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Influence of data quality on computed Dutch hospital quality indicators: a case study in colorectal cancer surgery
Vrije University of Amsterdam, Netherlands University of Amsterdam, Netherlands .
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. University of Amsterdam, Netherlands.
Vrije University of Amsterdam, Netherlands .
University of Amsterdam, Netherlands .
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2014 (English)In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, Vol. 14, no 32Article in journal (Refereed) Published
Abstract [en]

Background: 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). Methods: 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. Results: 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%. Conclusions: 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.

Place, publisher, year, edition, pages
BioMed Central , 2014. Vol. 14, no 32
Keyword [en]
Data quality; Clinical quality indicators; Electronic medical record; Clinical audit; Patient data; Reuse; Secondary use
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-106969DOI: 10.1186/1472-6947-14-32ISI: 000335251700001OAI: diva2:721386
Available from: 2014-06-04 Created: 2014-06-02 Last updated: 2014-06-04

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