Improving data quality in clinical research informatics tools

In this 2022 case study by AbuHalimeh, de-identified data quality dimensions (DDQD) in two real-life clinical systems are examined to better understand discrepancies and requirements for their correction in order to improve data quality for clinical researchers. After a brief introduction to the topic of clinical informatics and data quality, AbuHalimeh presents a case study using patient count data from a TranSMART Foundation i2b2 and Epic SlicerDicer system of “a healthcare organization that wanted to have the ability to ingest other sources of research-specific data.” After discussing the methodology and results, the author proposes a series of eight steps to improve data quality generated from de-identified system.” Those steps “together form guidelines for a methodology of manual and automated procedures and tools used to manage data quality and data governance in a multifaceted, diverse information environment such as healthcare organizations, as well as to enhance data quality among data housed in de-identified data systems,” the author concludes.

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