OpenEHR Conference Demonstrates Advances in Clinical Data Interoperability and Artificial Intelligence
By Google News AI ·
A scientific supplement published in BMC Proceedings compiles thirteen peer-reviewed abstracts accepted to the scientific track of the EHRCON25 openEHR International Conference, held in Barcelona in October 2025.
Across short papers and poster presentations, the collection documents technical innovations and empirical evaluations focused on interoperable, clinically meaningful, and sustainable health information architectures.
The presented proceedings highlight key developments across health informatics, examining how openEHR archetypes, semantic data modeling, artificial intelligence integration, and cross-standard conversions function in operational environments.
Rather than relying on isolated proprietary database systems, the research evaluates how archetype-based architectures can structure clinical narratives, modernize legacy hospital systems, harmonize multi-center research registries, and facilitate automated querying.
The published contributions represent collaborative initiatives undertaken across academic medical centers, regional health authorities, public health bodies, and software development teams advancing standardized health information systems.To bridge the divide between unstructured clinical text and formal archetype models, researchers from Better Ltd created a Retrieval-Augmented Generation pipeline for
narrative-to-structure semantic binding.
While openEHR archetypes provide a rich semantic foundation for structured data, routine clinical documentation begins with free-text narratives where tools such as the Clinical Knowledge Manager Resource Finder struggle with medical abbreviations, synonyms, and clinical phrasing.
The proposed architecture combined clinical entity extraction with hybrid lexical and semantic retrieval over an attribute-weighted index of archetypes sourced from the Clinical Knowledge Manager. Archetypes were decomposed into granular semantic units and indexed using BM25 keyword search alongside neural vector embeddings, applying attribute weighting to prioritize clinically meaningful metadata.
Evaluated on 800 narrative clinical queries mapped to gold-standard archetypes, the pipeline achieved an archetype recall of 0.73, notably outperforming the Clinical Knowledge Manager Resource Finder at 0.53 and zero-shot GPT-4.1 prompting at 0.44.In a complementary investigation into clinical query interfaces, researchers developed Text2AQL, an artificial intelligence assistant designed to translate natural language
inquiries into executable Archetype Query Language statements.
Formulating Archetype Query Language commands requires precise familiarity with specialized syntax and domain information structures, presenting an ongoing hurdle for healthcare practitioners seeking direct database access.
Text2AQL addresses this complexity through a multi-stage workflow: clinical concepts are extracted from user inputs, mapped to archetypes and data element paths via Retrieval-Augmented Generation over vector indexes, and assembled into queries by a large language model. An integrated validation step identifies and corrects syntax errors such as malformed clauses or non-existent paths.
Evaluated on 1,274 natural language descriptions paired with ground-truth queries, Text2AQL generated syntactically valid statements in 81.9 percent of tests, exceeding general models such as GPT-4o at 73.6 percent and GPT-5 mini at 46.2 percent, ensuring more dependable real-world execution.Bridging openEHR repositories with…
Originally published by Google News AI
