One key component of realizing Evidence-informed decision making (EIDM) is ensuring decision makers have easy access to the best available evidence as quickly as possible. NCCMT does this through several initiatives including offering high-quality resources, real-world training, and practical mentorship. Their work also has a global impact, as they conduct critical COVID-19 research to support public health decision-making at the local, provincial/territorial, national, and international levels.
Reducing the Screening Burden by Over 75%
The Health Evidence™ Registry is a repository of over 6,500 critically appraised systematic reviews on the effectiveness of public health interventions maintained by NCCMT. Every month, they search and identify relevant systematic reviews for their repository. On average, their database searches return approximately 8,000-14,000 references monthly to screen. As the number of reviews getting published each year steadily rises, the NCCMT team needed a better solution to manage their growing workload.
“Screening over 8,000 references each month before starting quality appraisal is a very time-consuming process. We needed to explore automation options to help make the workload more feasible,” said Kristin Read, Research Coordinator, NCCMT. “With DistillerSR, we can confidently reduce our result sets by over 75% with minimal false excludes.”
Consistently Faster Evidence
NCCMT is tasked with appraising and adding new systematic reviews to the Health Evidence Registry each month. Working with a team of only 4-6 staff, it's imperative that time and resources are maximized. By using DistillerSR’s artificial intelligence, the NCCMT team saves approximately 15-20 hours per month on screening.
By reducing the time it takes to perform the initial screening, NCCMT can expedite their monthly processes and move relevant references on to critical appraisal and eventually upload them to the repository earlier. The approach makes it easier and faster to complete the entire monthly process.
“DistillerSR helped us create an easier workflow especially at the beginning of each month when the workload is heaviest,” said Maureen Dobbins, RN, PHD, Scientific Director, NCCMT, School of Nursing, McMaster University. “We’re at a point now where doing it manually is no longer feasible and is a barrier to getting the research out to decision makers ASAP.”