Secondary EMR data for quality improvement and research: A comparison of manual and electronic data collection from an integrated critical care electronic medical record system
Secondary EMR data for quality improvement and research: A comparison of manual and electronic data collection from an integrated critical care electronic medical record system
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DOI：
10.1016/j.jcrc.2018.07.021
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发表时间：
2018-10-01
影响因子：
3.7
通讯作者：
Stelfox, Henry T.
中科院分区：
文献类型：
--
作者：
Brundin-Mather, Rebecca;Soo, Andrea;Stelfox, Henry T.
目的：本研究测量的质量，从临床信息系统中提取的数据广泛用于重症监护质量的改善和research.Materials和方法：我们从30个领域的随机样本中抽取的207例患者入住9个成人，医疗外科重症监护病房的数据。我们评估了收集的数据之间的一致性：（1）由经过培训的稽查员手动从床边系统（eCritical MetaVision）收集的数据，以及（2）从系统数据仓库（eCritical TRACER）收集的电子数据。协议进行了评估，使用科恩的Kappa分类变量和组内相关系数（ICC）连续variable.Results：数据集之间的一致性是优秀的。11/30个变量（35%）完全一致。16个分类变量的中位Kappa评分为0.99（IQR 0.92-1.00）。APACHE II的ICC为0.936（0.898-0.960）。观察到SOFA肾脏和呼吸组分的一致性最低（ICC分别为0.804和0.846）。评分翻译错误的人工审核员是最常见的来源的data discrimination.Conclusions：人工验证过程中的电子数据是复杂的验证传统的临床文件。这项研究代表了一种直接的方法来验证数据存储库的使用，以支持可靠和有效地使用高质量的二次利用数据。(C)2018爱思唯尔公司All rights reserved.
Purpose: This study measured the quality of data extracted from a clinical information system widely used for critical care quality improvement and research.Materials and methods: We abstracted data from 30 fields in a random sample of 207 patients admitted to nine adult, medical-surgical intensive care units. We assessed concordance between data collected: (1) manually from the bedside system (eCritical MetaVision) by trained auditors, and (2) electronically from the system data warehouse (eCritical TRACER). Agreement was assessed using Cohen's Kappa for categorical variables and intraclass correlation coefficient (ICC) for continuous variables.Results: Concordance between data sets was excellent. There was perfect agreement for 11/30 variables (35%). The median Kappa score for the 16 categorical variables was 0.99 (IQR 0.92-1.00). APACHE II had an ICC of 0.936 (0.898-0.960). The lowest concordance was observed for SOFA renal and respiratory components (ICC 0.804 and 0.846, respectively). Score translation errors by the manual auditor were the most common source of data discrepancies.Conclusions: Manual validation processes of electronic data are complex in comparison to validation of traditional clinical documentation. This study represents a straightforward approach to validate the use of data repositories to support reliable and efficient use of high quality secondary use data. (C) 2018 Elsevier Inc. All rights reserved.