When I was in training in the 2010s, there was a big push for sub-specialization. It was felt to be the future of radiology (and of course, everyone absolutely needed to do a fellowship). Observers opined that the days of the general radiologist were numbered because people needed fancier skills to deal with the increasingly complex and increasingly high-volume of complex imaging.
When the ABR ditched the original oral boards in favor of exclusively multiple-choice examinations, they pushed the final “Certifying Exam” until after fellowship and gave examinees the ability to select a portion of their testing content precisely because the idea was that everybody would be increasingly specialized, and therefore the test should accommodate that increasing specialization. (Never mind that the test was duplicative and useless—that tailoring was at least part of the attempt.)
The Flaw
One flaw in that logic is that increasing imaging volumes have increased imaging across the board. Yes, MRI and CT have disproportionately increased, but there are still plenty of plain films and ultrasounds and DEXA scans, and plenty of CTs are bread-and-butter work well within the skillset of the majority of radiologists. If everybody is so specialized and reads only in their fellowship—doing magical high-end imaging—then no one is left except the aging, near-retirement boomers to read a huge swath of high-volume, often low-RVU work. That is obviously not sustainable. The approach was inherently flawed for our times and has certainly contributed to the current shortage.
The Spectrum
Many discussions of generalist vs specialist are a false dichotomy in the sense that being generalized or specialized is more of a continuum than a binary. There are varying degrees of everything, and the shifting nature of radiology and the expectations of any given job mean that basic foundational skills can end up being important—even if they seem superfluous based on a very narrowly defined position that some radiologists, particularly in academia, find themselves in.
All points on the subspecialization continuum are available. 100% cross-sectional neuro-only? Yes. 100% subspecialized during regular weekday shifts with general radiology only on call (like evenings and weekends)? You bet. Mostly subspecialized with a daily shared pool of things like plain films? Totally. Mostly generalized with carve-outs for things like specific surgeon requests, small joint MRI, certain kinds of procedures, or breast imaging? That too. “General” may include breast imaging, or it may not.
Whatever way you think things are always done, you’re wrong. We have multiple ways to work in part because we have many different employers across 50 states, all trying to solve the question of how to best provide radiological care for patients. The fewer/larger employers we have, the fewer models we’ll continue to enjoy. (That’s one reason I like to support independent practices.)
Back to That Push for Subspecialization
There are several good reasons for increasing specialization. One is that proposed by the ivory tower: complex imaging demands greater skill, and people with more training and focus can theoretically (at least on average) provide higher-value and higher-quality care in those cases. It’s easier, on average, to be better at doing a small subset of the same things over and over again than trying to maintain a broad skillset as a jack of all trades. That narrow skillset can be brittle (all those body parts are squeezed into some tight real estate after all), but there are plenty of surgeons out there who essentially operate on one joint for the same reason.
Obviously, not every case requires marshaling our greatest diagnostic powers, but the reality is that you never know prospectively which cases do—or how to get them to the right person (please, please don’t invoke AI case assignment right now). And in many cases, retrospectively, we don’t know either. Plenty of subtle findings are missed for this reason. Radiology is the easiest field to Monday morning quarterback because the pictures are always there.
So we trade breadth for depth. This approach was once common only in academia but is now increasingly available in the broader market for several reasons—but in large part because people want it.
- In a tight job market, many practices have had to offer more subspecialization in order to land candidates. For one simple example, an academic neuroradiologist who hasn’t read a chest x-ray in 20 years may not be willing to fill your practice’s neuro needs if you make them start reading the other stuff. So the easiest way to recruit people who are already subspecialized is to offer subspecialization.
- Even many young people like the idea of specializing. When you spend a year of fellowship doing one thing over and over again, it’s easier to envision spending the rest of your career in a similar fashion. This can feel natural, especially since many people train in an academic environment where most attendings are similarly siloed.
- Certainly, to an extent, a job can be “easier” in many ways because you develop and evolve your crystallized skillset faster when you’re doing the same thing in higher volume. There’s comfort there—especially when we live in a world with productivity incentives and productivity metrics, where it’s easier to hit production numbers or deal with high call volumes if you’re able to work efficiently.
- Increasingly common productivity compensation models (e.g. flat $/RVU) encourage subspecialization because it’s easier to be fast and reasonably accurate doing a smaller number of things. This is especially true when your niche involves reading things that are higher-value, like mammograms, and you can make yourself immune to routine plain films and ultrasound. Yes, internal RVUs can mitigate some of the workload “benefits” of subspecialization, but that doesn’t change the true reimbursement value or the general nationwide trend.
