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.
Can Radiomics Bolster Low-Dose CT Prognostic Assessment for High-Risk Lung Adenocarcinoma?
December 16th 2024A CT-based radiomic model offered over 10 percent higher specificity and positive predictive value for high-risk lung adenocarcinoma in comparison to a radiographic model, according to external validation testing in a recent study.
Can AI Facilitate Single-Phase CT Acquisition for COPD Diagnosis and Staging?
December 12th 2024The authors of a new study found that deep learning assessment of single-phase CT scans provides comparable within-one stage accuracies to multiphase CT for detecting and staging chronic obstructive pulmonary disease (COPD).
Study Shows Merits of CTA-Derived Quantitative Flow Ratio in Predicting MACE
December 11th 2024For patients with suspected or known coronary artery disease (CAD) without percutaneous coronary intervention (PCI), researchers found that those with a normal CTA-derived quantitative flow ratio (CT-QFR) had a 22 percent higher MACE-free survival rate.