Automating Data Transformation in Medical Education: A Case Study on ETL Implementation

Abstract

The increasing abundance of data in the field of medical education has created both opportunities and challenges for effective data management and analysis. The proliferation of electronic health records, survey data, and other digital sources has introduced a pressing need for systematic approaches to consolidate and leverage this wealth of information. One such approach is the implementation of Extract, Transform, and Load processes, which can automate the transformation of raw data into a format suitable for analysis and decision-making.