Interoperable health information exchange depends on adoption of terminology standards, but international use of such standards can be challenging because of language differences between local concept names and the standard terminology. To address this important barrier, we describe the evolution of an efficient process for constructing translations of LOINC terms names, the foreign language functions in RELMA, and the current state of translations in LOINC. We also present the development of the Italian translation to illustrate how translation is enabling adoption in international contexts. We built a tool that finds the unique list of LOINC Parts that make up a given set of LOINC terms. This list enables translation of smaller pieces like the core component "hepatitis c virus" separately from all the suffixes that could appear with it, such "Ab.IgG", "DNA", and "RNA". We built another tool that generates a translation of a full LOINC name from all of these atomic pieces. As of version 2.36 (June 2011), LOINC terms have been translated into nine languages from 15 linguistic variants other than its native English. The five largest linguistic variants have all used the Part-based translation mechanism. However, even with efficient tools and processes, translation of standard terminology is a complex undertaking. Two of the prominent linguistic challenges that translators have faced include: the approach to handling acronyms and abbreviations, and the differences in linguistic syntax (e.g. word order) between languages. LOINC's open and customizable approach has enabled many different groups to create translations that met their needs and matched their resources. Distributing the standard and its many language translations at no cost worldwide accelerates LOINC adoption globally, and is an important enabler of interoperable health information exchange.
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http://dx.doi.org/10.1016/j.jbi.2012.01.005 | DOI Listing |
Interact J Med Res
December 2024
Life Science Laboratories, KDDI research atelier, KDDI Research, Inc, Fujimino, Saitama, Japan.
Government policies in the United States and the European Union promote standardization and value creation in the use of FAIR (findability, accessibility, interoperability, and reusability) data, which can enhance trust in digital health systems and is crucial for their success. Trust is built through elements such as FAIR data access, interoperability, and improved communication, which are essential for fostering innovation in digital health technologies. This Viewpoint aims to report on exploratory research demonstrating the feasibility of testing a patient-centric data flow model facilitating semantic interoperability on precision medical information.
View Article and Find Full Text PDFIn 2023, the Korean Core Data for Interoperability (KR-CDI), comprising 77 elements, was established as a compliance item for healthcare data exchange in Korea to promote patient-centered medical information exchange and reestablish national interoperability in healthcare standardization. Radiologic examinations are in the core classification of diagnostic imaging tests, and the examination name, results, and image data must be exchanged based on standard codes of terminology and transfer. Accordingly, the Korean Society of Radiology has formed a standardization committee that maps radiologic examination names to international standard codes, such as LOINC and SNOMED CT.
View Article and Find Full Text PDFJ Surg Res
October 2024
Atlanta VA Health Care System, Surgical and Perioperative Care, Decatur, Georgia; Division of Surgical Oncology, Department of Surgery, Emory University School of Medicine, Atlanta, Georgia; Department of Surgery, Morehouse School of Medicine, Atlanta, Georgia.
Introduction: The Veterans Affairs Surgical Quality Improvement Program (VASQIP) trains surgical quality nurses (SQNs) at each Veterans Affairs (VA) hospital to extract or verify 187 variables from the medical record for all cardiac surgical cases. For ten preoperative laboratory values, VASQIP has a semiautomated (SA) system in which local lab values are automatically extracted, verified by SQNs, and lab values recorded at other VA facilities are manually extracted. The objective of this study was to develop and validate a method to automate the extraction of these ten preoperative laboratory values and compare results with the current SA method.
View Article and Find Full Text PDFAnn Lab Med
November 2024
Health O&T, Seoul, Korea.
Radiology
June 2024
From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Techie Maestro, Waterloo, Ontario, Canada (M.H.); Department of Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, Md (T.S.S.); and Canon Medical Research USA, Vernon Hills, Ill (K.P.O.).
The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations also increase the likelihood of unanticipated problems.
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