A basic model for assessing primary health care electronic medical record data quality
A basic model for assessing primary health care electronic medical record data quality
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DOI：
10.1186/s12911-019-0740-0
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发表时间：
2019-02-12
影响因子：
3.5
通讯作者：
Thind, Amardeep
中科院分区：
文献类型：
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作者：
Terry, Amanda L.;Stewart, Moira;Thind, Amardeep
工作背景：在加拿大初级卫生保健实践中，电子病历（EMR）的使用越来越多，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其应用的关系。在这里，我们提出了一个基本模型，用于评估初级卫生保健电子病历数据质量，包括一组数据质量的措施在四个领域。我们描述了开发和测试这套措施的过程，分享了在三个EMR衍生数据集中应用这些措施的结果，并讨论了这揭示了什么措施和EMR数据质量。该模型提供了一个起点，从数据用户可以完善自己的方法，根据自己的needs.Methods：使用迭代过程，EMR数据质量的措施创建在四个领域：可比性，完整性，正确性和货币。我们使用了一系列的过程步骤来制定措施。然后操作的措施，并在三个数据集内进行测试，从不同的EMR软件products.Results创建的一组11个最终措施。我们无法在一个数据集中计算多个指标的结果，因为数据是在特定的EMR中收集的。总体而言，我们发现变异的测试措施的结果（如敏感性值最高的糖尿病，最低的肥胖），数据集（如记录的高度），并按患者的年龄和性别（如记录的血压，身高和体重）。结论：本文提出了一个基本模型，用于评估初级卫生保健EMR数据质量。我们在四个领域内，在三个不同的EMR衍生的初级卫生保健数据集中开发和测试了多个数据质量指标。测试这些措施的结果表明，并非所有措施都可以用于所有数据集，并说明数据质量的变化。这是在创建一套标准的数据质量衡量标准方面向前迈出的一步。尽管如此，每个项目都有独特的挑战，因此在进行之前需要进行自己的数据质量评估。
Background: 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.Methods: 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.Results: 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).Conclusions: 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.