AUTOMATED ETL SYSTEM FOR A NETWORK OF MEDICAL INSTITUTIONS - BADZ, VIKTORIIA; DIMBROVSKA, KARINA (Herald of Khmelnytskyi National University. Technical sciences, 2025)
The paper presents the results of the design and implementation of a multi-layer Extract–Transform–Load (ETL) system developed to ensure effective collection, integration, and processing of heterogeneous medical data originating from a wide network of healthcare institutions. The proposed solution enables the automatic extraction of structured and semi-structured information from distributed sources, its subsequent transformation according to unified standards, and reliable loading into centralized storage repositories. Particular attention is paid to the modular architecture of the system, which allows flexible adaptation to various types of medical data, including records of patient visits, financial transactions, and lists of prescribed diagnostic and therapeutic services. For the purpose of in-depth analysis and decision support, an analytical platform was integrated with the ETL pipeline. On this platform, a set of interactive dashboards and visualization tools was developed, enabling medical administrators, healthcare managers, and policy makers to explore aggregated indicators, identify trends in service provision, and evaluate resource utilization across different institutions. Such visualization improves the transparency of medical processes and provides evidence-based support for strategic planning. The system is oriented toward practical use within the field of medical analytics, demonstrating efficiency in routine monitoring and operational management tasks. At the same time, its architectural scalability and compatibility with modern data standards make it a potential foundation for extending functionality beyond individual institutions. In particular, the proposed approach can be scaled up to create regional or even nationwide information platforms, thereby supporting integrated healthcare management, public health monitoring, and the development of data-driven health policies.
Keywords: data warehouse; data processing; healthcare; analytics