Bigger Pie, Easier to Slice
Another nuance is that—thanks to regulatory demands, payor shenanigans, increasing workloads, quality bureaucracy, and recruiting/retention challenges—the increasing consolidation in the radiology space has itself enabled greater subspecialization.
A small group sharing a call burden means that everyone working alone on the weekend has to read whatever the hospital throws at them. But if multiple hospitals are consolidated into a shared worklist, then there’s enough volume and enough people working to divide out the work by subspecialty in ways that would previously have only been possible within academia.
Whereas previously fellowship training meant that the complicated cases (or the postoperative cases, or the MRIs, etc) went to the person who had done fellowship training and everything else was just shared equally, now it might mean that most if not all cases can be spread similarly.
People operating at the peak of their efficiency—which is, in many cases, more likely to occur when people have a narrow work focus—means that these large corporations, larger companies, and larger groups can also probably get more bang for their buck working with that strategy. Given the workforce shortage, any edge to getting the work done can be a big deal (also, it’s easier to squeeze a juicier fruit). For those rads in the gig economy, it’s also easier to earn a higher hourly rate when you’re reading what you can crank on.
All of this is why “body” imaging and general radiology are in such incredibly high demand—because we need people to do general radiology, especially when many radiologists have opted out.
Making General Work Pay
Long-term, this has some problems, not just because people want to practice at the “height” of their license and training, but because it’s easier to do a “full day’s work” (as measured in RVUs) reading MRIs than it is reading plain films. Adjusting the internal work values to account for the desirability of cases that nobody wants to do—the low-reimbursement, high-frustration, often tedious work of plain films and DEXA and ultrasounds—is one solution. But any change, even internally, means winners and losers. And everyone hates to lose.
The economic and spiritual degradation of general radiology has also meant that with fewer and fewer people really focusing on certain exam types, the quality of those interpretations has gone down, leaving the door open for mid-level encroachment or AI replacement of many tasks.
What Next?
The status quo isn’t going to last.
But the reality is, long-term, it’s impossible to know exactly where things will go, in part because we are at the jagged frontier of AI in radiology. It may be that the need for general radiology will continue to grow as people increasingly subspecialize and opt out of maintaining broad skills from training, older radiologists retire, and imaging volumes continue to explode.
Or, perhaps the hot job market (and fear of being inflexible in the coming AI world) will encourage some people to forgo fellowship and enough others to maintain broad skills to alleviate this pressing issue.
Or, it may be that those tasks—like ultrasounds and plain films—will be the easiest to satisfactorially offload and/or preliminary pre-draft reports from AI tools, such that we can better account for relatively low reimbursement while meeting the already acceptably low quality of those interpretations.
That being said, there’s no way to know how these tools and techniques will percolate through the broad swath of radiology tasks and radiology practices, and what radiologists’ responses to those changes will be, and what the payors responses to that utilization will be, and what the regulators will do when bad outcomes make the news, and so on and so on and so on—and therefore it’s impossible to know the ripple effects in the day to day or the broader workforce (and even later on, the radiology training pipeline).
Predictions are hard.
I would argue that, regardless of individual desires or quality differences, there are several regulatory and market forces that have pushed us toward consolidation that will be difficult to undo. And in a world of increasing consolidation, it is relatively easy to silo people into discrete boxes in ways that are not possible for small groups, especially when those people want to be siloed.
If small groups continue to thrive despite market pressures, then the model of general radiology will continue to survive.
Lastly, Fighting Automation Bias
One related question: as AI tools become more helpful, do we end up in a world where human beings must be extremely skilled in order to add value and countermand automation bias? If so, that may be the strongest and potentially most durable argument for sub-specialization.
A person who reads mostly normal brain MRIs here and there may not be able to function as an effective “liability operator” (or “sin eater“) for AI tools the same way that a subspecialized neuroradiologist could be. We’ve already seen in early trials that susceptibility to AI mistakes is experience-mediated.
So it does depend on how that dance plays out and how regulation plays a role in the implementation of AI tools going forward. There are several plausible outcomes (not to mention midlevel involvement if we can’t get our act together).
But, in the meantime, the willingness to do full-spectrum radiology is and will remain a desirable and valuable skill.
One Comment
Well said. Thanks.