Identifying, Investigating, and Classifying Data Errors: an Analysis of Clinical Research Data Quality from an Observational HIV Research Network in Latin America and the Caribbean

Abstract

Observational studies of health conditions and outcomes often combine clinical care data from many sites without explicitly assessing the accuracy and completeness of these data. Data auditing is an expensive but effective method of determining the quality of research data. We conducted a series of source verification data audits to determine the accuracy and completeness of data submitted to CCASAnet, an international, multicenter observational research network for HIV epidemiology. Our findings motivated investigations of sources of error, relevant dimensions of data quality, and the applicability of computer-based audit tools. This work underscores the importance of data quality assessment for collaborative research networks, especially those in international settings. Investigators should consider implementing an audit program to evaluate the reliability of different data sources and correct discrepancies in data that have already been collected. Such audits can be made more efficient by using computerized tools and flexible error metrics.