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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7613542 | PMC |
| http://dx.doi.org/10.1016/S2589-7500(20)30247-8 | DOI Listing |
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In 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 PDFHistory, Implementation and Current Use of the IFCC-IUPAC's Nomenclature for Properties and Units (NPU) Terminology in Denmark, Norway and Sweden.
EJIFCC
October 2024
Department of Clinical Biochemistry, North Zealand Hospital, DK-3400 Hillerød, Denmark Equalis AB, SE-751 09 Uppsala, Sweden.
Electronic exchange of health care data demands code/terminology systems. In the Scandinavian countries, the IFCC-IUPAC's Nomenclature for Properties and Units (NPU) terminology is used for results in biochemistry, pharmacology, and immunology. Implementation, use and administration of NPU has differed between the countries despite similar health care and lab sectors.
View Article and Find Full Text PDFLaboratory Data as a Potential Source of Bias in Healthcare Artificial Intelligence and Machine Learning Models.
Ann Lab Med
January 2025
Department of Pathology, UT Southwestern Medical Center, Dallas, TX, USA.
Artificial intelligence (AI) and machine learning (ML) are anticipated to transform the practice of medicine. As one of the largest sources of digital data in healthcare, laboratory results can strongly influence AI and ML algorithms that require large sets of healthcare data for training. Embedded bias introduced into AI and ML models not only has disastrous consequences for quality of care but also may perpetuate and exacerbate health disparities.
View Article and Find Full Text PDFEnhancing the Functionalities of Personal Health Record Systems: Empirical Study Based on the HL7 Personal Health Record System Functional Model Release 1.
JMIR Med Inform
October 2024
Department of Medical Informatics, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan, 81 227177572, 81 227178022.
Background: The increasing demand for personal health record (PHR) systems is driven by individuals' desire to actively manage their health care. However, the limited functionality of current PHR systems has affected users' willingness to adopt them, leading to lower-than-expected usage rates. The HL7 (Health Level Seven) PHR System Functional Model (PHR-S FM) was proposed to address this issue, outlining all possible functionalities in PHR systems.
View Article and Find Full Text PDFClassifier Chains for LOINC Transcoding.
Stud Health Technol Inform
August 2024
Onaos, Montpellier, France.
Purpose: Mapping clinical observations and medical test results into the standardized vocabulary LOINC is a prerequisite for exchanging clinical data between health information systems and ensuring efficient interoperability.
Methods: We present a comparison of three approaches for LOINC transcoding applied to French data collected from real-world settings. These approaches include both a state-of-the-art language model approach and a classifier chains approach.
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