SynthesisBMC medical informatics and decision making2025
Semantics-driven improvements in electronic health records data quality: a systematic review.
Synthesis in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- From interface to outcome: a 4I framework for AI-linked functionality in electronic health records.BMC medical informatics and decision making · 2026Pooled it
- Semantics-driven improvements in electronic health records data quality: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Incompleteness of Electronic Health Records: An Impending Process Problem Within Healthcare.Healthcare (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundData quality (DQ) of electronic health record (EHR) is crucial for the advancement of health informatization, yet it remains a significant challenge. Scholars are showing a growing interest in leveraging semantic technologies to enhance EHR data quality. However, previous studies have focused predominantly on specific semantic technologies, scenarios, or objectives-such as interoperability-often overlooking the potential of a various semantic technologies across different scenarios.
objectiveThis systematic review aimed to explore the potential of employing a range of semantic technologies to improve EHR data quality in a broader spectrum of application scenarios.
methodsOur systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Three databases were searched, including PubMed, IEEE Xplore, and Web of Science Core Collection. The search terms used included "Semantic*", "Quality", "Electronic Health Record*", "EHR*", "Electronic Medical Record*", and "EMR*". These terms were combined via various Boolean operators to formulate multiple search queries.
resultsThirty-seven papers that met the inclusion criteria between 2008 and 2024 were analyzed. Six semantic techniques were identified as instrumental in improving EHR DQ: EHR standardization, controlled vocabulary, ontology, semantic web, knowledge graph, and natural language processing (NLP). These technologies were further mapped to 16 core data quality indicators and the FAIR principles (Findable, Accessible, Interoperable, and Reusable), highlighting their contributions across both technical and governance dimensions.
conclusionsThe six identified semantic technologies can be categorized into three levels: foundational, general, and advanced. These technologies show significant potential in enhancing EHR DQ, particularly in the areas of conformance, portability, usability, and applicability, and they are suitable for a variety of contexs beyond interoperability, aligning with FAIR-aligned best practices in data management and reuse.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.