ATLANTA, June 22, 2021 /PRNewswire/ -- The Radiology Group (TRG), a national radiology professional services company with a physical presence in 28 states, leverages artificial intelligence (AI) to improve workflow intelligence with Radiology AI company RadiLens. In a market dominated by imaging-diagnostic tools, RadiLens is focused on integrating AI into operating processes to realize efficiency gains for constrained but growing healthcare organizations.
Medical imaging volume in the US has grown 10x over a 10-year span while the number of Radiologists has remained largely the same. The use of advanced imaging within the Emergency Department (ED) is also growing exponentially, leading to increasing pressures to minimize wait time for emergent cases. The pursuit to keep up with rising demands has led to a 48% burnout rate for Radiologists.
Understanding these dynamics and considering recent and unique challenges posed by COVID-19 to the industry, TRG partnered with RadiLens to proactively reduce costs, and better manage workloads, flows, and processes for their existing team.
RadiLens deployed their first AI product focused on intelligent and dynamic queueing within the Radiology worklist to improve turnaround time (TAT) standards across all modalities and anatomies. The RadiLens solution integrates directly with existing PACS/RIS viewers to significantly reduce the implementation and learning required for adoption.
"Early assessments indicate an expected TAT improvement of 30% and a margin lift of 12-15% across the group." says Dr. Anand Lalaji, MSK Radiologist and CEO of The Radiology Group. He continues: "More than improving our average TAT, we've gained confidence that the right study is being presented to read every time, significantly reducing wasted attention and context switching from monitoring the worklist. Our team can more exclusively focus on clinical work."
From the perspective of a clinician ordering an imaging study, automated prioritization and assignment garners trust that their studies will be returned in the best timeframe possible. For the Radiology team, this automation removes administrative hurdles they face to appropriately order and allocate studies with complex rules.