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arXiv · 2607.22954

Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records

Jian Lu·Panyu Chen·Miriam Treggiari·Robert Blessing·Danyang Zhuo·Chunhua Weng·William W. Stead·Anru R. Zhang
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Abstract

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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Jian Lu, Panyu Chen, Miriam Treggiari, Robert Blessing, Danyang Zhuo, Chunhua Weng, William W. Stead, Anru R. Zhang. 2026-07-24. Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records. https://arxiv.org/abs/2607.22954

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