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Semantics-driven improvements in electronic health records data quality: a systematic review
Yirong Wu1, Mudan Ren2, Na Chen2
1Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, Zhuhai, 519087, China.
BMC Medical Informatics and Decision Making
|August 12, 2025
Summary
Semantic technologies can significantly improve electronic health record (EHR) data quality across various applications. Six key techniques, including ontologies and knowledge graphs, enhance EHR data quality beyond just interoperability.
Area of Science:
- Health Informatics
- Data Science
- Semantic Web Technologies
Background:
- Electronic health record (EHR) data quality is critical for health informatization but remains a challenge.
- Semantic technologies offer a promising approach to enhance EHR data quality.
- Previous research often focused narrowly on specific technologies or applications like interoperability.
Purpose of the Study:
- To systematically review the potential of diverse semantic technologies for improving EHR data quality.
- To explore a broader spectrum of application scenarios for these technologies.
Main Methods:
- Systematic review following PRISMA guidelines.
- Searched PubMed, IEEE Xplore, and Web of Science Core Collection.
- Used keywords like "Semantic*", "Quality", "Electronic Health Record*", "EHR*", "Electronic Medical Record*", and "EMR*".
Main Results:
- Analyzed 37 papers published between 2008 and 2024.
- Identified six key semantic techniques: EHR standardization, controlled vocabulary, ontology, semantic web, knowledge graph, and NLP.
- Mapped these techniques to 16 data quality indicators and FAIR principles, showing contributions to technical and governance aspects.
Conclusions:
- Six semantic technologies (foundational, general, advanced) can enhance EHR data quality.
- These technologies improve conformance, portability, usability, and applicability.
- They are suitable for diverse contexts beyond interoperability, supporting FAIR data management and reuse.
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