Enhancing clinical insight through business intelligence over OpenEHR-structured data

Purpose This study presents a modular and scalable functional architecture for the standardisation, processing and analysis of clinical data in hospital environments. The aim is to bridge the gap between OpenEHR modelling and its near-real-time analytical usability for clinical decision support and institutional auditing. Design/methodology/approach The Design Science Research methodology was employed in the design and validation of the proposed artefact. The solution is organised into three integrated layers: (1) Clinical Data, in which records are captured in the hospital information system and stored in Oracle as the authoritative transactional source, (2) Data Processing, supported by a Python-based ETL micro-batch pipeline that extracts, validates, and semantically transforms new or updated OpenEHR-structured records and (3) Data Analysis, with scalable NoSQL storage (MongoDB) serving as the analytical store, and a Flask-based presentation layer that provides interactive dashboards and multidimensional visualisations. Findings The architecture supported end-to-end semantic consistency, preserving data integrity through near-real-time micro-batch processing and providing useful analytical artefacts to support decision-making and auditing. The semantic middleware helped mitigate the effects of OpenEHR model versioning, while the combination of Python-based ETL processes with MongoDB favoured scalability and flexibility. Although evaluated within a single-site context, the findings suggest that the proposed architecture may be transferable to other institutional scenarios. Originality/value This work provides an integrated perspective combining semantic rigour, programmatic processing, and analytical capacity within a modular and scalable framework. It contributes to data-driven digital transformation in healthcare by reinforcing interoperability, near-real-time analytical refresh, and alignment between semantic models and analytical dimensions.