Master Data Management in Healthcare: AI-Driven Architectures for Data Governance and Security
Authors:-Rishanth
Abstract-:The healthcare industry is undergoing a profound digital transformation, driven by the exponential growth of data from electronic health records (EHRs), wearable devices, genomics, and telemedicine. Amid this data surge, Master Data Management (MDM) has emerged as a critical strategy to harmonize, manage, and govern healthcare data efficiently. With the integration of Artificial Intelligence (AI), MDM systems are becoming more robust, scalable, and secure, ensuring better patient outcomes, streamlined workflows, and enhanced decision-making. This review explores the development and adoption of AI-driven architectures for MDM in healthcare, focusing on their role in ensuring data governance, integrity, security, and compliance with regulatory standards like HIPAA and GDPR. Sections examine the key challenges of traditional MDM approaches, how AI enhances data quality and governance, and the implementation of machine learning algorithms in data cleansing, deduplication, and metadata management. The article also highlights use cases, ethical implications, and future trends where AI and MDM intersect in improving healthcare systems globally. As healthcare organizations strive for digital maturity, AI-driven MDM offers a pathway toward a unified, trusted, and secure data ecosystem that is both agile and adaptive to the evolving needs of clinicians, patients, and policymakers.
