Department of Family Medicine, Department of Epidemiology & Biostatistics, Schulich Interfaculty Program in Public Health, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.
Department of Family Medicine, Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada.
BMC Med Inform Decis Mak. 2019 Feb 12;19(1):30. doi: 10.1186/s12911-019-0740-0.
The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs.
Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products.
A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight).
This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.
加拿大初级医疗保健实践中电子病历（EMR）的使用增加，导致 EMR 数据的可用性扩大。这些数据的潜在用户需要了解其质量与应用的用途有关。在此，我们提出了一种评估初级保健 EMR 数据质量的基本模型，该模型由四个领域中的一组数据质量措施组成。我们描述了开发和测试此套措施的过程，分享了在三个 EMR 衍生数据集应用这些措施的结果，并讨论了这些措施和 EMR 数据质量的情况。该模型旨在为数据用户提供一个起点，以便根据自己的需求，对自己的方法进行改进。
我们使用迭代过程，在四个领域内创建 EMR 数据质量的度量标准：可比性；完整性；正确性；和时效性。我们使用了一系列过程步骤来开发这些度量标准。然后，将这些措施在三个不同 EMR 软件产品创建的数据集内进行了实施和测试。
创建了一套 11 个最终措施。由于特定 EMR 中数据收集的方式，我们无法在一个数据集内计算出几个措施的结果。总体而言，我们发现测试措施的结果存在差异（例如，糖尿病的敏感性值最高，肥胖的敏感性值最低），数据集之间（例如，身高的记录）以及患者年龄和性别之间（例如，血压，身高和体重的记录）存在差异。
本文提出了一种评估初级保健 EMR 数据质量的基本模型。我们在三个不同的 EMR 衍生初级保健数据集中，在四个领域内开发并测试了多个数据质量措施。测试这些措施的结果表明，并非所有措施都可以在所有数据集中使用，并且说明了数据质量的可变性。这是朝着创建一套标准的数据质量度量标准迈出的一步。尽管如此，每个项目都有其独特的挑战，因此在继续之前，都需要对其数据质量进行评估。
