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Data redundancy in health records represents a significant challenge for healthcare organizations, leading to inefficiencies, increased storage costs, and potential errors in patient care. The proliferation of digital health information, including electronic health records (EHRs), necessitates the implementation of robust data deduplication algorithms to streamline data management processes and enhance data integrity. This study investigates various data deduplication algorithms, analyzing their effectiveness in reducing redundancy within health records while maintaining accuracy and accessibility.The research employs a comprehensive evaluation framework that assesses each algorithm based on criteria such as computational efficiency, scalability, and impact on data integrity. Various algorithms, including hash-based methods, content-based deduplication, and machine learning approaches, are examined to determine their suitability for health record management. Case studies from diverse healthcare settings illustrate the practical implications of deduplication strategies, highlighting the potential benefits of implementing these techniques. Preliminary findings indicate that while several algorithms demonstrate significant potential in reducing data redundancy, their effectiveness varies based on the nature of the health data and the specific context of implementation. For instance, hash-based deduplication methods are highly efficient but may struggle with complex data structures often encountered in healthcare. Conversely, machine learning algorithms show promise in adapting to evolving data patterns but may require substantial computational resources. This study not only contributes to the understanding of data deduplication techniques in healthcare but also offers a strategic framework for healthcare organizations to optimize their data management practices. By identifying best practices for implementing effective deduplication algorithms, this research ultimately aims to improve the quality of health records, enhance patient care, and support cost-effective data management solutions.