RESUMEN
Purpose: This study assesses the feasibility of biomedical informatics resources for efficient recruitment of rural residents with cancer to a clinical trial of a quality-of-life (QOL) mobile app. These resources have the potential to reduce costly, time-consuming, in-person recruitment methods. Methods: A cohort was identified from the electronic health record data repository and cross-referenced with patients who consented to additional research contact. Rural-urban commuting area codes were computed to identify rurality. Potential participants were emailed study details, screening questions, and an e-consent link via REDCap. Consented individuals received baseline questionnaires automatically. A sample minimum of n = 80 [n = 40 care as usual (CAU) n = 40 mobile app intervention] was needed. Results: N = 1298 potential participants (n = 365 CAU; n = 833 intervention) were screened for eligibility. For CAU, 68 consented, 67 completed baseline questionnaires, and 54 completed follow-up questionnaires. For intervention, 100 consented, 97 completed baseline questionnaires, and 58 completed follow-up questionnaires. The CAU/intervention reached 82.5%/122.5% of the enrollment target within 2 days. Recruitment and retention rates were 15.3% and 57.5%, respectively. The mean age was 59.5 ± 13.5 years. The sample was 65% women, 20% racial/ethnic minority, and 35% resided in rural areas. Conclusion: These results demonstrate that biomedical informatics resources can be highly effective in recruiting for cancer QOL research. Precisely identifying individuals likely to meet inclusion criteria who previously indicated interest in research participation expedited recruitment. Participants completed the consent and baseline questionnaires with zero follow-up contacts from the research team. This low-touch, repeatable process may be highly effective for multisite clinical trials research seeking to include rural residents.
RESUMEN
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.