OBJECTIVE: Multicenter clinical trials often abstract data from the electronic health record (EHR) onto a case report form (CRF) via an electronic data capture (EDC) system. The abstraction process is manual, time-consuming, and error prone. We evaluated scaling automated CRF completion from one institution to other sites in a multicenter trial. METHODS: We exported a REDCap project with embedded EHR mapping for a completed platform trial from one institution and delivered it to two other study sites. The receiving sites determined whether additional data elements could be mapped for their institution. We measured the proportion of data entry that could be automated, the extent of agreement between the human- and automation-entered data, and the staff effort required to set up automated CRF completion. RESULTS: It took approximately 26 and 15 h to set up automation and to map data from the EHR systems at the two receiving institutions, respectively. For 20 total participants at the two receiving institutions, out of 4404 fields with human-entered data, using CDIS could have prevented 764 data entry errors that persisted after monitoring and would have saved 17 total hours or 51 min per participant of manual data entry time. CONCLUSION: With these initial estimates of the configuration and data re-mapping time required to scale automated CRF completion and impact on data quality, investigators planning multicenter trials are better positioned to determine when benefits of automation outweigh the expense of manual data abstraction for clinical trials.

OBJECTIVE: We describe the Clickbusters initiative implemented at Vanderbilt University Medical Center (VUMC), which was designed to improve safety and quality and reduce burnout through the optimization of clinical decision support (CDS) alerts. MATERIALS AND METHODS: We developed a 10-step Clickbusting process and implemented a program that included a curriculum, CDS alert inventory, oversight process, and gamification. We carried out two 3-month rounds of the Clickbusters program at VUMC. We completed descriptive analyses of the changes made to alerts during the process, and of alert firing rates before and after the program. RESULTS: Prior to Clickbusters, VUMC had 419 CDS alerts in production, with 488 425 firings (42 982 interruptive) each week. After 2 rounds, the Clickbusters program resulted in detailed, comprehensive reviews of 84 CDS alerts and reduced the number of weekly alert firings by more than 70 000 (15.43%). In addition to the direct improvements in CDS, the initiative also increased user engagement and involvement in CDS. CONCLUSIONS: At VUMC, the Clickbusters program was successful in optimizing CDS alerts by reducing alert firings and resulting clicks. The program also involved more users in the process of evaluating and improving CDS and helped build a culture of continuous evaluation and improvement of clinical content in the electronic health record.

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.