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Introduction: Ecosystem-centered healthcare innovations, such as digital health platforms, patient-centric records, and mobile health applications, depend on the semantic interoperability of health data. This ensures efficient, patient-focused healthcare delivery in a mobile world where citizens frequently travel for work and leisure. Beyond healthcare delivery, semantic interoperability is crucial for secondary health data use. This paper introduces a tool and techniques for achieving health data semantic interoperability, using reusable visual transformation components to create and validate transformation rules and maps, making them usable for domain experts with minimal technical skills., Methods: The tool and techniques for health data semantic interoperability have been developed and validated using Design Science, a common methodology for developing software artifacts, including tools and techniques., Results: Our tool and techniques are designed to facilitate the interoperability of Electronic Health Records (EHRs) by enabling the seamless unification of various health data formats in real time, without the need for extensive physical data migrations. These tools simplify complex health data transformations, allowing domain experts to specify and validate intricate data transformation rules and maps. The need for such a solution arises from the ongoing transition of the Estonian National Health Information System (ENHIS) from Clinical Document Architecture (CDA) to Fast Healthcare Interoperability Resources (FHIR), but it is general enough to be used for other data transformation needs, including the European Health Data Space (EHDS) ecosystem., Conclusion: The proposed tool and techniques simplify health data transformation by allowing domain experts to specify and validate the necessary data transformation rules and maps. Evaluation by ENHIS domain experts demonstrated the usability, effectiveness, and business value of the tool and techniques., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Bossenko, Randmaa, Piho and Ross.)

The necessity for robust, enduring, and relevant healthcare interoperability is universal across all clinical domains. However, we identified a gap in the availability of open-source, no-cost, high-quality tools that offer multilingual support and an advanced graphical interface. To address this, we developed TermX, an open-source platform to harmonise terminology and support interoperability between healthcare institutions and systems. TermX incorporates a terminology server, a Wiki, a model designer, a transformation editor, and tools for authoring and publishing. TermX is designed to develop terminology and implementation guides for healthcare systems at both the national and regional levels. It aims to ensure open, standardised access to published data and guarantee semantic interoperability based on the FHIR standard.

This article addresses critical health data integrity by proposing an HF (Hyperledger Fabric)-based architecture with integration into the global health data architecture based on distributed content-addressable storage networks.

Introduction: This paper addresses the dilemmas of accessibility, comprehensiveness, and ownership related to health data. To resolve these dilemmas, we propose and justify a novel, globally scalable reference architecture for a Personal Health Data Space (PHDS). This architecture leverages decentralized content-addressable storage (DCAS) networks, ensuring that the data subject retains complete control and ownership of their personal health data. In today's globalized world, where people are increasingly mobile for work and leisure, healthcare is transitioning from episodic symptom-based treatment toward continuity of care. The main aims of this are patient engagement, illness prevention, and active and healthy longevity. This shift, along with the secondary use of health data for societal benefit, has intensified the challenges associated with health data accessibility, comprehensiveness, and ownership., Method: The study is structured around four health data use case scenarios from the Estonian National Health Information System (EHIS): primary medical use, medical emergency use, secondary use, and personal use. We analyze these use cases from the perspectives of accessibility, comprehensiveness, and ownership. Additionally, we examine the security, privacy, and interoperability aspects of health data., Results: The proposed architectural solution allows individuals to consolidate all their health data into a unified Personal Health Record (PHR). This data can come from various healthcare institutions, mobile applications, medical devices for home use, and personal health notes., Discussions: The comprehensive PHR can then be shared with healthcare providers in a semantically interoperable manner, regardless of their location or the information systems they use. Furthermore, individuals maintain the autonomy to share, sell, or donate their anonymous or pseudonymous health data for secondary use with different systems worldwide. The proposed reference architecture aligns with the principles of the European Health Data Space (EHDS) initiative, enhancing health data management by providing a secure, cost-effective, and sustainable solution., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Klementi, Piho and Ross.)

A blockchain and decentralized storage technology-based architecture is proposed to support the integrity of Electronic Health Records (EHRs).

Written text has been the preferred medium for storing health data ever since Hippocrates, and the medical narrative is what enables a humanized clinical relationship. Can't we admit natural language as a user-accepted technology that has stood against the test of time? We have previously presented a controlled natural language as a human-computer interface for semantic data capture already at the point of care. Our computable language was driven by a linguistic interpretation of the conceptual model of the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). This paper presents an extension that allows the capture of measurement results with numerical values and units. We discuss the relation our method can have with emerging clinical information modelling.

On the basis of the Estonian National Health System, we propose a universal and reusable methodology for migrating HL7 CDA documents or their components.