GE Healthcare Launches Workload Management Platform at SIIM Conference

Article

The new workload management system reportedly emphasizes predictive analytics to facilitate efficient workload distribution and increase reading capacity for radiologists.

Recognizing the challenging burden of escalating worklist volume for radiologists, GE Healthcare launched a new workload management system at the Society for Imaging Informatics in Medicine (SIIM) conference.

The company said the workload management software, which can be utilized with its PACS products, employs predictive analytics to optimize workflow and productivity among radiologists while ensuring peak reading efficiency. The workload management system is reportedly integrated with Helix Radiology Performance Suite, a combination of products and services that enhance imaging workflow, developed by Q-IT, a subsidiary of Quantum Imaging and Therapeutic Associates.

Elizabeth Bergey, M.D., the president and CEO of Quantum Imaging and Therapeutic Associates, said the new workload management system can reduce STAT turnaround times and slash the percentage of exceptions with contractual service level agreements (SLAs). Dr. Bergey emphasized that the new platform from GE Healthcare takes the guesswork out of worklist case selection.

“With this intelligent workload management solution, enterprise equilibrium is achieved by intelligently prioritizing and dynamic exam assignment based on the real-time assessment of the active radiologist workforce, their skill sets and using novel exam complexity modifiers to help enable the right radiologist is reading the right exam at the right time,” explained Dr. Bergey.

Recent Videos
Can AI Assessment of Longitudinal MRI Scans Improve Prediction for Pediatric Glioma Recurrence?
A Closer Look at MRI-Guided Adaptive Radiotherapy for Monitoring and Treating Glioblastomas
Incorporating CT Colonography into Radiology Practice
What New Research Reveals About Computed Tomography and Radiation-Induced Cancer Risk
New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection
Can Deep Learning Provide a CT-Less Alternative for Attenuation Compensation with SPECT MPI?
Employing AI in Detecting Subdural Hematomas on Head CTs: An Interview with Jeremy Heit, MD, PhD
Pertinent Insights into the Imaging of Patients with Marfan Syndrome
What New Brain MRI Research Reveals About Cannabis Use and Working Memory Tasks
Current and Emerging Legislative Priorities for Radiology in 2025
Related Content
© 2025 MJH Life Sciences

All rights reserved.