A novel method to assess data quality in large medical registries and databases. (4th January 2019)

• Record Type: Journal Article
• Authors: Perren, Andreas; Cerutti, Bernard; Kaufmann, Mark; Rothen, Hans Ulrich
• Is Part Of: International journal for quality in health care. Volume 31:Number 7(2019)
• Page Start: 1
• Page End: 7
• Publication Date: 2019-01-04
• Subjects: medical registry -- quality control -- accuracy -- completeness -- funnel-plot; Medical care -- Quality control -- Periodicals; 362.1068
• DOI: 10.1093/intqhc/mzy249
• Languages: English
• ISSNs: 1353-4505

Abstract

Abstract: Background: There is no gold standard to assess data quality in large medical registries. Data auditing may be impeded by data protection regulations. Objective: To explore the applicability and usefulness of funnel plots as a novel tool for data quality control in critical care registries. Method: The Swiss ICU-Registry from all 77 certified adult Swiss ICUs (2014 and 2015) was subjected to quality assessment (completeness/accuracy). For the analysis of accuracy, a list of logical rules and cross-checks was developed. Type and number of errors (true coding errors or implausible data) were calculated for each ICU, along with noticeable error rates (>mean + 3 SD in the variable's summary measure, or >99.8% CI in the respective funnel-plot). Results: We investigated 164 415 patient records with 31 items each (37 items: trauma diagnosis). Data completeness was excellent; trauma was the only incomplete item in 1495 of 9871 records (0.1%, 0.0%–0.6% [median, IQR]). In 15 572 patients records (9.5%), we found 3121 coding errors and 31 265 implausible situations; the latter primarily due to non-specific information on patients' provenance/diagnosis or supposed incoherence between diagnosis and treatments. Together, the error rate was 7.6% (5.9%–11%; median, IQR). Conclusions: The Swiss ICU-Registry is almost complete and data quality seems to be adequate. We propose funnel plots as suitable, easy to implement instrument to assist in quality assurance of such a registry. Based on our analysis, specific feedback to ICUs with special-cause variation is possible and may promote such ICUs to improve the quality of their data.