QAIT: A quality assurance issue tracking tool to facilitate the improvement of clinical data quality

Yonghong Zhang, Weihong Sun, Emily M. Gutchell, Leonid Kvecher, Joni Kohr, Anthony Bekhash, Craig D. Shriver, Michael N. Liebman, Richard J. Mural, Hai Hu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In clinical and translational research as well as clinical trial projects, clinical data collection is prone to errors such as missing data, and misinterpretation or inconsistency of the data. A good quality assurance (QA) program can resolve many such errors though this requires efficient communications between the QA staff and data collectors. Managing such communications is critical to resolving QA problems but imposes a major challenge for a project involving multiple clinical and data processing sites. We have developed a QA issue tracking (QAIT) system to support clinical data QA in the Clinical Breast Care Project (CBCP). This web-based application provides centralized management of QA issues with role-based access privileges. It has greatly facilitated the QA process and enhanced the overall quality of the CBCP clinical data. As a stand-alone system, QAIT can supplement any other clinical data management systems and can be adapted to support other projects.

Original languageEnglish
Pages (from-to)86-91
Number of pages6
JournalComputer Methods and Programs in Biomedicine
Issue number1
StatePublished - Jan 2013
Externally publishedYes


  • Clinical data
  • Data acquisition
  • Data management
  • Issue tracking
  • Quality assurance


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