In a recent video interview, Arun Krishnaraj, MD, MPH and David Larson, MD, MBA, discussed the continued use of physical media to transport medical images between different health-care facilities, resulting inefficiencies and delays with patient care, and the initiative to create a linked multi-hub model to end this dated practice once and for all.
Despite the technological advances in transferring electronic health information, medical imaging data, to this day, is predominantly exchanged between instiutions via physical media (such as a CD or flash drive) that patients carry to an appointment at another facility.
Emphasizing the impact of this seemingly dated practice on workflow efficiencies within radiology and the broader field of health care, Arun Krishnaraj, MD, MPH and David Larson, MD, MBA, said the continued reliance on patients to transport medical images via physical media (such as a CD or flash drive) can lead to delays in patient care.
In their quest to change this dynamic, Drs. Krishnaraj and Larson recently co-authored a provocative article, “Moving Toward Seamless Interinstitutional Electronic Image Transfer” in the Journal of the American College of Radiology (JACR). In the article and in a video interview with Diagnostic Imaging, Drs. Krishnaraj and Larson discussed the potential creation of a “linked multi-hub model” that would facilitate electronic transmission of medical images through image exchange companies that would connect different health-care facilities.
“Everyone in the imaging community should really see this as a call to arms to work with our imaging vendor exchange communities to solve this problem for the benefit of patients,” emphasized Dr. Krishnaraj, Chief of the Division of Body Imaging with the Department of Radiology and Medical Imaging at the University of Virginia School of Medicine.
For more insights from Drs. Krishnaraj and Larson, watch the video below:
Seven Takeaways from New CT and MRI Guidelines for Ovarian Cancer Staging
January 20th 2025In an update of previous guidelines from the European Society of Urogenital Radiology published in 2010, a 21-expert panel offered consensus recommendations on the utility of CT, MRI and PET-CT in the staging and follow-up imaging for patients with ovarian cancer.
Can AI Bolster Breast Cancer Detection in DBT Screening?
January 16th 2025In sequential breast cancer screening with digital breast tomosynthesis (DBT), true positive examinations had more than double the AI case score of true negative examinations and the highest positive AI score changes from previous exams, according to new research.
CT Study Reveals Key Indicators for Angiolymphatic Invasion in Non-Small Cell Lung Cancer
January 15th 2025In computed tomography (CT) scans for patients with solid non-small cell lung cancer (NSCLC) < 30 mm, emerging research suggests the lollipop sign is associated with a greater than fourfold likelihood of angiolymphatic invasion.
Can Generative AI Facilitate Simulated Contrast Enhancement for Prostate MRI?
January 14th 2025Deep learning synthesis of contrast-enhanced MRI from non-contrast prostate MRI sequences provided an average multiscale structural similarity index of 70 percent with actual contrast-enhanced prostate MRI in external validation testing from newly published research.