In a recent interview, Richard Duszak, MD, discussed new study findings that showed over nine percent annual increases in ultrasound, CT and MRI interpretation by office-based non-physician practitioners (NPPs) between 2013 and 2022.
New research shows that non-physician practitioner (NPP) image interpretation in office settings grew by more than nine percent annually over the course of a decade for ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) and increased by more than seven percent annually for nuclear medicine interpretation.
In a recent interview, study co-author Richard Duszak, MD, shared his perspective on the increases in NPP image interpretation reported in the study, which was recently published in the Journal of the American College of Radiology.
“The compound growth rate here is pretty staggering: 9 percent per year for X-ray, ultrasound, CT, and MR with (nuclear medicine) not far behind at 7 percent. They’re all growing at a relatively similar rate,” noted Dr. Duszak, a professor and chair of the Department of Radiology, at the University of Mississippi Medical Center.
“It's only about 5.5 percent of all the nurse practitioners and (physician assistants) that we were able to identify (via) Medicare claims (data) that are doing this, but they are empowered, perhaps emboldened, to say, ‘I can look at an ankle X ray, therefore, I'm going to look at a brain CT scan.’ From somebody who spent an extensive amount of time in medical school, in residency, going through board certification, to have folks come out of nurse practitioner school with essentially no training in imaging — which is what the data from nurse practitioner training program literature says — (and attempt to interpret images) is a bit scary.”
(Editor’s note: For related content, see “The Rise of NPP Image Interpretation: What New Radiology Research Reveals,” “Current Perspectives on Radiology Workforce Issues and Potential Solutions” and “Emergency Department Radiology: Study Shows Higher Imaging Orders by NPPs.”)
Dr. Duszak said there was considerable state by state variation with the NPP image interpretation rates. For example, Dr. Duszak explained that the probability of having an office-based NPP interpret imaging in Alaska is approximately 20 percent and noted this would have been closer to zero percent when he was in residency training. He noted the importance of identifying causes behind these trends in outlier states and emphasizing targeted advocacy and education.
“This is an exponential curve. It really is a potential game changer with regard to what's happening in the workforce and in the marketplace. I think (this) really requires a lot more attention, not just from researchers, but from policy makers as well,” emphasizes Dr. Duszak, a member of the Board of Chancellors for the American College of Radiology.
For more insights from Dr. Duszak, 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.
The Reading Room Podcast: Emerging Trends in the Radiology Workforce
February 11th 2022Richard Duszak, MD, and Mina Makary, MD, discuss a number of issues, ranging from demographic trends and NPRPs to physician burnout and medical student recruitment, that figure to impact the radiology workforce now and in the near future.
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.
Can MRI-Based AI Enhance Risk Stratification in Prostate Cancer?
January 13th 2025Employing baseline MRI and clinical data, an emerging deep learning model was 32 percent more likely to predict the progression of low-risk prostate cancer (PCa) to clinically significant prostate cancer (csPCa), according to new research.