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. 2014 Nov-Dec;21(6):1129-35.
doi: 10.1136/amiajnl-2013-002629. Epub 2014 Jul 3.

Using the CER Hub to ensure data quality in a multi-institution smoking cessation study

Kari L Walker  1 , Olga Kirillova  1 , Suzanne E Gillespie  1 , David Hsiao  2 , Valentyna Pishchalenko  2 , Akshatha Kalsanka Pai  3 , Jon E Puro  4 , Robert Plumley  5 , Rustam Kudyakov  6 , Weiming Hu  1 , Art Allisany  1 , MaryAnn McBurnie  1 , Stephen E Kurtz  7 , Brian L Hazlehurst  1
Affiliations

Using the CER Hub to ensure data quality in a multi-institution smoking cessation study

Kari L Walker et al. J Am Med Inform Assoc. 2014 Nov-Dec.

Abstract

Comparative effectiveness research (CER) studies involving multiple institutions with diverse electronic health records (EHRs) depend on high quality data. To ensure uniformity of data derived from different EHR systems and implementations, the CER Hub informatics platform developed a quality assurance (QA) process using tools and data formats available through the CER Hub. The QA process, implemented here in a study of smoking cessation services in primary care, used the 'emrAdapter' tool programmed with a set of quality checks to query large samples of primary care encounter records extracted in accord with the CER Hub common data framework. The tool, deployed to each study site, generated error reports indicating data problems to be fixed locally and aggregate data sharable with the central site for quality review. Across the CER Hub network of six health systems, data completeness and correctness issues were prevalent in the first iteration and were considerably improved after three iterations of the QA process. A common issue encountered was incomplete mapping of local EHR data values to those defined by the common data framework. A highly automated and distributed QA process helped to ensure the correctness and completeness of patient care data extracted from EHRs for a multi-institution CER study in smoking cessation.

Keywords: Electronic medical records; Research data quality assurance.

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Figures

Figure 1
Figure 1
Use of the emrAdapter tool and components of the comparative effectiveness research (CER) Hub common data framework for the quality assurance (QA) process: reading in clinical research document (CRD) files and site defined value mapping files, converting discrete fields as defined in the lookup tables, calculating and producing report files.
Figure 2
Figure 2
Example of a segment from a clinical research document file with associated samples of each report type produced during the quality assurance process. Summary statistics vary by computational process depending on objective. Beyond frequencies of the variable's values reviewed, counts indicating breadth (total number of files variable populated, ‘filesset’) and trend (count of variable populated, encompassing repeating fields, ‘set’) are produced.
Figure 3
Figure 3
Field value mapping process: text string matches are used to identify if the original value entry is present in the look up table, if so, then the assigned value from the look up table is used when creating the output file (1). If the original entry does not match any of the values in the lookup table, no replacements occur (2).
Figure 4
Figure 4
Quality assurance (QA) process workflow.
See this image and copyright information in PMC

References

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