AI-driven and Non-AI Methods for Electronic Health Records Duplication Remediation for Healthcare Organizations

Abstract

Duplicate Electronic Health Records (EHRs) represent a critical challenge for healthcare organizations, leading to incomplete patient data, potential medical errors, increased operational costs, and compromised quality of care. Traditional methods such as deterministic and probabilistic matching, combined with Enterprise Master Patient Index (EMPI) systems and robust data governance, have long been the cornerstone in tackling duplicates. However, these approaches face limitations when handling large-scale, heterogeneous patient data. In response, AI-driven techniques—particularly Machine Learning (ML)—have emerged as powerful alternatives, enhancing record linkage accuracy, automation, and adaptive capabilities. This article provides an in-depth review of current non-AI (deterministic and probabilistic) and AI-based deduplication strategies, including advanced ML algorithms, biometric patient identification, and real-time re-checking services. We analyze case studies from leading healthcare systems, demonstrating a reduction of duplicate rates from over 20% to under 2%. Additionally, the paper explores key management and organizational factors for successful adoption of deduplication solutions, emphasizing the need for adequate training, policy development, and continuous monitoring. Concluding with practical recommendations and future directions, this research serves as a comprehensive resource for healthcare IT and data management executives aiming to ensure high-quality patient records, strengthen compliance, and support value-based care initiatives.