期刊
MEDICAL CARE
卷 51, 期 8, 页码 S22-S29出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MLR.0b013e31829b1e2c
关键词
comparative effectiveness research; distributed research network; data quality
类别
资金
- Agency for Healthcare Research and Quality [1R01HS019912-01, R01HS019908]
- NIH/NCATS Colorado CTSI [TR000154]
Background: Electronic health information routinely collected during health care delivery and reimbursement can help address the need for evidence about the real-world effectiveness, safety, and quality of medical care. Often, distributed networks that combine information from multiple sources are needed to generate this real-world evidence. Objective: We provide a set of field-tested best practices and a set of recommendations for data quality checking for comparative effectiveness research (CER) in distributed data networks. Methods: Explore the requirements for data quality checking and describe data quality approaches undertaken by several existing multi-site networks. Results: There are no established standards regarding how to evaluate the quality of electronic health data for CER within distributed networks. Data checks of increasing complexity are often used, ranging from consistency with syntactic rules to evaluation of semantics and consistency within and across sites. Temporal trends within and across sites are widely used, as are checks of each data refresh or update. Rates of specific events and exposures by age group, sex, and month are also common. Discussion: Secondary use of electronic health data for CER holds promise but is complex, especially in distributed data networks that incorporate periodic data refreshes. The viability of a learning health system is dependent on a robust understanding of the quality, validity, and optimal secondary uses of routinely collected electronic health data within distributed health data networks. Robust data quality checking can strengthen confidence in findings based on distributed data network.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据