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电子健康记录研究中的数据质量:质量领域与评估方法

Data Quality in Electronic Health Records Research: Quality Domains and Assessment Methods.

作者信息

Feder Shelli L

机构信息

1 Yale University, West Haven, CT, USA.

出版信息

West J Nurs Res. 2018 May;40(5):753-766. doi: 10.1177/0193945916689084. Epub 2017 Jan 24.

Abstract

The proliferation of the electronic health record (EHR) has led to increasing interest and opportunities for nurse scientists to use EHR data in a variety of research designs. However, methodological problems pertaining to data quality may arise when EHR data are used for nonclinical purposes. Therefore, this article describes common domains of data quality and approaches for quality appraisal in EHR research. Common data quality domains include data accuracy, completeness, consistency, credibility, and timeliness. Approaches for quality appraisal include data validation with data rules, evaluation and verification of data abstraction methods with statistical measures, data comparisons with manual chart review, management of missing data using statistical methods, and data triangulation between multiple EHR databases. Quality data enhance the validity and reliability of research findings, form the basis for conclusions derived from the data, and are, thus, an integral component in EHR-based study design and implementation.

摘要

电子健康记录(EHR)的普及使得护士科学家对在各种研究设计中使用EHR数据的兴趣日益增加,机会也越来越多。然而,当EHR数据用于非临床目的时,可能会出现与数据质量相关的方法学问题。因此,本文描述了EHR研究中数据质量的常见领域以及质量评估方法。常见的数据质量领域包括数据准确性、完整性、一致性、可信度和及时性。质量评估方法包括使用数据规则进行数据验证、用统计方法评估和验证数据提取方法、与手工病历审查进行数据比较、使用统计方法管理缺失数据以及在多个EHR数据库之间进行数据三角测量。高质量的数据可提高研究结果的有效性和可靠性,构成从数据得出结论的基础,因此是基于EHR的研究设计和实施中不可或缺的组成部分。

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