Methods of Information in Medicine

Abstract

Background Multisite research networks such as the project "Collaboration on Rare Diseases" connect various hospitals to obtain sufficient data for clinical research.However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems.High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such distributed data.Objectives The aim of this work is the development of an interoperable methodology for assessing the quality of data recorded in heterogeneous sources to improve the quality of rare disease (RD) documentation and support clinical research.Methods We first developed a conceptual framework for DQ assessment.Using this theoretical guidance, we implemented a software framework that provides appropriate tools for calculating DQ metrics and for generating local as well as crossinstitutional reports.We further applied our methodology on synthetic data distributed across multiple hospitals using Personal Health Train.Finally, we used precision and recall as metrics to validate our implementation.Results Four DQ dimensions were defined and represented as disjunct ontological categories.Based on these top dimensions, 9 DQ concepts, 10 DQ indicators, and 25 DQ parameters were developed and applied to different data sets.Randomly introduced DQ issues were all identified and reported automatically.The generated reports show the resulting DQ indicators and detected DQ issues.