INFORMATION AND REPORTING MODULE FOR THE ELECTRONIC MEDICAL RECORDS (EMR) DATA ARCHIVE

Main Article Content

Bander Alshehri
Sana Ahmad
Saleemullah
Shazia fathima
Dr Haseeb Umar

Keywords

Neuroscience databases, data archives, statistical analysis, data management, Python programming, Postgre SQL, Django development, Visual Paradigm modeling.

Abstract

Objective: This study outlines the development of a statistical and reporting module aimed at addressing challenges in monitoring, classifying, and extracting meaningful information from the extensive neuroscience database maintained by the Electronic Medical Records (EMR). The primary goal is to enhance the representativeness of data and facilitate efficient analysis for the benefit of neuroscience researchers.


Methods: The identified limitations in the current system prompted the creation of a module incorporating Python programming, Postgre SQL data management, Django development framework, Visual Paradigm modeling, and Django development language. These technologies and tools were strategically employed to establish a robust statistical and reporting system capable of handling the vast amounts of data in the archive.


Results: The implementation of the module is anticipated to revolutionize data management at the Electronic Medical Records (EMR). By providing features for representativeness from diverse perspectives and enabling the extraction of valuable information, the module aims to assist neuroscience scientists in their research and application scenarios.


Conclusion: The utilization of advanced technologies in the development of this module signifies a significant step toward overcoming challenges associated with data analysis and reporting in neuroscience research. Upon integration into the repository, the module is poised to offer metadata and archived data information, aligning itself with global standards in neuroscience data management.

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