Kahn Michael G, Brown Jeffrey S, Chun Alein T, Davidson Bruce N, Meeker Daniella, Ryan Patrick B, Schilling Lisa M, Weiskopf Nicole G, Williams Andrew E, Zozus Meredith Nahm
University of Colorado.
Harvard Pilgrim Health Care Institute ; Harvard Medical School.
EGEMS (Wash DC). 2015 Mar 23;3(1):1052. doi: 10.13063/2327-9214.1052. eCollection 2015.
Poor data quality can be a serious threat to the validity and generalizability of clinical research findings. The growing availability of electronic administrative and clinical data is accompanied by a growing concern about the quality of these data for observational research and other analytic purposes. Currently, there are no widely accepted guidelines for reporting quality results that would enable investigators and consumers to independently determine if a data source is fit for use to support analytic inferences and reliable evidence generation.
We developed a conceptual model that captures the flow of data from data originator across successive data stewards and finally to the data consumer. This "data lifecycle" model illustrates how data quality issues can result in data being returned back to previous data custodians. We highlight the potential risks of poor data quality on clinical practice and research results. Because of the need to ensure transparent reporting of a data quality issues, we created a unifying data-quality reporting framework and a complementary set of 20 data-quality reporting recommendations for studies that use observational clinical and administrative data for secondary data analysis. We obtained stakeholder input on the perceived value of each recommendation by soliciting public comments via two face-to-face meetings of informatics and comparative-effectiveness investigators, through multiple public webinars targeted to the health services research community, and with an open access online wiki.
Our recommendations propose reporting on both general and analysis-specific data quality features. The goals of these recommendations are to improve the reporting of data quality measures for studies that use observational clinical and administrative data, to ensure transparency and consistency in computing data quality measures, and to facilitate best practices and trust in the new clinical discoveries based on secondary use of observational data.
数据质量差可能严重威胁临床研究结果的有效性和可推广性。随着电子管理和临床数据的可得性不断提高，人们越来越关注这些数据用于观察性研究和其他分析目的时的质量。目前，尚无广泛接受的报告质量结果的指南，无法使研究者和数据使用者独立确定数据源是否适合用于支持分析推断和生成可靠证据。
我们开发了一个概念模型，该模型描述了数据从数据创建者经过连续的数据管理者最终流向数据使用者的过程。这个“数据生命周期”模型说明了数据质量问题如何导致数据返回给先前的数据保管者。我们强调了数据质量差对临床实践和研究结果的潜在风险。由于需要确保对数据质量问题进行透明报告，我们创建了一个统一的数据质量报告框架以及一套针对使用观察性临床和管理数据进行二次数据分析的研究的20条补充数据质量报告建议。我们通过信息学和比较效果研究者的两次面对面会议、面向卫生服务研究界的多次公开网络研讨会以及一个开放获取的在线维基征求公众意见，从而获得利益相关者对每条建议感知价值的看法。
我们的建议提议报告一般和特定分析的数据质量特征。这些建议的目标是改进对使用观察性临床和管理数据的研究的数据质量测量报告，确保计算数据质量测量时的透明度和一致性，并促进基于观察性数据二次使用的新临床发现的最佳实践和信任。
