AI-Enabled Knowledge Creation: A Unified Database Approach in a Multinational Hospital Network

Abstract

This study outlines an approach for integrating health data from 72 hospitals in 13 countries, focusing on COVID-19 patients with cardiovascular issues. The goal is to combine data within a European project, prioritizing data privacy and utilizing an Extract, Transform, and Load (ETL) architecture for efficient data management. Machine learning, including AI, is applied to predict anomalies in patient data, enhancing the ETL process's capability to support such algorithms. An alert system is established to flag potential outliers for swift medical attention. Challenges such as interoperability and privacy are addressed, and the study evaluates the ETL and AI methods against key performance metrics, confirming their effectiveness. The unified database allows for benchmarking and sharing best practices across hospitals, improving healthcare quality. The paper contributes by detailing ETL development challenges in healthcare and showcasing the benefits of a centralized data repository for healthcare management, particularly through a machine learning algorithm designed to predict abnormal patient values, thereby aiding healthcare professionals in decision-making and improving patient care.