openEHR - From data silos to interoperability

As part of a literature search, sources from recognised scientific databases were used and selected from subject-specific online sources to ensure a comprehensive perspective. Publications relating to the implementation and impact of openEHR in clinical settings were considered.

The literature was analysed through targeted searches using relevant keywords, including: openEHR, interoperability, health IT, electronic health records and healthcare systems. The aim was to gain sound insights into the practical implementation and challenges of openEHR in order to derive practical recommendations.

The implementation of openEHR as a data representation model has a number of advantages. The separation of data and applications (reference model, archetypes, templates) creates a long-term, sustainable architecture that promotes innovation and enables the efficient use of medical data. Improved interoperability ensures that healthcare data can be exchanged between different systems without time-consuming manual adjustments (Allen, 2022). The reuse of clinical models reduces the development effort for new applications and increases the consistency of medical data. At the same time, long-term data storage is ensured, allowing patient information to be kept available for decades without data loss or compatibility problems (Better, 2024).

However, there are also certain challenges that need to be mentioned. The standard is still relatively new and focuses on the clinical data repository of an organisation such as a hospital. For non-medical data, other standards and representations are potentially better suited. OpenEHR is not yet fully supported in many HIS or EHR systems (openEHR.org, 2025). The introduction of openEHR requires significant investment in training, technology and organisational adjustments. The migration of existing data formats to an openEHR environment can be complex and resource-intensive. In addition, semantic interoperability remains a challenge, as different medical terms and classifications need to be translated between different systems (Dagliati et al., 2021; Raghupathi & Raghupathi, 2014). There are repeated discussions as to whether HL 7 FHIR, which is a data exchange format, would also be suitable for storing the data. This always depends on the use case and the scope of the storage. But as a rule of thumb, OpenEHR can be seen as a data representation and storage format and FHIR as a data exchange format with mapping options (Pedrera-Jiménez et al., 2022).

Several factors are crucial to ensure the successful implementation of openEHR:

• Technology- and system-independent information architecture of the organisation: Design a comprehensive information and data architecture for your company for the most important data entities and their relationships. This forms the basis for all further decisions on which data standards you want to rely on, which IT systems you want to map and how the future IT landscape needs to be further developed.
• Establish governance structures: A clear set of rules for managing clinical data models is essential to ensure consistency and quality in the long term (Leslie, 2020).
• Training for professionals: Healthcare professionals and IT staff should be fully trained in the basics of openEHR and the associated modelling tools (Oliveira et al., 2021).
• Iterative adoption: Implementation should be gradual, starting with pilot projects to gain experience and make adjustments early (Allen, 2022)
• Use interoperability standards: Combining openEHR with established standards such as HL7 FHIR and OMOP can facilitate integration and ensure data compatibility (Delussu et al., 2024).
• Utilise experience from existing projects: Collaborating with institutions that already have experience with openEHR can make the implementation process much easier (Better, 2024).

Only a systematic and structured approach can ensure that the benefits of openEHR are fully utilised and that a future-proof, efficient data infrastructure is created.