Open Access Highly Accessed Research article

Influence of data quality on computed Dutch hospital quality indicators: a case study in colorectal cancer surgery

Kathrin Dentler12*, Ronald Cornet24, Annette ten Teije1, Pieter Tanis3, Jean Klinkenbijl3, Kristien Tytgat3 and Nicolette de Keizer2

Author Affiliations

1 Department of Computer Science, VU University Amsterdam, Amsterdam, Netherlands

2 Department of Medical Informatics, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands

3 Gastrointestinal Oncology Centre Amsterdam, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands

4 Department of Biomedical Engineering, Linköping University, Linköping, Sweden

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BMC Medical Informatics and Decision Making 2014, 14:32  doi:10.1186/1472-6947-14-32

Published: 11 April 2014



Our study aims to assess the influence of data quality on computed Dutch hospital quality indicators, and whether colorectal cancer surgery indicators can be computed reliably based on routinely recorded data from an electronic medical record (EMR).


Cross-sectional study in a department of gastrointestinal oncology in a university hospital, in which a set of 10 indicators is computed (1) based on data abstracted manually for the national quality register Dutch Surgical Colorectal Audit (DSCA) as reference standard and (2) based on routinely collected data from an EMR. All 75 patients for whom data has been submitted to the DSCA for the reporting year 2011 and all 79 patients who underwent a resection of a primary colorectal carcinoma in 2011 according to structured data in the EMR were included. Comparison of results, investigating the causes for any differences based on data quality analysis. Main outcome measures are the computability of quality indicators, absolute percentages of indicator results, data quality in terms of availability in a structured format, completeness and correctness.


All indicators were fully computable based on the DSCA dataset, but only three based on EMR data, two of which were percentages. For both percentages, the difference in proportions computed based on the two datasets was significant.

All required data items were available in a structured format in the DSCA dataset. Their average completeness was 86%, while the average completeness of these items in the EMR was 50%. Their average correctness was 87%.


Our study showed that data quality can significantly influence indicator results, and that our EMR data was not suitable to reliably compute quality indicators. EMRs should be designed in a way so that the data required for audits can be entered directly in a structured and coded format.

Data quality; Clinical quality indicators; Electronic medical record; Clinical audit; Patient data; Reuse; Secondary use