Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records

• URL: http://arxiv.org/abs/2607.22954v1
• Date: Fri, 24 Jul 2026 23:43:35 GMT
• Title: Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records
• Abstract summary: General-domain large language model (LLM) can surface from real-world discharge summaries.<n>LLM surfaced 3,460 candidate inconsistencies, affecting 69.7% of admissions.
• Score: 16.68744679025527
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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