Tag: JACR

When Radiologists Quit

Posted on by Brian Casey

The chance that a radiologist would quit their job for a new one doubled over a recent 10-year period. And a new JACR study identifies the exact point in terms of case workload when radiologists are most likely to leave.

The burnout epidemic among healthcare professionals has been closely tied to workload, which has been rising steadily due to growing patient volumes and ongoing staff shortages.

  • In radiology, the problem has been exacerbated as radiologists are reading more images (from more complex cases) while the number of new radiologists being trained in residency programs remains static.

In the new paper, researchers from the ACR’s Neiman HPI investigated changes in radiologist turnover from 2013 to 2022 and how they compared with workload as measured by work relative value units, the most standard measure of physician productivity. 

  • They analyzed data on services provided by 39.4k unique radiologists representing 280.7k radiologist-years over the study period, then correlated that with data on how often radiologists changed practices.

Researchers found…

  • The radiologist turnover rate increased 61% (from 5.3% to 8.5%).
  • Odds of radiologist turnover were nearly 2X in 2022 versus 2013 (OR = 1.96).
  • And were 6% higher for female radiologists and 12% higher for metropolitan versus nonmetropolitan radiologists.
  • While academic radiologists had 9% lower turnover odds than nonacademic imagers.

But what about the connection between workload and turnover? This is where the study gets interesting, as the researchers found a U-shaped relationship between the two.

At low wRVU levels, turnover tended to drop as workload went up, perhaps as radiologists found more job satisfaction (and maybe higher pay) with more work to do.

But this changed once wRVUs hit a threshold, and turnover began rising as well, apparently as radiologists found themselves overworked. This inflection point differed for different types of radiologists…

  • Occurring at 12.9k wRVUs for all radiologists.
  • But at 13.4k wRVUs for private-practice radiologists.
  • And only 8.8k wRVUs for academic radiologists.

The 34% lower wRVU threshold for academic radiologists could be because many have prioritized research and teaching, and see a growing clinical care workload as a distraction without commensurate compensation. 

The Takeaway

The new study offers a fascinating look at the forces driving when and why radiologists quit, and provides a new benchmark showing precisely where the breaking point is for most radiologists. Let’s hope this data is put to good use.  

Managing Incidental Findings Isn’t Impossible

Posted on by Brian Casey

The number of incidental findings on medical imaging scans nearly quadrupled over nine years at a large academic medical center. That’s according to a new JACR analysis that fortunately offers strategies for following up on these unexpected imaging discoveries.

Incidental findings – defined as suspicious areas on medical images that aren’t related to a patient’s chief concern – comprise 15-30% of all medical imaging exams and are a growing challenge in radiology as imaging volume rises. 

  • Radiologists have a responsibility to include incidental findings in their reports, but who’s responsible for making sure patients know about them? 

Healthcare providers have adopted different methods for incidental follow-up, ranging from workflow changes to medical IT solutions.

In the current study, researchers from Northwestern University describe the incidental follow-up system they developed, which worked as follows…

  • An electronic button was embedded in the EMR for radiologists to click when an incidental finding was detected.
  • This relayed a note to the nursing team, which ensured that the patient’s care provider (or the patient themselves) knew about the finding.
  • The system required cases to be resolved when patients were notified of their findings and were told of the next steps to take.

In an analysis covering a total of 25.2k incidental findings from 2015 to 2023, researchers discovered… 

  • The number of findings grew at a compound annual growth rate of 21% with an average of 233 per month. 
  • Annual findings grew from 835 in 2015 to 4k in 2023 – a nearly 4X increase.
  • 99% of findings were resolved. 
  • Cases had to be resolved within seven months of the finding’s discovery.

One caveat is that Northwestern considered the loop closed once the patient was notified of the finding, rather than whether the patient complied with the recommendation.

  • A more robust protocol might involve additional longitudinal tracking to measure downstream effectiveness, which the authors note as a possibility for future research. 

The Takeaway

The new study underscores the stunning growth of incidental findings in radiology. But it also offers hope to imaging facilities through implementation of a simple IT fix and workforce changes that go a long way toward keeping patients notified of their imaging results.

AI for Bone Density Screening with X-Ray

Posted on by Brian Casey

Screening women for osteoporosis using AI analysis of chest X-rays acquired for other clinical indications meets U.S. thresholds for cost-effectiveness. That’s according to a new study in JACR that highlights the potential of radiography AI for opportunistic screening.

Osteoporosis screening is already performed using DEXA scanners that detect bone density loss in women.

  • But DEXA scanners aren’t always available, and dedicated screening for just one condition can be expensive. 

Using AI to analyze chest X-rays that women might be getting for other conditions could expand the pool of women being screened for osteoporosis without incurring significant additional costs.

