• Allison M Cole, Kari A Stephens, Gina A Keppel, Hossein Estiri, and Laura-Mae Baldwin.
• University of Washington, Institute of Translational Health Sciences.
• EGEMS (Wash DC). 2016 Jan 1; 4 (2): 1206.

ContextThe widespread adoption of electronic health records (EHRs) offers significant opportunities to conduct research with clinical data from patients outside traditional academic research settings. Because EHRs are designed primarily for clinical care and billing, significant challenges are inherent in the use of EHR data for clinical and translational research. Efficient processes are needed for translational researchers to overcome these challenges. The Data QUEST Coordinating Center (DQCC), which oversees Data Query Extraction Standardization Translation (Data QUEST) - a primary-care, EHR data-sharing infrastructure - created processes that guide EHR data extraction for clinical and translational research across these diverse practices. We describe these processes and their application in a case example.Case DescriptionThe DQCC process for developing EHR data extractions not only supports researchers' access to EHR data, but supports this access for the purpose of answering scientific questions. This process requires complex coordination across multiple domains, including the following: (1) understanding the context of EHR data; (2) creating and maintaining a governance structure to support exchange of EHR data; and (3) defining data parameters that are used in order to extract data from the EHR. We use the Northwest-Alaska Pharmacogenomics Research Network (NWA-PGRN) as a case example that focuses on pharmacogenomic discovery and clinical applications to describe the DQCC process. The NWA-PGRN collaborates with Data QUEST to explore ways to leverage primary-care EHR data to support pharmacogenomics research.FindingsPreliminary analysis on the case example shows that initial decisions about how researchers define the study population can influence study outcomes.Major Themes And ConclusionsThe experience of the DQCC demonstrates that coordinating centers provide expertise in helping researchers understand the context of EHR data, create and maintain governance structures, and guide the definition of parameters for data extractions. This expertise is critical to supporting research with EHR data. Replication of these strategies through coordinating centers may lead to more efficient translational research. Investigators must also consider the impact of initial decisions in defining study groups that may potentially affect outcomes.

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