Date of Award

May 2019

Degree Type


Degree Name

Doctor of Philosophy


Health Sciences

First Advisor

Jake Luo

Committee Members

Ron Cisler, Timothy Patrick, Rohit Kate, Michael Payne


Data warehouses, Informatics, Outcomes, Technology adoption, Translational research


Introduction: While funding for research has declined since 2004, the need for rapid, innovative, and lifesaving clinical and translational research has never been greater due to the rise in chronic health conditions, which have resulted in lower life expectancy and higher rates of mortality and adverse outcomes. Finding effective diagnostic and treatment methods to address the complex challenges in individual and population health will require a team science approach, creating the need for multidisciplinary collaboration among practitioners and researchers.

To address this need, the National Institutes of Health (NIH) created the Clinical and Translational Science Awards (CTSA) program. The CTSA program distributes funds to a national network of medical research institutions, known as “hubs,” that work together to improve the translational research process. With this funding, each hub is required to achieve specific goals to support clinical and translational research teams by providing a variety of services, including cutting edge use of informatics technologies. As a result, the majority of CTSA recipients have implemented and maintain data warehouses, which combine disparate data types from a range of clinical and administrative sources, include data from multiple institutions, and support a variety of workflows. These data warehouses provide comprehensive sets of data that extend beyond the contents of a single EHR system and provide more valuable information for translational research.

Although significant research has been conducted related to this technology, gaps exist regarding research team adoption of data warehouses. As a result, more information is needed to understand how data warehouses are adopted and what outcomes are achieved when using them. Specifically, this study focuses on three gaps: research team awareness of data warehouses, the outcomes of data warehouse training for research teams, and how to measure objectively outcomes achieved after training.

By assessing and measuring data warehouse use, this study aims to provide a greater understanding of data warehouse adoption and the outcomes achieved. With this understanding, the most effective and efficient development, implementation, and maintenance strategies can be used to increase the return on investment for these resource-intensive technologies. In addition, technologies can be better designed to ensure they are meeting the needs of clinical and translational science in the 21st century and beyond.

Methods: During the study period, presentations were held to raise awareness of data warehouse technology. In addition, training sessions were provided that focused on the use of data warehouses for research projects. To assess the impact of the presentations and training sessions, pre- and post-assessments gauged knowledge and likelihood to use the technology. As objective measurements, the number of data warehouse access and training requests were obtained, and audit trails were reviewed to assess trainee activities within the data warehouse. Finally, trainees completed a 30-day post-training assessment to provide information about barriers and benefits of the technology.

Results: Key study findings suggest that the awareness presentations and training were successful in increasing research team knowledge of data warehouses and likelihood to use this technology, but did not result in a subsequent increase in access or training requests within the study period. In addition, 24% of trainees completed the associated data warehouse activities to achieve their intended outcomes within 30 days of training. The time needed for adopting the technology, the ease of use of data warehouses, the types of support available, and the data available within the data warehouse may all be factors influencing this completion rate.

Conclusion: The key finding of this study is that data warehouse awareness presentations and training sessions are insufficient to result in research team adoption of the technology within a three-month study period. Several important implications can be drawn from this finding. First, the timeline for technology adoption requires further investigation, although it is likely longer than 90 days. Future assessments of technology adoption should include an individual’s timeline for pursuing the use of that technology. Second, this study provided a definition for outcome achievement, which was completion of