Overcoming Challenges in Healthcare Data Warehousing with AI-Enhanced ETL Testing Tools

Abstract

The paper consists of four main sections. The first section presents the history of medical information systems and the services they offer to society. The necessity, benefits, and how to apply data warehousing technology in healthcare institutions are given in the second section. The third section introduces medical data concerns and challenges with healthcare data warehousing. The proposal for the AI-ETL model and its applicability to the healthcare data warehousing process are presented in the fourth section. Finally, the conclusion is presented in the fifth section.
The development of medical information systems has evolved with a history that is equal to the role of technology in the healthcare industry. Starting from the records on paper, the system then transformed into local digital systems, such as electronic health records, health informatics systems, health information management systems, and most recently, healthcare data warehouses. The healthcare data warehouse offers integrated data querying functions to gain valuable insights, provide decision support, and conduct research by end users. To maximize the potential outcome, maintain the data quality integrity of the healthcare data warehouse, and perform the ETL process necessary to overcome data quality issues, the healthcare industry has faced an increase in demand for ETL testing and data quality environments in recent years. Our AI-enhanced ETL testing tool meets this need, where it helps reduce the testing overhead by providing users with data extraction and translation testing automation, alerts and diagrams, and optimization advice. With the assistance of our AI-enhanced ETL tool, healthcare staff can easily and efficiently maintain and benefit from an ETL process environment.