Towards Error-Free EHRs: Reasoning-Intensive Consistency Verification Between Clinical Notes and Structured Tables in Electronic Health Records
arXiv:2605.26463v1 Announce Type: new Abstract: Data consistency between unstructured clinical notes and structured tables in Electronic Health Records (EHRs) is essential for patient safety and clinical decision-making. However, existing work on note-table consistency verification mainly relies on surface-level matching of numeric values or simple events. Such approaches fail to capture the reasoning underlying real-world EHR documentation, including clinical interpretation, event relations, and temporal changes. To address this gap, we introduce EHR-ReasonCon, a reasoning-intensive benchmark
The increasing sophistication of generative AI models and the critical need for reliable health data are converging, making deeper consistency verification in EHRs achievable and imperative.
Ensuring data consistency in Electronic Health Records (EHRs) through advanced AI reasoning is vital for patient safety, accurate clinical decision-making, and the foundational reliability of healthcare AI applications.
The ability to verify complex reasoning and temporal relationships between structured and unstructured clinical data will significantly improve the trustworthiness and utility of EHRs beyond simple numeric matching.
- · AI researchers in natural language processing
- · Healthcare providers and hospitals
- · Patients
- · Electronic Health Record (EHR) system developers
Improved reliability and safety in healthcare operations and diagnoses.
Accelerated development and adoption of AI-driven clinical decision support systems built on more trustworthy data.
Enhanced overall trust in digital health infrastructure, potentially leading to new models of preventative and personalized medicine.
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Read at arXiv cs.CL