Data Aggregation of HEDIS Measures using ETL Technologies - Avinash Dulam (International Journal of Computational and Experimental Science and Engineering, 2026)
The Healthcare Effectiveness Data and Information Set (HEDIS) has standardized performance measures. These measures aim to confirm that the health care services provided and utilized are of high quality. For reporting HEDIS measures, the data must be collected and aggregated from a variety of sources, including electronic health records (EHRs), claims data, pharmacy data, and lab information systems. This article uses Extract, Transform, Load (ETL) technologies to orchestrate data integration processes and provide trustable measure calculations. In the extract process, heterogeneous data sources are connected to the ETL pipelines by database interfaces, application programming interfaces (APIs), and streaming ingestion protocols, with security and compliance requirements. This phase includes cleansing and validation checks, as well as standardizing and recoding all of the different coding systems and heterogeneous constructs into a common structure that is consistent with the HEDIS specifications. The loading phase involves complex calculations and performance reporting. Business benefits include improved data quality via data validation at the source, process efficiency via workflow automation and scalability, and better decision support with improved visibility for all stakeholders in real-time operational performance. Implementation considerations include enterprise platforms, open-source and cloud-native applications, interoperability via Fast Healthcare Interoperability Resources (FHIR) standards, and available software development kits (SDKs). As new technologies like real-time data processing, cloud computing, and AI-based validation develop, healthcare organizations will choose their reporting technology based on their size, technical skills, and available resources, while also improving data management and adapting to new standards and regulations in a data-heavy environment.