Towards Error-Free EHRs: Reasoning-Intensive Consistency Verification Between Clinical Notes and Structured Tables in Electronic Health Records

Researchers introduce EHR-ReasonCon, a benchmark dataset and EHR-Inspector, an LLM-based framework designed to verify consistency between unstructured clinical notes and structured data in Electronic Health Records. The work addresses a critical gap in healthcare data quality by moving beyond simple value matching to capture clinical reasoning, temporal relationships, and event interpretations that reflect real-world documentation practices.

This research tackles a fundamental healthcare IT challenge: ensuring data integrity between narrative clinical notes and structured EHR tables. Current consistency verification methods rely on surface-level pattern matching that fails to detect logical inconsistencies, temporal conflicts, or clinical interpretation mismatches—errors that can compromise patient safety and clinical decision-making. The introduction of EHR-ReasonCon with 8,048 expert-annotated entities from MIMIC-III creates a rigorous evaluation standard that captures the complexity of actual clinical documentation.

The healthcare industry has long struggled with EHR data quality issues, costing billions in adverse events and operational inefficiency. As EHRs become increasingly integrated with AI-driven clinical decision support systems, the reliability of underlying data becomes critical. This work represents a meaningful shift toward reasoning-based validation rather than heuristic rule checking.

For healthcare organizations, this advancement offers practical value through the EHR-Inspector framework, which leverages large language models to understand clinical context and relationships that rule-based systems miss. The framework's modular design—segmentation, entity extraction, temporal reference identification, and table verification—provides a replicable methodology that institutions could adapt to their own data governance workflows.

Looking forward, the integration of advanced reasoning into EHR auditing systems will likely accelerate as more healthcare providers recognize data quality as a foundational AI governance requirement. The real-world impact depends on adoption by EHR vendors and healthcare systems, with potential implications for regulatory compliance, malpractice reduction, and AI model reliability in clinical settings.

• →EHR-ReasonCon benchmark enables evaluation of consistency verification at the reasoning level, not just surface-level data matching
• →EHR-Inspector framework achieves state-of-the-art performance using LLM-based analysis with table-exploration tools for systematic evidence retrieval
• →Expert-guided annotations on MIMIC-III data provide high-quality ground truth for training and validating clinical note-table consistency
• →Current EHR inconsistencies stemming from clinical interpretation gaps pose patient safety risks that traditional validation systems cannot detect
• →Reasoning-intensive verification represents a shift toward AI-assisted data governance in healthcare documentation practices