Hospital inpatient care relies on constant monitoring and reliable real-time data. Continuous improvement, adaptability, and state-of-the-art technologies are critical for ongoing efficiency, productivity, and readiness growth. When appropriately used, technologies, such as blockchain and IoT-enabled devices, can change the practice of medicine and ensure that it is performed based on correct assumptions and reliable data. The proposed electronic health record (EHR) can obtain context information from beacons, change the user interface of medical devices according to their location, and provide a more user-friendly interface for medical devices. The data generated, which are associated with the location of the beacons and devices, were stored in Hyperledger Fabric, a permissioned distributed ledger technology. Overall, by prompting and adjusting the user interface to context- and location-specific information while ensuring the immutability and value of the data, this solution targets a decrease in medical errors and an increase in the efficiency in healthcare inpatient care by improving user experience and ease of access to data for health professionals. Moreover, given auditing, accountability, and governance needs, it must ensure when, if, and by whom the data are accessed.

The application of blockchain technology in the health and healthcare sector is considered disruptive, thus requiring trust, integrity, and value of data. In addition, this document analyses the potential of permissioned blockchain, more specifically Hyperledger Fabric, to improve the security, integrity, and efficiency of healthcare systems. It examines ways in which this can be done in a manner that has both scalability and sustainability prospects in the long-term. This, by reaching the optimal compromise between efficiency of resources and its ease of use, management and upscaling. The ability to be as lightweight as possible, while being able to quickly expand the infrastructure and seamlessly integrate new functionalities, maintaining operational efficiency. The research developed involves a review of the literature, the development of three different blockchain implementations/architectures where the practical assessment of performance metrics is performed. Finally, a blockchain management platform is presented. This was developed to ensure long-term usability and maintenance of blockchain solutions in the healthcare industry. This work aims to advance the application of blockchain in healthcare, addressing both immediate and long-term needs for security and efficiency.

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.