The Landscape of Documentation Failure: Error Typologies, Temporal Dynamics, and Linguistic Patterns in Hospital Recordss in Academic Writing

Abstract

Clinical documentation failures remain a critical patient safety threat despite widespread electronic health record (EHR) adoption. Yet, the underlying linguistic mechanisms and temporal dynamics driving high-severity errors are poorly understood. This mixed-methods study characterized high-severity documentation failures across three tertiary hospitals, integrating multivariate logistic regression for risk quantification with qualitative discourse analysis and temporal mapping. We analyzed 60 cases using the Documentation Failure Severity Index (DFSI). Omission errors were the most prevalent (38%) and conferred the highest risk of severe adverse outcomes (adjusted odds ratio [aOR] = 3.12, $p = 0.001$). Crucially, ambiguous negation (e.g., "no clear evidence of...") demonstrated strong co-occurrence with omission errors ($\Phi = 0.62$), functioning as a linguistic "mask" for absent clinical information. Temporal analysis revealed distinct vulnerability windows: omissions peaked immediately upon admission (Day 0) and Day 1, while contradictions emerged during later hospitalization (Days 2–5). A novel four-tier typology linked this quantified risk to five recurrent qualitative linguistic glitches. These results confirm that documentation failures are temporally patterned and linguistically mediated phenomena. Proactive, linguistically-informed prevention strategies are imperative. Our findings support implementing admission-period structured templates, mid-stay reconciliation tools, and specialized staff training focused on identifying and mitigating ambiguous negation and passive voice constructions. Integrating these identified glitch patterns into natural language processing systems is essential for achieving real-time error prevention and improving patient safety across healthcare settings