The Year in Review: Radiology’s Wild 2021 Ride

Dec. 24, 2021
The specialty of radiology, after years sitting in something like “splendid isolation,” has now been pulled firmly into the vortex of cascading change that is impacting U.S. healthcare

Having covered radiology and imaging informatics for three decades, I can say that for the first two of those decades, I shared the (largely unspoken) view of many, that, of all the medical specialties, radiology was the least likely to undergo significant change. Certainly, during the long era of discounted fee-for-service payment in healthcare, it felt at times as though radiology would never fundamentally change. I remember one year at the RSNA Annual Conference (held annually at Chicago’s vast McCormick Place Convention Center), when one of the main pieces of news was that the average annual radiologist salary had dipped from $550,000 to $500,000 (a development that provoked more than a few comments). It appeared then that radiologists would continue forward in their “splendid isolation,” as members of one of the last specialties to be affected by the winds of change—if perhaps ever.

Well, fast-forwarding to the present moment, wow. Radiology is now one of the medical specialties being subjected to the greatest levels of change, along multiple dimensions. The demand for productivity alone, has shaken the profession. With the aging of the U.S. population and the explosion in chronic illness, the disease burden is increasing markedly, and the demand for diagnostic imaging has been growing year over year, along with a concomitant demand for reporting services, which is outstripping the ability of the current cohort of radiologists to keep up. As Marty Stempniak wrote in an article in Radiology Business that was published online on June 16, “America’s shortage of radiologists and other physician specialists could surpass 35,000 by 2034, according to a recently published analysis. Across all care segments, the number could climb as high as 124,000. Both aging and population growth are primary drivers of these shortages, with the 65-and-up segment projected to swell by more than 42% over the next decade. The findings are part of the Association of American Medical Colleges’ seventh annual analysis of physician supply and demand, published June 11. The report did not give a specific number for radiology alone, lumping the specialty together with anesthesiology, neurology, emergency medicine and addiction specialists. This segment is projected to see a shortfall of between 10,300 and 35,600 physicians by 2034. “Nonprimary care specialties” have the highest anticipated deficit (upward of 77,100), while “medical specialties” (such as cardiology, oncology and pulmonology) had the lowest (13,400).”

Not surprisingly, “Radiologists are under increasing pressure to read and interpret hundreds of images a day,” an Oct. 21, 2019 article published in Health IT Analytics, and sponsored by Pure Storage, stated, noting that “One study estimated that the average radiologist needs to interpret one image every 3–4 seconds in an 8-hour workday to meet workload demands. As reimbursement rates for image reviews are declining, the burden on radiologists to read and interpret more images is only increasing, forcing them to increase their daily productivity if they are to stay afloat. Image reading reimbursement rates can vary drastically depending on whether the read was a "limited" or "complete" and by the type of insurer. While the increased number of imaging reads is creating massive amounts of data for researchers to study, it is also leading many providers to feel burnt out as well as taking time away from patients. Instead of counseling patients on the results of their images and care options, radiologists are spending time in front of a computer.”

Not surprisingly, despite their initial massive resistance to the idea of adopting artificial intelligence (AI) and machine learning (ML) algorithms to support radiologic practice, most radiologist have at least begun to accept the reality that AI and ML will help them to keep up with the speed and productivity demands being placed on them by their referring-physician colleagues. I wrote extensively a few weeks ago about how very prominent AI/ML was at the RSNA Annual Conference this year. There were literally dozens of educational sessions that covered various aspects of AI/ML, from the strategic challenges to the operational and clinical issues, to the ethics of AI.

For example, as I wrote on Dec. 2, “If anything was true about RSNA 2021 this year, it was the omnipresence of both the term and the concept: “artificial intelligence” was everywhere, both in terms of educational sessions and in terms of the exhibit floor. A few years in, radiologists, health IT leaders, and other stakeholders are moving forward to create AI algorithms and use them for various purposes—tremendously diverse purposes, as it turns out, with complexities everywhere. One of the most stimulating panels at the conference,” I wrote, was on “The Business of AI in Radiology: A Cost, a Long-term Investment, or an Immediate Business Opportunity?”

That Nov. 30 panel, moderated by Paul J. Chang, M.D., a professor of radiology at the University of Chicago health system in Chicago. “Dr. Chang’s fellow panelists were Nina Kottler, M.D., M.S., associate medical director at the El Segundo, Calif.-based Radiology Partners national radiology group practice; Hari Trivedi, M.D., assistant professor of radiology and co-director of the HITI Lab at Emory University; Luciano Prevedello, M.D., M.P.H., of The Ohio State University Wexner Medical Center; and Mona G. Flores, M.D., global head of medical AI at the Santa Clara, Calif.-based NVIDIA Corporation. Dr. Flores appeared virtually, while everyone else was present in person at McCormick Place,” I wrote.

