Indicators of data quality: revision of a guideline for networked medical research

Jürgen Stausberg, Ron Pritzkuleit, Carsten O Schmidt, Thomas Schrader, Michael Nonnemacher


Data quality significantly impacts the reliability and validity of empirical medical research. Specific measures can be used to check the quality of data during operation of a research project like a register. Furthermore these indicators allow an assessment of data quality independently from the institution responsible for data recording. A previously developed set of 24 data quality indicators was compared with measures of three research projects, each representing a specific view on the topic. The structure of the set was confirmed, being able to capture most of the projects' measures under the headings plausibility, organization, and correctness. Solely two indicators about metadata could not be appropriately mapped. However, several measures not considered so far were added to reach a number of 51 quality indicators in a first draft of a revised set. Most of the new indicators refine existing ones; e. g. the indicator "allowed values for missings" refines the existing indicator "allowed values for qualitative data elements". Seven projects' measures contribute supplementary aspects of data quality. The draft of the revised set of quality indicators will now be reviewed within and beyond the group.

Original languageEnglish
JournalStudies in Health Technology and Informatics
Pages (from-to)711-5
Number of pages5
Publication statusPublished - 2012


Dive into the research topics of 'Indicators of data quality: revision of a guideline for networked medical research'. Together they form a unique fingerprint.

Cite this