Complementary approaches are needed to harness both retrospective and real-time data to identify potential study recruits and alert appropriate staff, according to an article published in an online article in the Journal of the American Medical Informatics Association .
Jeffrey M. Ferranti, MD, CMIO at Duke University School of Medicine in Durham, N.C., and colleagues sought to create an extensible, easy-to-implement workflow for cohort recruitment scenarios in which eligibility depends upon time-sensitive criteria or data not in the Decision Support Repository (DSR), Duke’s enterprise data warehouse.
“Failure to reach research subject recruitment goals is a significant impediment to the success of many clinical trials,” Ferranti and colleagues wrote. “Implementation of health IT has allowed retrospective analysis of data for cohort identification and recruitment, but few institutions have also leveraged real-time streams to support such activities.”
Duke Medicine deployed a hybrid tool, Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN), that combines both retrospective warehouse data and clinical events contained in prospective HL7 messages to immediately alert study personnel of potential recruits as they become eligible.
DISCERN analyzes more than 500,000 messages daily in service of 12 projects, according to the authors. Users may receive results via email, text pages or on-demand reports. Preliminary results suggested DISCERN's ability to reason over both retrospective and real-time data increases study enrollment rates while reducing the time required to complete recruitment-related tasks. The authors have introduced a preconfigured DISCERN function as a self-service feature for users.
For example, DISCERN was used to enroll patients in a study investigating the timing of administration of a HPV vaccine series; specifically, the effects on the immune system if doses are not given at specified intervals (zero, two, and six months). The study required blood to be drawn from subjects at the administration of the third vaccine dose and again one month later. Researchers needed to be able to quickly find patients residing in the appropriate window of the vaccine series.
To identify a cohort for this study, DISCERN scanned for adolescent girls who received the first or second shot in the series, as identified by CPT codes, according to the authors. Results were made accessible to researchers through on-demand reporting that showed patient identifiers and upcoming appointments. The study team then contacted the attending physician to obtain permission to approach the patient at the next appointment.
In the first 10 months of the study, subjects were recruited using traditional pathways, which resulted in 43 enrollees out of 448 patients approached (an enrollment rate of 9.6 percent). “Starting in April of 2010, DISCERN was used to identify potential recruits. Over 4 months, there were 62 enrollees out of 421 patients approached—an enrollment rate of 14.7 percent,” they wrote. “Not only was this 53 percent higher, but the recruitment rate increased from an average of 4.3 subjects/month to 15.5 subjects/month.”
According to the authors, the DISCERN framework is adoptable primarily by organizations using both HL7 message streams and a data warehouse. “More efficient recruitment may exacerbate competition for research subjects, and investigators uncomfortable with new technology may find themselves at a competitive disadvantage in recruitment,” they wrote.
“The DISCERN framework serves a variety of clinical trial recruitment scenarios, including problematic variations in which study eligibility hinges on a confluence of specifications, clinical events and temporal dependencies,” the authors concluded. “In contrast to other cohort recruitment tools, the DISCERN model offers a highly flexible solution for prospective recruitment, with architecture extensible to any other organization that relies on HL7 messaging among clinical data systems. Our experience may help others seeking to deploy similar systems achieve success more quickly.”
The authors did note that more study is needed to definitively evaluate whether DISCERN goes beyond patient identification to measurably increase the speed of successful recruitment into clinical trials.