Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records
This study uses a two-stage large language model pipeline to automatically detect internal inconsistencies in discharge summaries from electronic health records. Applied to 3,000 MIMIC-IV-Note discharge summaries, the pipeline surfaced 3,460 candidate inconsistencies affecting 69.7% of admissions. Expert review revealed failure modes in temporal reasoning, evolving-diagnosis context, and outpatient prescribing knowledge. A graded ontology is proposed to categorize inconsistencies, providing a foundation for large-scale EHR inconsistency analysis.
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[Submitted on 24 Jul 2026]
Title:Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records
View a PDF of the paper titled Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records, by Jian Lu and Panyu Chen and Miriam Treggiari and Robert Blessing and Danyang Zhuo and Chunhua Weng and William W. Stead and 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.
Subjects:
Computation and Language (cs.CL); Applications (stat.AP)
Cite as: arXiv:2607.22954 [cs.CL]
(or arXiv:2607.22954v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.22954
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Anru R. Zhang [view email] [v1] Fri, 24 Jul 2026 23:43:35 UTC (99 KB)
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