Development of a data entry auditing protocol and quality assurance for a tissue bank database
- 380 Downloads
Human transcription error is an acknowledged risk when extracting information from paper records for entry into a database. For a tissue bank, it is critical that accurate data are provided to researchers with approved access to tissue bank material. The challenges of tissue bank data collection include manual extraction of data from complex medical reports that are accessed from a number of sources and that differ in style and layout. As a quality assurance measure, the Breast Cancer Tissue Bank (http:\\www.abctb.org.au) has implemented an auditing protocol and in order to efficiently execute the process, has developed an open source database plug-in tool (eAuditor) to assist in auditing of data held in our tissue bank database. Using eAuditor, we have identified that human entry errors range from 0.01% when entering donor’s clinical follow-up details, to 0.53% when entering pathological details, highlighting the importance of an audit protocol tool such as eAuditor in a tissue bank database. eAuditor was developed and tested on the Caisis open source clinical-research database; however, it can be integrated in other databases where similar functionality is required.
KeywordseAuditor Caisis Quality control protocol Auditing BCTB
The authors would like to thank Dr. Dinny Graham for her helpful feedback. The Breast Cancer Tissue Bank is funded by the National Health and Medical Research Council of Australia, the National Breast Cancer Foundation and the Cancer Institute NSW (CINSW). RLB is a CINSW Fellow.
- Carpenter JE, Miller JA et al (2007) The Caisis system for biorepository data requirements—Breast cancer tissue bank, Australia. Cell Preserv Technol 5(1):51–52Google Scholar
- Fearn P, Regan K et al (2007) Lessons learned from Caisis: an open source, web-based system for integrating clinical practice and research. Computer-based medical systems, 2007. CBMS ‘07. In: Twentieth IEEE international symposium on 2007Google Scholar
- Goldberg SI, Niemierko A et al (2008) Analysis of data errors in clinical research databases. AMIA Annu Symp Proc 2008:242–246Google Scholar