And I extensively quoted Dr. Kottler, who told the assembled audience that “I am in a private practice. We have a national onsite radiology practice. We have about 3,000 radiologists and do about 10 percent of the radiology in the U.S. We’ve invested in AI and, in one case, we created our own NLP [natural language processing] AI algorithm, in 2017, deployed it in 2018, have deployed it to about 1200 radiologists so far. We’ve developed an NLP platform. These tools are clinical tools. You have to go do training to work with it. Radiologists need feedback on how well they’re using it. The NLP tool has gone through millions of reports. We generally pilot something first. Our pilots are big because our practice is big. We did partner with a vendor, and have seven of their FDA-cleared algorithms. We have millions of exams going through their tool. Piloting another NLP algorithm that provides summary of findings.”

“Is it fair to say in your organization, the motivation was use case, clinical need, rather than hypothesis testing?” Chang asked. “That’s correct,” Kottler responded. “You have to have a need. And we went to a vendor to help us; they ended up joining us, and are now internal to us.”

And therein lies one of the truths that radiologists, clinician and non-clinician informaticists, and all the others involved in adoption AI in radiologic practice, are learning: that there is simply no “plug-and-play” when it comes to integrating AI algorithms into practice. It simply isn’t working out that way. Radiologists and those working with them are having to do their own individual-organizational research to determine what they need and want, what might work, and how to integrate any algorithms into the radiologic workflow. Will templates be adopted over time that will be able to be implemented widely? Certainly. But earlier experiments that involved simply collecting enormous amounts of data and dumping those data points into data lakes for potential use in diagnostic work, have proven to be failures. So, just as happened with population health management, the early days of AI adoption in radiology are going to continue to be experimental, and require a lot of collaborative work.

Meanwhile, the business imperatives are all shifting, too, and increasingly, radiologists are now working in very large group practices, many of which are doing remote-reading work. Given the fact that, unlike other medical specialists, radiologists can usually be located anywhere, the pressures to gain productivity, and the increasing demand for reads, are pushing radiologists into consolidating, either under the banner of larger and larger radiology groups, of larger and larger multispecialty groups, or of hospital system-employed groups. Some of the largest remote-read groups are now numbering in the hundreds of radiologists.

Arvind Vijayasarathi, M.D., MBA, M.P.H., and Noriko Salamon, M.D., Ph.D., gave a presentation in 2018 at the American College of Radiology 2018 annual meeting; both practice in the Department of Radiology at the David Geffen School of Medicine at UCLA. They noted in a PowerPoint presentation a number of factors involved in the current business activity, Historically, they noted, private-practice radiology groups have been organized and run by radiologists themselves. But both financial and operational pressures are changing things for practicing radiologists. On the financial side, declining outpatient reimbursement, the infrastructure required to comply with MACRA reporting guidelines, the large capital investment required to acquire new technology, and consolidation across other healthcare sectors, including payers, are all pressuring radiologists towards consolidation. On the operational side, they noted, there is increasing demand for “near-100-percent-subpecialty interpretation,” plus increasing demand both for after-hours reading, and for shorter turnaround times. All of those factors, they argued, have compelled more and more radiologists to consolidate. And, they noted, what’s relatively new has been the emergence of both private equity-backed and publicly traded radiology groups. They say a relatively small number of very large entities beginning to aggregate the market, whether those medical groups are radiology-specific or multispecialty groups.

Speaking at the 2019 annual meeting of the Radiology Business Management Association in Colorado Springs, Colo., Randal Roat, COO of the Palmetto, Fla.-based Strategic Radiology, a coalition of privately owned radiology groups, told the audience at that gathering that “Radiology is fragmented and there is opportunity in this industry, so capitalizing on this opportunity is a reason.” He said that fear, uncertainty and doubt, including the fear of missing out on capitalization opportunities, are causing some practices to choose to sell to a consolidator, and in some cases, a practice may not see another path. “For some very large practices that have scaled to a region, scaled to a state, the next evolutionary step might be scaling nationally,” he said. “They may feel they need some assistance, some business support, and some financial support.” And, he cited economic opportunity. “Everybody says, ‘Yeah, they want the money,’ and to an extent that is true,” Roat said.  “I also think there are a lot of other factors that go into this decision, and you can’t tie it just to one of them.”

And then there is the Appropriate Use Criteria Program (AUC) under the Protecting Access to Medicare Act of 2014 (PAMA). Though the financial penalties for non-compliance with the program have been delayed multiple times now, and appear to not be going into effect until Jan. 1, 2023, as of the date of this article, there is no question that radiologists are bracing for the potential impact of the full implementation of that program. In a fee-for-service-based payment system, radiologists would be incented to work even faster and produce more; but many are now employed by integrated health systems that are increasingly participating in value-based and even risk-based contracting programs. So there is an element there that remains fairly unpredictable.

In the end, radiologists are in fact now living through a time of rapid change. Faced with intensifying demands on their time and energy, shifting reimbursement, and the ongoing advancement of AI and machine learning tools, at the same time that organizational consolidation is impacting them at all levels, it’s clear that the world of radiology and diagnostic imaging is changing very quickly now, and there is far less certainty than there was perceived to be five years ago, or even two years ago. One can only imagine that 2022 and 2023 with bring further change. Radiology is no longer sitting in splendid isolation; just like the other medical specialties, its practitioners are living the whirlwind that is U.S. healthcare in the third decade of the 21st century.

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