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利用标准的分子病理学序列数据模型信息管理计划:从测序仪到电子健康记录。

A Model Information Management Plan for Molecular Pathology Sequence Data Using Standards: From Sequencer to Electronic Health Record.

机构信息

Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska.

Department of Pathology, Children's Healthcare of Atlanta, Atlanta, Georgia.

出版信息

J Mol Diagn. 2019 May;21(3):408-417. doi: 10.1016/j.jmoldx.2018.12.002. Epub 2019 Feb 20.

Abstract

Incorporating genetic variant data into the electronic health record (EHR) in discrete computable fashion has vexed the informatics community for years. Genetic sequence test results are typically communicated by the molecular laboratory and stored in the EHR as textual documents. Although text documents are useful for human readability and initial use, they are not conducive for data retrieval and reuse. As a result, clinicians often struggle to find historical gene sequence results on a series of oncology patients within the EHR that might influence the care of the current patient. Second, identification of patients with specific mutation results in the EHR who are now eligible for new and/or changing therapy is not easily accomplished. Third, the molecular laboratory is challenged to monitor its sequencing processes for nonrandom process variation and other quality metrics. A novel approach to address each of these issues is presented and demonstrated. The authors use standard Health Level 7 laboratory result message formats in conjunction with international standards, Systematized Nomenclature of Medicine Clinical Terms and Human Genome Variant Society nomenclature, to represent, communicate, and store discrete gene sequence data within the EHR in a scalable fashion. This information management plan enables the support of the clinician at the point of care, enhances population management, and facilitates audits for maintaining laboratory quality.

摘要

将遗传变异数据以离散可计算的方式纳入电子健康记录 (EHR) 多年来一直困扰着信息学社区。遗传序列测试结果通常由分子实验室传达,并作为文本文件存储在 EHR 中。虽然文本文件对于人类的可读性和初始使用很有用,但它们不利于数据检索和重用。因此,临床医生经常难以在 EHR 中找到一系列肿瘤患者的历史基因序列结果,这些结果可能会影响当前患者的治疗。其次,在 EHR 中识别出具有特定突变结果的患者,这些患者现在有资格接受新的和/或改变的治疗,这并不容易实现。第三,分子实验室面临着监测其测序过程中非随机过程变异和其他质量指标的挑战。本文提出并展示了一种解决这些问题的新方法。作者使用标准的健康水平 7 实验室结果消息格式以及国际标准、系统命名法医学临床术语和人类基因组变异协会命名法,以可扩展的方式在 EHR 中表示、传达和存储离散的基因序列数据。该信息管理计划支持临床医生在护理点的工作,增强了人群管理,并为维护实验室质量提供了审计支持。

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UniProt: the universal protein knowledgebase.通用蛋白质知识库:UniProt
Nucleic Acids Res. 2018 Mar 16;46(5):2699. doi: 10.1093/nar/gky092.

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