GuidelineAnnals of laboratory medicine2026
Essential Data Elements for Laboratory Test Interoperability: An Expert Consensus-Based Framework.
Guideline in Annals of laboratory medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
31 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing digitalization of healthcare necessitates laboratory data interoperability to ensure reliable clinical decision-making, efficient data exchange, and effective secondary use. Most Korean institutions use electronic systems for laboratory test reporting. However, the lack of standardized terminology, measurement units, and data structures has led to reporting inconsistencies and limited integration across institutions. We have developed a guideline using a consensus-based laboratory data framework for essential laboratory data elements that is aligned with international standards and guidelines. The laboratory data framework organizes essential laboratory data elements into five categories: Patient, Test, Specimen, Date/time, and Administrative data. Each element is also assigned to one or more usage three tiers: Data for Clinical Data Interchange, Data for Report, and Data for Laboratory. Mandatory and conditional elements are further differentiated, incorporating aspects of clinical relevance, regulatory requirements, and implementation feasibility. The laboratory data framework incorporates terminology standards, such as the Logical Observation Identifiers Names and Codes, Systematized Nomenclature of Medicine-Clinical Terms, and Unified Code for Units of Measure, supporting both semantic and syntactic interoperability. This approach enables improved data consistency, facilitates national health information exchange, and lays a foundation for advanced applications, such as clinical decision support and artificial intelligence-driven analytics.
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