Comprehensive Solution for Resolving Master Data Management Issues in Electronic Medical Records (EMR) Systems

Title

Comprehensive Solution for Resolving Master Data Management Issues in Electronic Medical Records (EMR) Systems

Author

1. Aditya Patel, Student, Weill Cornell Graduate School of Medical Sciences, New York, United States

Abstract

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.

Keywords

Conclusion

By leveraging advanced data standardization, normalization, matching techniques, machine learning algorithms, and NLP, the comprehensive solution ensures accurate, reliable, and consistent master drug lists in EMR systems, improving patient safety and treatment efficacy.

Author Contrubution

All study-related tasks, from conception and design to data analysis and manuscript creation, were solely managed by the author.

Funding

The research, authorship, and publication of this article were not funded by any specific grants from public, commercial, or non-profit agencies.

Conflict of Interest

All authors declare the absence of any conflicts of interest.

Data Sharing Statement

The study does not include any data sharing components.

Software And Tools Use

No specific software or tools were used in the research.

Acknowledgements

I extend my gratitude to everyone who contributed their expertise to this study and manuscript, and to the anonymous reviewers for their helpful comments.