From Data to Action: Leveraging data engineering for healthcare decision-making

The healthcare sector's digital transformation has catalyzed an unprecedented surge in data production, creating both challenges and opportunities for improving patient care and operational efficiency. This technical review explores how data engineering serves as the foundational infrastructure for converting raw healthcare information into actionable clinical and administrative insights. Beginning with the architectural components necessary for effective data management—including ingestion frameworks, storage paradigms, and scalable processing pipelines—the article demonstrates how these technical foundations enable sophisticated analytics capabilities. Advanced business intelligence ecosystems, machine learning pipeline integration, and real-time analytics architectures are examined through the lens of healthcare-specific requirements and outcomes. Case studies illustrate successful implementations of predictive readmission models, resource optimization systems, and clinical decision support platforms, highlighting the technical innovations that drive measurable improvements. Looking forward, emerging technologies such as edge computing, federated learning, enhanced interoperability standards, and automated governance controls promise to further transform healthcare's data landscape while addressing privacy concerns and regulatory requirements. Throughout, the article emphasizes how robust data engineering directly translates to improved clinical outcomes, enhanced operational efficiency, and more informed decision-making across all levels of healthcare delivery.