Creating and implementing a COVID-19 recruitment Data Mart.

The COVID-19 pandemic has resulted in an unprecedented strain on every aspect of the healthcare system, and clinical research is no exception. Researchers are working against the clock to ramp up research studies addressing every angle of COVID-19 - gaining a better understanding of person-to-person transmission, improving methods for diagnosis, and developing therapies to treat infection and vaccines to prevent it. The impact of the virus on research efforts is not limited to investigators and their teams. Potential participants also face unparalleled opportunities and requests to participate in research, which can result in a significant amount of participant fatigue. The Vanderbilt Institute for Clinical and Translational Research recognized early in the pandemic that a solution to assist researchers in the rapid identification of potential participants was critical, and thus developed the COVID-19 Recruitment Data Mart. This solution does not rest solely on technology; the addition of experienced project managers to support researchers and facilitate collaboration was essential. Since the platform and study support tools were launched on July 20, 2020, four studies have been onboarded and a total of 1693 potential participant matches have been shared. Each of these patients had agreed in advance to direct contact for COVID-19 research and had been matched to study-specific inclusion/exclusion criteria. Our innovative Data Mart system is scalable and looks promising as a generalizable solution for simultaneously recommending individuals from a pool of patients against a pool of time-sensitive trial opportunities.

The REDCap consortium: Building an international community of software platform partners.

The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.

Evaluating automated electronic case report form data entry from electronic health records.

Background: Many clinical trials leverage real-world data. Typically, these data are manually abstracted from electronic health records (EHRs) and entered into electronic case report forms (CRFs), a time and labor-intensive process that is also error-prone and may miss information. Automated transfer of data from EHRs to eCRFs has the potential to reduce data abstraction and entry burden as well as improve data quality and safety. Methods: We conducted a test of automated EHR-to-CRF data transfer for 40 participants in a clinical trial of hospitalized COVID-19 patients. We determined which coordinator-entered data could be automated from the EHR (coverage), and the frequency with which the values from the automated EHR feed and values entered by study personnel for the actual study matched exactly (concordance). Results: The automated EHR feed populated 10,081/11,952 (84%) coordinator-completed values. For fields where both the automation and study personnel provided data, the values matched exactly 89% of the time. Highest concordance was for daily lab results (94%), which also required the most personnel resources (30 minutes per participant). In a detailed analysis of 196 instances where personnel and automation entered values differed, both a study coordinator and a data analyst agreed that 152 (78%) instances were a result of data entry error. Conclusions: An automated EHR feed has the potential to significantly decrease study personnel effort while improving the accuracy of CRF data.