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.
Burnout in Radiology: Key Risk Factors and Promising Solutions
June 9th 2025Recognizing the daunting combination of increasing imaging volume and workforce shortages, these authors discuss key risk factors contributing to burnout and moral injury in radiology, and potential solutions to help preserve well-being among radiologists.
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.
Mammography AI Platform for Five-Year Breast Cancer Risk Prediction Gets FDA De Novo Authorization
June 2nd 2025Through AI recognition of subtle patterns in breast tissue on screening mammograms, the Clairity Breast software reportedly provides validated risk scoring for predicting one’s five-year risk of breast cancer.