Bradley J. Bohnert, MD: Parsing the Subspecialties

Having radiologists reading the studies within their area of subspecialty training as much as possible enables studies to be read both more quickly and more accurately. It leads to better patient care.

Bradley J. Bohnert, MD
CEO, Radiology Ltd.
May 31, 2018

When neuroradiologist Bradley J. Bohnert, MD, MBA, isn’t describing brain tumors, measuring carotid stenosis, or interpreting spinal MRI studies, he wrestles with practice staffing issues and other management functions as CEO of Radiology Ltd., a Strategic Radiology (SR) member practice in Tucson, Arizona.  He also found the time to map nearly 1000 radiology CPT codes to their appropriate subspecialties as part of SR’s broader effort to aggregate and organize data for clinical benchmarking and business intelligence purposes. In its infancy, the subspecialty mapping project is the first step in an endeavor that Dr. Bohnert hopes will demonstrate the value of radiology subspecialization.

“I am a big proponent of subspecialization in radiology,” Bohnert says. “Not only does it make sense from a business standpoint, but I think it's better medicine. Having radiologists reading the studies within their area of subspecialty training as much as possible enables studies to be read both more quickly and more accurately.  It leads to better patient care.”

Data collection is a primary tool used to identify quality and business best practices and targets for improvement within SR member practices. When Dave Polmanteer, analytics and business intelligence director, shared that he had achieved 100% participation among core member practices in the SR accounts receivables database, Dr. Bohnert’s subspecialty project was conceived.

A Heavy Lift

The first step in creating the database was assigning an appropriate subspecialty for each CPT code.  “We needed to go through the code list and assign a primary subspecialty to each CPT code when appropriate,” he recalls. “Considering the number of CPT codes used in radiology, this was a big haul.”    

They soon realized that they also needed to account for variations that existed across member practices.  For instance, in some practices MRIs of the spine are read by neuroradiologists, while in other practices they are interpreted by MSK radiologists.  “In cases like this, when an exam is interpreted by a radiologist with training in either subspecialty, it is considered to be a subspecialty reading,” Bohnert says. “We had to consider that radiologists could have acquired the same level of expertise for certain types of exams through different training pathways.” 

Dr. Bohnert and Polmanteer also needed to determine the subspecialty classification for each radiologist in SR.  Radiologists without a subspecialty focus or who regularly interpreted exams from multiple different subspecialties were categorized as general radiologists. “General radiologists provide significant value to practices too, since they are often able to fill practice gaps by working across multiple subspecialties,” Bohnert notes.

First Iteration

Initially participating practices have received data for their own groups, which Dr. Bohnert has found useful as a practice leader. “Looking within our group, it gives us a good measure of the areas where we are highly subspecialized and also quickly shows areas where we can do a better job,” he reports. Currently, Bohnert can see how much each individual radiologist is reading within his or her specialty within a given time period. “I can see that all of our neuroradiologists read more than 90% within their subspecialty,” Bohnert notes. “But there are other sections in our practice such as interventional where the percentage of interventional procedures is lower than I would expect it to be in other practices.”

Polmanteer is working on providing the subspecialty data in the context of SR benchmarks, which will provide Bohnert and other practice leaders insight into how their group compares to other SR member groups.  Having this global perspective will be helpful in rethinking how the group is organized and how the work can be distributed to optimize the use of subspecialization.  Says Bohnert: “These insights will be valuable in making both hiring decisions and internal practice decisions regarding how to best distribute the work to improve patient care.”

“The next iteration also will enable us to look by CPT code and by modality,” he continues. “We are still refining the data to make sure we have the CPT codes and the doctors categorized correctly. While it is already useful, we are not yet utilizing the data to its fullest extent.”

Improving the Value Equation

Ultimately, Bohnert envisions SR’s ability to aggregate subspecialty data in the community practice setting as a preliminary step in linking subspecialization to improved outcomes. “I believe that subspecialization in radiology provides better patient care, and hopefully this data will eventually help us confirm that,” he says.

As a neuroradiologist, Bohnert sees how increased subspecialization could reduce unnecessary hospital transfers, something he encounters routinely. “It happens all the time,” he notes. “There is a finding categorized as important by an outside radiologist who doesn’t have neuroradiology training, when in reality it is often minor and does not require the patient to be transferred. When you factor in the costs of transportation and the additional hospitalization, there will be big savings to the system if you can prevent those transfers by having a subspecialist reading those studies.”

Meanwhile, Bohnert is hoping someone in SR will step forward and take on the challenge. “I am not a researcher, and I don’t have the time to analyze all of the data,” he says. “But as we continue to acquire and refine the data and start looking at economics and outcomes, I definitely think we will be able to show the benefits of radiology subspecialization.”


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