  • Indeed, Japanese researchers recently published a study honing in on the best techniques for AI-enhanced osteoporosis screening with radiography.

In the new study, researchers performed a modeling analysis that simulated the cost-effectiveness of an osteoporosis screening program based on AI-enhanced chest radiographs for U.S. women aged 50 and up. 

  • The cost analysis compared osteoporosis screening plus treatment versus treatment alone, incorporating standard fracture treatment and imaging costs ($66 for DEXA scans, $20 for chest X-rays).

In a sample of 1k women, AI-enhanced X-ray osteoporosis screening…

  • Had an ICER of $72.1k per QALY, below the U.S. cost-effectiveness thresholds of $100k to $150k per QALY.
  • Would produce healthcare savings of $99k, offset by treatment costs of $208k.
  • Would prevent 2.8 fractures and increase QALYs by 1.5.
  • Would remain cost-effective as long as AI’s cost did not exceed $62 per patient.

Adjusting the model’s parameters produced even better performance for AI-based screening. 

  • If medication adherence improved by 50%, the ICER was reduced to $28.6k.

The Takeaway

The new research offers more support for opportunistic osteoporosis screening, this time perhaps from the most important angle of all: cost-effectiveness. If confirmed with other studies, AI-based bone density analysis could make routine chest X-rays even more valuable.

RadGPT Simplifies Radiology Reports for Patients

Posted on by Brian Casey

When it comes to informing patients of their imaging results, radiologists are caught between a rock and hard place. A new study in JACR shows how generative AI can help by drafting patient-friendly reports that are simple but accurate.

Patients must be informed immediately of their medical results according to a 2021 final rule under the 21st Century Cures Act that prevents medical information blocking. 

  • And while the technology exists to do that through tools like email and electronic patient portals, rapid notification can create confusion because the language physicians use to communicate with each other isn’t easily understood by anyone outside medicine.

Sure, radiology reports could be rewritten manually for patients, who typically read at about the eighth-grade level.

  • But given today’s workforce shortages, who’s going to do that?

Generative AI and large language models offer a solution. In the new JACR paper, researchers from Stanford University led by senior author Curtis Langlotz, MD, PhD, described their development of RadGPT, an LLM designed to improve patient communication.

  • To develop RadGPT, researchers started with OpenAI’s GPT-4 model and the RadGraph concept extraction tool to create an LLM that analyzes patient radiology reports and generates concept explanations and question-and-answer pairs.

How well did RadGPT work? The researchers tested it on 30 radiology reports generated at Stanford from 2012 to 2020, including different modalities and clinical applications. 

  • The LLM was asked to generate reports at a fifth-grade reading level (the level recommended by the Joint Commission for patient-facing healthcare materials).

Five radiology-trained physicians then rated the quality of RadGPT’s responses, finding …

  • The average rating of RadGPT-generated concept explanations was 4.8 out of 5.
  • 95% of concept explanations had an average rating of 4 or higher.
  • 50% of concept explanations were rated 5, the highest possible rating.
  • Questions and answers generated by RadGPT were also rated highly, with an average rating of 3.0 on a three-point scale..

The Stanford researchers told The Imaging Wire that their goal is to make RadGPT more widely available as part of a prospective evaluation with real patients.

  • They are also developing a user-friendly interface in which patients can receive hyperlinked radiology reports.

The Takeaway

RadGPT and solutions like it fill a desperate need for tools that can save time for radiologists while helping patients better understand their reports and get more engaged in their care. The next step is to get technology like this into the hands of practicing radiologists.

Imaging Workload Jumps with Higher Use of CT, MRI

Posted on by Brian Casey

Radiology’s shift to more advanced modalities like CT and MRI is increasing the burden on radiologists to interpret more complex exams. A new study in JACR documents the trend, finding that radiologist workload for inpatient imaging has risen sharply over the last 10 years. 

Like many physicians, radiologists are feeling burned out from rising patient workload, personnel shortages, and declining reimbursement. 

  • But radiology has the added burden of being one of healthcare’s most technology-focused specialties, with new imaging modalities giving them cooler tools to work with, but at the cost of steadily increasing exam complexity.

Researchers from Brigham and Women’s Hospital have been tracking inpatient imaging utilization for the past 40 years, and the new paper provides the latest update. 

  • They calculated inpatient imaging volume at Brigham and Women’s from 2012 to 2023, during which 896k imaging exams were performed.  

Results for the study were as follows …

  • Total annual inpatient imaging volume grew 17% over 10 years (102k to 119k exams).
  • Total imaging exams per patient admission (adjusted by case mix and disease severity) fell 20% due to declines in X-ray, ultrasound, and nuclear medicine.
  • But imaging exams per patient admission grew for CT (19%) and MRI (21%).
  • Leading to growth in CT and MRI’s combined share of all radiology global RVUs (62% to 75%).
  • Hospital length of stay rose 32% (5.6 to 7.4 days), possibly due to the COVID-19 pandemic. 

