Using the CER Hub to ensure data quality in a multi-institution smoking cessation study. (3rd July 2014)

• Record Type: Journal Article
• Authors: Walker, Kari L; Kirillova, Olga; Gillespie, Suzanne E; Hsiao, David; Pishchalenko, Valentyna; Pai, Akshatha Kalsanka; Puro, Jon E; Plumley, Robert; Kudyakov, Rustam; Hu, Weiming; Allisany, Art; McBurnie, MaryAnn; Kurtz, Stephen E; Hazlehurst, Brian L
• Is Part Of: Journal of the American Medical Informatics Association. Volume 21:Number 6(2014:Nov.)
• Page Start: 1129
• Page End: 1135
• Publication Date: 2014-07-03
• Subjects: Electronic medical records -- Research data quality assurance; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1136/amiajnl-2013-002629
• Languages: English
• ISSNs: 1067-5027

Abstract

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.