Master Data Management (MDM) issues in Electronic Medical Records (EMR) systems, such as inconsistent drug information, pose significant challenges for healthcare providers. This paper outlines a comprehensive solution to these issues by implementing advanced data standardization, normalization, and matching techniques. The solution leverages machine learning algorithms, Natural Language Processing (NLP), and robust database management practices to ensure accurate, reliable, and consistent master drug lists.
The study does not include any data sharing components.
All authors declare the absence of any conflicts of interest.
All study-related tasks, from conception and design to data analysis and manuscript creation, were solely managed by the author.
The research, authorship, and publication of this article were not funded by any specific grants from public, commercial, or non-profit agencies.
No specific software or tools were used in the research.
I extend my gratitude to everyone who contributed their expertise to this study and manuscript, and to the anonymous reviewers for their helpful comments.
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