What does it all mean? Basically, the number of inpatient imaging exams per patient is declining when adjusted for disease severity, but radiologists are still having to work harder because the studies are more complex. 

  • Imaging could also be shifting from the inpatient setting to outpatient centers due to reimbursement changes aimed at shifting exams to lower-cost settings than hospitals.

One big question with the new study is the degree to which the COVID-19 pandemic skewed the results compared with previous years. 

  • The pandemic may have spurred more use of CT, especially given its value in providing a definitive diagnosis of SARS-CoV-2 infection. 

The Takeaway

If you feel like you’re working harder than ever, the new findings show that you’re not crazy. And given radiology’s breakneck pace of innovation, it’s not likely the trends revealed in the new study will let up any time soon.

Forecasting Radiologist Supply

Posted on by Brian Casey

Two new studies published this week in JACR raise the provocative question: Will there be a radiologist shortage in the future given growing demand for medical imaging services?

It’s a question that’s become commonplace across healthcare as burnout and other issues prompt many physicians to leave the field. 

  • This has caused workforce shortages that raise questions about whether the U.S. – and other advanced economies – will be able to meet growing demand for healthcare services by an aging population.

The new studies were conducted by Harvey L. Neiman Health Policy Institute researchers and each tackles one aspect of the supply/demand equation over the next 30 years. 

The first study analyzed past growth in the radiologist workforce to find …

  • There were 37.5k radiologists enrolled to provide care to Medicare patients in 2023. 
  • With no growth in the number of residency positions, there will be 47.1k radiologists in 2055, an increase of 26%.
  • If residency positions grow, there will be 52.6k radiologists, an increase of 40%.

The wildcard here is growth in residency positions, which are mostly controlled by Medicare through its graduate medical education program – and it literally takes an act of Congress to increase the number of trainee positions. 

  • Another factor is whether the higher physician attrition rate seen during the COVID-19 pandemic continues into the future. 

The second study addressed growth in imaging volume by analyzing trends in claims data for Medicare, Medicaid, and private insurance, finding …

  • Imaging utilization will be 17-27% higher by modality by 2055 assuming no continuation of recent utilization trends.
  • Most utilization growth will be seen in nuclear medicine (27%), CT (25%), interventional radiology (23%), X-ray (18%), and MRI and ultrasound (17% each).
  • Adding recent utilization trends to the model finds utilization by 2055 either -5.6% lower or up by 45%.

Factors affecting future utilization include population growth (73-88% of increase) and population aging (12-27%). 

The Takeaway

So will there be a radiologist shortage in the future? The new studies indicate that there are too many variables to make an accurate prediction right now. But they do provide a foundation for future research – and debate. 

Radiologist Salaries Lag Inflation

Posted on by Brian Casey

A new study in JACR confirms what many radiologists have suspected: salary growth for private-practice radiologists has lagged inflation over the last 10 years. While there were a few bright spots, the study mostly shows that radiologists are working harder for less pay. 

Radiology has long been one of the better-compensated medical specialties, often landing in the top 10 of disciplines with the highest average annual compensation. 

  • But radiology has also been a target for reimbursement cuts by the U.S. government as it tries to shift more Medicare and Medicaid payments to primary care practitioners.

As a result, previous studies have found that payments per Medicare beneficiary in radiology have actually declined. 

  • And another 2.83% cut is on the docket for 2025 unless Congress steps in before the end of the current legislative session to prevent cuts in the 2025 Medicare Physician Fee Schedule.

The new study analyzes radiologist compensation based on MGMA salary survey data from 2014 to 2023. 

  • Researchers compared salaries for both diagnostic and interventional radiologists, and also between private-practice and academic radiologists. 

Based on the data, they found …

  • Diagnostic radiologists saw median total compensation grow over the survey period, but at a faster rate for academic radiologists (32% vs. 18%). 
  • Academic radiologists enjoyed faster annualized salary growth (3.2% vs. 1.9%) and had an edge after adjustment for inflation (+0.3% vs. -1%).
  • Work RVUs (a measure of productivity) also grew but at a slightly higher rate for academic radiologists (21% vs. 20%). 
  • Interventional radiologists saw higher salary growth for both non-academic and academic physicians (41% and 35%). 

The findings indicate that the traditional salary gap between private-practice and academic radiologists may be narrowing.

  • The growth in wRVUs in a time of stagnant or declining salaries after inflation adjustment may confirm the suspicions of both types of radiologists: that they are working harder for less pay. 

The Takeaway

The findings could be a gut punch for private-practice diagnostic radiologists, who are finding that their salary gains aren’t keeping pace with inflation (sound familiar?). They also suggest that academic radiology could offer a refuge from the market and government forces that are reshaping the private sector.

