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To achieve the Learning Health Care System, we must harness electronic health data (EHD) by providing effective tools for researchers to access data efficiently. EHD is proliferating and researchers are relying on these data to pioneer discovery. Tools must be user-centric to ensure their utility. To this end, we conducted a qualitative study to assess researcher needs and barriers to using EHD. Researchers expressed the need to be confident about the data and have easy access, a clear process for exploration and access, and adequate resources, while barriers included difficulties in finding datasets, usability of the data, cumbersome processes, and lack of resources. These needs and barriers can inform the design process for innovating tools to increase utility of EHD. Understanding researcher needs is key to building effective user-centered EHD tools to support translational research.

As Electronic Health Record (EHR) systems are becoming more prevalent in the U.S. health care domain, the utility of EHR data in translational research and clinical decision-making gains prominence. Leveraging primay· care-based. multi-clinic EHR data, this paper introduces a web-based visualization tool, the Variability Explorer Tool (VET), to assist researchers with profiling variability among diagnosis codes. VET applies a simple statistical method to approximate probability distribution functions for the prevalence of any given diagnosis codes to visualize between-clinic and across-year variability. In a depression diagnoses use case, VET outputs demonstrated substantial variability in code use. Even though data quality research often characterizes variability as an indicator for data quality, variability can also reflect real characteristics of data, such as practice-level, and patient-level issues. Researchers benefit from recognizing variability in early stages of research to improve their research design and ensure validity and generalizability of research findings.

Bi-directional translational pathways between scientific discoveries and primary care are crucial for improving individual patient care and population health. The Data QUEST pilot project is a program supporting data sharing amongst community based primary care practices and is built on a technical infrastructure to share electronic health record data. We developed a set of governance requirements from interviewing and collaborating with partner organizations. Recommendations from our partner organizations included: 1) partner organizations can physically terminate the link to the data sharing network and only approved data exits the local site; 2) partner organizations must approve or reject each query; 3) partner organizations and researchers must respect local processes, resource restrictions, and infrastructures; and 4) partner organizations can be seamlessly added and removed from any individual data sharing query or the entire network.

The University of Washington Institute of Translational Health Sciences is engaged in a project, LC Data QUEST, building data sharing capacity in primary care practices serving rural and tribal populations in the Washington, Wyoming, Alaska, Montana, Idaho region to build research infrastructure. We report on the iterative process of developing the technical architecture for semantically aligning electronic health data in primary care settings across our pilot sites and tools that will facilitate linkages between the research and practice communities. Our architecture emphasizes sustainable technical solutions for addressing data extraction, alignment, quality, and metadata management. The architecture provides immediate benefits to participating partners via a clinical decision support tool and data querying functionality to support local quality improvement efforts. The FInDiT tool catalogues type, quantity, and quality of the data that are available across the LC Data QUEST data sharing architecture. These tools facilitate the bi-directional process of translational research.