Advancing Methods to Measure and Improve the Quality of Large Scale Health Data

 描述(申请人提供)：临床和公共卫生既是生物医学科学，也是信息科学，数据是它们的命脉。几乎所有临床和公共卫生领域的无数活动(或用例)(例如，患者护理、监测、社区健康评估、政策)都涉及生成、收集、存储、分析或共享有关单个患者或群体的数据。21世纪有效的临床和公共卫生实践需要从越来越多的信息系统获取数据，包括但不限于电子健康记录。然而，在监测和政策等一些用例中，电子健康记录系统中的数据质量已被证明是糟糕的或“不适合使用”。此外，衡量信息系统中数据质量的方法还处于萌芽阶段。这为制定评估和改进电子系统数据质量的改进方法提供了机会。在拟议的项目中，我们将使用卫生数据管理框架来指导在大型观察性卫生数据集中衡量数据质量的方法的开发和测试。具体地说，我们将1)扩展大规模纵向证据系统(Achilles)软件的卫生信息自动化表征，以衡量从不同的信息系统向公共卫生机构报告的电子数据的质量，以用于疾病监测；以及2)应用Achilles扩展来探索从多个真实世界的卫生系统、医院、实验室和诊所捕获的数据的质量。我们将进一步向公共卫生专业人员演示扩展的软件，收集有关方法和软件工具的反馈，以支持公共卫生机构定期监测用于监测疾病流行和负担的数据的质量。此外，由于Achilles软件是开源的，并得到多个医疗系统的支持，我们的工作可能适用于其他组织，这些组织寻求表征其他用例的大规模观察数据集的质量，包括比较有效性研究(CER)、以患者为中心的结果研究(PCOR)和药物流行病学。

 DESCRIPTION (provided by applicant): Clinical and public health are as much information sciences as they are biomedical sciences, and data are their lifeblood. Nearly all of the myriad activities (or use cases) in clinical and public health (e.g., patient care, surveillance, communit health assessment, policy) involve generating, collecting, storing, analyzing, or sharing data about individual patients or populations. Effective clinical and public health practice in the twenty-first century requires access to data from an increasing array of information systems, including but not limited to electronic health records. However, the quality of data in electronic health record systems has been shown to be poor or "unfit for use" across a number of use cases like surveillance and policy. In addition, methods for measuring the quality of data in information systems are nascent. This presents an opportunity for the development of improved methods for assessing and improving the quality of data in electronic systems. In the proposed project, we will use a Health Data Stewardship framework to guide the development and testing of methods that measure data quality in large observational health data sets. Specifically, we will 1) extend the Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems (ACHILLES) software to measure the quality of data electronically reported from disparate information systems to public health agencies for disease surveillance; and 2) apply the ACHILLES extensions to explore the quality of data captured from multiple real-world health systems, hospitals, laboratories, and clinics. We will further demonstrate the extended software to public health professionals, gathering feedback on the ability of the methods and software tool to support public health agencies' efforts to routinely monitor the quality of data received for surveillance of disease prevalence and burden. Furthermore, because the ACHILLES software is available as open-source and supported by multiple health systems, our work may be applicable to other organizations that seek to characterize the quality of large scale observational data sets for other use cases including comparative effectiveness research (CER), patient centered outcomes research (PCOR), and pharmacoepidemiology.