OBJECTIVES: Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility. MATERIALS AND METHODS: We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts. RESULTS: EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science. DISCUSSION: As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied. CONCLUSION: This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.

IMPORTANCE: This manuscript will be of interest to most Clinical and Translational Science Awards (CTSA) as they retool for the increasing emphasis on translational science from translational research. This effort is an extension of the EDW4R work that most CTSAs have done to deploy infrastructure and tools for researchers to access clinical data. OBJECTIVES: The Iowa Health Data Resource (IHDR) is a strategic investment made by the University of Iowa to improve access to real-world health data. The goals of IHDR are to improve the speed of translational health research, to boost interdisciplinary collaboration, and to improve literacy about health data. The first objective toward this larger goal was to address gaps in data access, data literacy, lack of computational environments for processing Personal Health Information (PHI) and the lack of processes and expertise for creating transformative datasets. METHODS: A three-pronged approach was taken to address the objective. The approach involves integration of an intercollegiate team of non-informatics faculty and staff, a data enclave for secure patient data analyses, and novel comprehensive datasets. RESULTS: To date, all five of the health science colleges (dentistry, medicine, nursing, pharmacy, and public health) have had at least one staff and one faculty member complete the two-month experiential learning curriculum. Over the first two years of this project, nine cohorts totaling 36 data liaisons have been trained, including 18 faculty and 18 staff. IHDR data enclave eliminated the need to duplicate computational infrastructure inside the hospital firewall which reduced infrastructure, hardware and human resource costs while leveraging the existing expertise embedded in the university research computing team. The creation of a process to develop and implement transformative datasets has resulted in the creation of seven domain specific datasets to date. CONCLUSION: The combination of people, process, and technology facilitates collaboration and interdisciplinary research in a secure environment using curated data sets. While other organizations have implemented individual components to address EDW4R operational demands, the IHDR combines multiple resources into a novel, comprehensive ecosystem IHDR enables scientists to use analysis tools with electronic patient data to accelerate time to science.

OBJECTIVE: Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, effective approaches for enterprise data warehouses for research (EDW4R) development, maintenance, and sustainability remain unclear. The goal of this qualitative study was to understand CTSA EDW4R operations within the broader contexts of academic medical centers and technology. MATERIALS AND METHODS: We performed a directed content analysis of transcripts generated from semistructured interviews with informatics leaders from 20 CTSA hubs. RESULTS: Respondents referred to services provided by health system, university, and medical school information technology (IT) organizations as "enterprise information technology (IT)." Seventy-five percent of respondents stated that the team providing EDW4R service at their hub was separate from enterprise IT; strong relationships between EDW4R teams and enterprise IT were critical for success. Managing challenges of EDW4R staffing was made easier by executive leadership support. Data governance appeared to be a work in progress, as most hubs reported complex and incomplete processes, especially for commercial data sharing. Although nearly all hubs (n = 16) described use of cloud computing for specific projects, only 2 hubs reported using a cloud-based EDW4R. Respondents described EDW4R cloud migration facilitators, barriers, and opportunities. DISCUSSION: Descriptions of approaches to how EDW4R teams at CTSA hubs work with enterprise IT organizations, manage workforces, make decisions about data, and approach cloud computing provide insights for institutions seeking to leverage patient data for research. CONCLUSION: Identification of EDW4R best practices is challenging, and this study helps identify a breadth of viable options for CTSA hubs to consider when implementing EDW4R services.

Research Patient Data Repositories (RPDRs) have become essential infrastructure for traditional Clinical and Translational Science Award (CTSA) programs and increasingly for a wide range of research consortia and learning health system networks.1–5 Almost every institution with a CTSA or Clinical Translational Research (CTR) program (found in states with lower amounts of National Institutes of Health funding) hosts an RPDR for the benefit of affiliated researchers. These repositories aim to enable healthcare research based upon the patient populations they serve. Within the institution, RPDRs are valuable for a range of research activities. They are used to identify patients for clinical trial recruitment using privacy-preserving methods to search and extract specific cohorts of trial-eligible patients.6 They aid in developing and validating computable phenotypes that are increasingly important for accurately identifying patient cohorts in a reproducible fashion.7 RPDRs provide de-identified patient data for population health research and support a growing body of artificial intelligence to predict patient outcomes.8 Further, clinical studies can often be simulated using data from an RPDR.9 Beyond the institution, aggregates of de-identified datasets from multiple institutions linked with privacy-preserving hash codes provide an unprecedented opportunity to conduct population health research, perform comparative effectiveness analyses and apply artificial intelligence methods over large and diverse populations.10 The data contained within the RPDR vary across institutions, based on institutional strengths and weaknesses; the papers published in this issue reflect that variability (see Table 1). Data are commonly acquired from local electronic health records (EHRs) and other clinical information systems that capture information during clinical care. Data consist of diagnoses, problem lists, procedures, prescribed medications, laboratory exams, and many types of free-text reports. Overall, the benefits of the RPDR for accelerating translational research can be significant. For example, at Harvard, in 2006, between $94 and $136 million in annual research funding was linked to the use of data from the RPDR.11

OBJECTIVE: Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, adoption of electronic data warehouses for research (EDW4R) containing data from electronic health record systems is nearly ubiquitous. Although benefits of EDW4R include more effective, efficient support of scientists, little is known about how CTSA hubs have implemented EDW4R services. The goal of this qualitative study was to understand the ways in which CTSA hubs have operationalized EDW4R to support clinical and translational researchers. MATERIALS AND METHODS: After conducting semistructured interviews with informatics leaders from 20 CTSA hubs, we performed a directed content analysis of interview notes informed by naturalistic inquiry. RESULTS: We identified 12 themes: organization and data; oversight and governance; data access request process; data access modalities; data access for users with different skill sets; engagement, communication, and literacy; service management coordinated with enterprise information technology; service management coordinated within a CTSA hub; service management coordinated between informatics and biostatistics; funding approaches; performance metrics; and future trends and current technology challenges. DISCUSSION: This study is a step in developing an improved understanding and creating a common vocabulary about EDW4R operations across institutions. Findings indicate an opportunity for establishing best practices for EDW4R operations in academic medicine. Such guidance could reduce the costs associated with developing an EDW4R by establishing a clear roadmap and maturity path for institutions to follow. CONCLUSIONS: CTSA hubs described varying approaches to EDW4R operations that may assist other institutions in better serving investigators with electronic patient data.