Laboratory information systems have for decades formed the backbone of medical diagnostics. Their primary purpose has been to store, manage and transfer test results securely and efficiently. However, healthcare is now entering a phase where data complexity, interoperability, real-time analytics and artificial intelligence fundamentally change what is expected from laboratory systems.

Traditional LIMS architectures were not designed to support continuous data reuse across care processes, population health, research and AI-driven decision support. As diagnostics becomes increasingly data-driven, limitations in data models, context handling and cross-system integration become critical barriers to innovation.

This session presents the Nordic Data Model as a new architectural and conceptual foundation for diagnostic intelligence. The model focuses on unifying structured laboratory data with clinical context, metadata and longitudinal patient information to enable advanced analytics, AI-based pattern recognition and real-time insights. The lecture addresses how a common data foundation can support the shift from reactive diagnostics toward earlier detection, predictive monitoring and preventive care.

The session also discusses strategic implications for healthcare organizations, including data governance, interoperability, regulatory considerations and the prerequisites required to move from isolated systems toward an ecosystem where laboratory data becomes a core driver for clinical decision support and population-level insights.

The presentation is based on current developments within Nordic healthcare IT and internationally recognized principles for health data interoperability, AI readiness and learning health systems.