Teleradiologists perform better when they focus on images sent from fewer hospitals rather than reading for a larger number of facilities.
Teleradiologists generally are more accurate if they concentrate on images sent from fewer hospitals rather than reading for a larger number of facilities, according to a recent study in the journal Organization Science.
Outsourcing certain radiology services is becoming a more common practice. "There is debate out there about whether or not we should be outsourcing this kind of work," Jonathan Clark, assistant professor of health policy and administration, at Penn State, said in a press release.
"Some say that one CT is the same as another, so it doesn't really matter if the CT is coming from Hospital A or Hospital B; what matters is that the person reading the image has the right training and experience. The other side of the debate says that radiological images are not commodities and that the process is more nuanced than simply exchanging bits of information over the information super highway. From this perspective a radiologist's performance will improve as he or she learns the nuances of reading images from a particular hospital."
To address this issue Clark and his colleagues examined more than 2.7 million cases read by 97 radiologists for 1,431 customers. Researchers found that by estimating learning curves, they could determine the extent of a radiologist’s productivity was a function of reading an image for one hospital or many hospitals. Prior experience with one customer had a greater impact on performance.
That said, the researchers also did find that when radiologists had a variety of customers, they could increase their specific ability for those particular clients.
The researchers concluded that outsourcing and teleradiology is not a problem in itself, but that teleradiology companies should take into consideration that they may better serve their clients if they have certain radiologists provide service for specific clients as much as possible. Also, the facilities that outsource should figure out how they can most benefit from the radiologists’ productivity and accuracy.
Could AI-Powered Abbreviated MRI Reinvent Detection for Structural Abnormalities of the Knee?
April 24th 2025Employing deep learning image reconstruction, parallel imaging and multi-slice acceleration in a sub-five-minute 3T knee MRI, researchers noted 100 percent sensitivity and 99 percent specificity for anterior cruciate ligament (ACL) tears.
The Reading Room: Artificial Intelligence: What RSNA 2020 Offered, and What 2021 Could Bring
December 5th 2020Nina Kottler, M.D., chief medical officer of AI at Radiology Partners, discusses, during RSNA 2020, what new developments the annual meeting provided about these technologies, sessions to access, and what to expect in the coming year.
New Collaboration Offers Promise of Automating Prior Authorizations in Radiology with AI
March 26th 2025In addition to a variety of tools to promote radiology workflow efficiencies, the integration of the Gravity AI tools into the PowerServer RIS platform may reduce time-consuming prior authorizations to minutes for completion.
Strategies to Reduce Disparities in Interventional Radiology Care
March 19th 2025In order to help address the geographic, racial, and socioeconomic barriers that limit patient access to interventional radiology (IR) care, these authors recommend a variety of measures ranging from increased patient and physician awareness of IR to mobile IR clinics and improved understanding of social determinants of health.