Supporting interoperability of genetic data with LOINC

Author:

Deckard Jamalynne1,McDonald Clement J2,Vreeman Daniel J13

Affiliation:

1. Regenstrief Institute, Inc, Indianapolis, Indiana, USA

2. Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA

3. Indiana University School of Medicine, Indianapolis, Indiana, USA

Abstract

Abstract Electronic reporting of genetic testing results is increasing, but they are often represented in diverse formats and naming conventions. Logical Observation Identifiers Names and Codes (LOINC) is a vocabulary standard that provides universal identifiers for laboratory tests and clinical observations. In genetics, LOINC provides codes to improve interoperability in the midst of reporting style transition, including codes for cytogenetic or mutation analysis tests, specific chromosomal alteration or mutation testing, and fully structured discrete genetic test reporting. LOINC terms follow the recommendations and nomenclature of other standards such as the Human Genome Organization Gene Nomenclature Committee’s terminology for gene names. In addition to the narrative text they report now, we recommend that laboratories always report as discrete variables chromosome analysis results, genetic variation(s) found, and genetic variation(s) tested for. By adopting and implementing data standards like LOINC, information systems can help care providers and researchers unlock the potential of genetic information for delivering more personalized care.

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

Reference38 articles.

1. Integration of genomics into the electronic health record: mapping terra incognita;Kannry;Genet Med,2013

2. Charting a course for genomic medicine from base pairs to bedside;Green;Nature,2011

3. Practical challenges in integrating genomic data into the electronic health record;Kho;Genet Med,2013

4. Emerging landscape of genomics in the Electronic Health Record for personalized medicine;Ullman-Cullere;Hum Mutat,2011

5. Clinical lab-test results;US Department of Health and Human Services, Office of the National Coordinator for Health Information Technology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3