Abstract

In high-volume hospital environments, maintaining the accuracy of patient records is critical for ensuring clinical safety, efficient resource allocation, and institutional credibility. As Electronic Health Records (EHRs) become increasingly integral to healthcare operations, the risk of errors in data capture, entry, and maintenance is exacerbated by system complexity and operational volume. This study proposes a risk-based surveillance framework grounded in a comprehensive review of existing literature. It aims to identify methodologies for detecting and mitigating patient record inaccuracies by assessing institutional risk profiles and surveillance practices. Drawing on interdisciplinary insights from health informatics, data governance, and clinical risk management, the paper explores the design and implementation of adaptive, scalable models for EHR accuracy surveillance in high-throughput healthcare systems. The model emphasizes proactive error detection, critical control points, and continuous auditing mechanisms. This work contributes to a growing body of research that seeks to operationalize data quality principles in real-time healthcare settings without reliance on primary data, making it especially relevant for policy development and institutional benchmarking.