Elevating Healthcare ETL Quality: The Role of Automated Testing in Ensuring Data Excellence

Abstract

Data quality significantly impacts patient care, operational efficiency, and regulatory compliance in healthcare. Extract, Transform, and Load (ETL) processes connect and manage healthcare data, yet data quality is difficult to maintain. Larger data volumes and complexity make manual or semi-automated data quality assessments insufficient. Where automated testing improves healthcare ETL data quality is examined. Automation tools can discover and resolve ETL pipeline errors, ensuring the dataset meets healthcare standards. This study investigates healthcare ETL data quality before and after automated testing.Large hospital systems and national health data repositories use automated testing technology. Automated tools help with data accuracy, completeness,and consistency and are versatile for complex datasets. This study also examines the technical, organizational, and ethical challenges of automated testing in healthcare and offers solutions for healthcare organizations. The paper addresses some of the ways in which improved data quality affects healthcare outcomes and suggests further research in automated data quality management.