矛盾信息，提升医疗安全。
Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records
- 分两阶段用大模型识别病历中的潜在矛盾
- 发现3460处矛盾，影响近70%住院记录
- 适合医疗AI、临床质控人员参考
目标：分析通用大语言模型（LLM）在真实出院小结中能识别出哪些内部文档矛盾，并揭示限制其大规模可靠应用的常见失败模式。方法：采用两阶段LLM流程——先用Gemini 2.5 Pro进行开放式候选识别，再用Gemini 2.5 Flash进行上下文验证，处理了3000份随机抽取的MIMIC-IV-Note出院小结。部分输出由临床专家手动审查。结果：该流程共识别出3460个候选矛盾，涉及69.7%的住院记录。代表性矛盾涵盖人口统计、过敏史、操作、诊断、检验、用药及诊疗计划等多个领域，直接影响临床判断或患者安全。专家评审还发现，当验证需时间推理、疾病演变背景或门诊用药惯例时，模型常出现失败。讨论：检测高度依赖上下文，许多标记对需锚定至原始段落和临床领域，判断是否为真矛盾或信息缺失。我们提出一种分级本体论，涵盖严格矛盾与模糊情况，并设计框架按类别、段落、领域和矛盾轴线分类每项标记。结论：本研究建立方法论基础与概念框架，为后续经验证的大规模电子病历矛盾分析提供指引。
原文摘要 · Abstract (English)
Objective: To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale. Materials and Methods: We applied a two-stage LLM pipeline---open-ended candidate identification (Gemini 2.5 Pro) followed by context-grounded verification (Gemini 2.5 Flash)---to 3,000 randomly sampled MIMIC-IV-Note discharge summaries. A subset of the pipeline output was then reviewed manually by clinical experts. Results: Our pipeline surfaced 3,460 candidate inconsistencies, affecting 69.7% of admissions. Representative examples spanned demographics, allergies, procedures, diagnoses, laboratory, medications, and care-planning domains, with direct implications for clinical reasoning or patient safety. Expert review also revealed recurring failure modes that arise when verification requires temporal reasoning, evolving-diagnosis context, or knowledge of outpatient-prescribing conventions the model does not natively possess. Discussion: Detection is highly context-dependent: many flagged pairs require anchoring each statement to its source section and clinical domain, then assessing whether the conflict reflects a true contradiction or missing context. We propose a graded ontology spanning strict contradiction and ambiguity, with a schema characterizing each flagged case by category, section, domain, and inconsistency axis. Conclusion: This formative study establishes a methodological foundation and conceptual framework to guide subsequent validated, large-scale EHR-inconsistency analysis.
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