Do Imaging Costs Scare Patients?

Posted on by Brian Casey

A new study in JACR reveals an uncomfortable reality about medical imaging price transparency: Patients who knew how much they would have to pay for their imaging exam were less likely to complete their study. 

Price transparency has been touted as a patient-friendly tool that can get patients engaged with their care while also helping them avoid nasty billing surprises for out-of-pocket costs. 

  • Price transparency is considered to be so important that CMS in 2021 implemented rules requiring hospitals to disclose their standard charges online, as well as post a user-friendly list of their services that includes prices. 

But given that the rules were implemented relatively recently, not much is known about how they might affect patient behavior, such as compliance with recommended follow-up imaging exams.

  • Indeed, a recent study by some of the same authors found that patients are largely unaware of how much their imaging exams will cost them. 

So researchers analyzed data from two previously published studies of patients who either completed or were scheduled for outpatient imaging exams in Southern California. 

  • Patients were asked if they had been told how much their exam would cost them out-of-pocket when they scheduled it. 

Of the 532 patients who were surveyed, researchers found …

  • Only 15% said they knew about their out-of-pocket costs before their imaging exam. 
  • Fewer patients who completed their exams knew their costs compared to those who canceled (12% vs. 22%).
  • Patients who knew their costs were 67% less likely to complete their appointment than those who didn’t (OR=0.33).

So what’s the solution? The researchers suggested that healthcare providers may need to take a more proactive approach to disclosing price information to patients.

  • One possibility would be to integrate pricing discussions into patient-provider communications when ordering imaging exams, rather than relying on patients to seek pricing information on their own. 

The Takeaway

The findings show that medical imaging price transparency is more complicated than just posting a list of prices online and expecting patients to do the rest of the work. Imaging providers may need to get more involved in pricing discussions – the question is whether many of them are ready for it.

Study Shows AI’s Economic Value

Posted on by Brian Casey

One of the biggest criticisms of AI for radiology is that it hasn’t demonstrated its return on investment. Well, a new study in JACR tackles that argument head on, demonstrating AI’s ability to both improve radiologist efficiency and also drive new revenues for imaging facilities. 

AI adoption into radiology workflow on a broad scale will require significant investment, both in financial cost and IT resources. 

  • So far, there have been few studies showing that imaging facilities will get a payback for these investments, especially as Medicare and private insurance reimbursement for AI under CPT codes is limited to fewer than 20 algorithms. 

The new paper analyzes the use of an ROI calculator developed for Bayer’s Calantic platform, a centralized architecture for radiology AI integration and deployment. 

  • The calculator provides an estimate of AI’s value to an enterprise – such as by generating downstream procedures – by comparing workflow without AI to a scenario in which AI is integrated into operations.

The study included inputs for 14 AI algorithms covering thoracic and neurology applications on the Calantic platform, with researchers finding that over five years … 

  • The use of AI generated $3.6M in revenue versus $1.8M in costs, representing payback of $4.51 for every $1 invested
  • Use of the platform generated 1.5k additional diagnoses, resulting in more follow-up scans, hospitalizations, and downstream procedures
  • AI’s ROI jumped to 791% when radiologist time savings were considered
  • These time savings included a reduction of 15 eight-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time  

Although AI led to additional hospitalizations, it’s possible that length of stay was shorter: for example, reprioritization of stroke cases resulted in 264 fewer hospital days for patients with intracerebral hemorrhage. 

  • Executives with Bayer told The Imaging Wire that while the calculator is not publicly available, the company does use it in consultations with health systems about new AI deployments. 

The Takeaway

This study suggests that examining AI through the lens of direct reimbursement for AI-aided imaging services might not be the right way to assess the technology’s real economic value. Although it won’t settle the debate over AI’s economic benefits, the research is a step in the right direction.

Predicting Patient Follow-Up for Imaging Exams

Posted on by Brian Casey

There’s nothing more frustrating than patients who don’t comply with follow-up imaging recommendations. But a new study in JACR not only identifies the factors that can lead to patient non-compliance, it also points the way toward IT tools that could predict who will fall short – and help direct targeted outreach efforts.

The new study focuses specifically on incidental pulmonary nodules, a particularly thorny problem in radiology, especially as CT lung cancer screening ramps up around the world.

  • Prevalence of these nodules can range from 24-51% based on different populations, and while most are benign, a missed nodule could develop into a late-stage lung cancer with poor patient survival. 

Researchers from the University of Pennsylvania wanted to test a set of 13 clinical and socioeconomic factors that could predict lack of follow-up in a group of 1.6k patients who got CT scans from 2016 to 2019. 

  • Next, they evaluated how well these factors worked when fed into several different types of homegrown machine learning models – precursors of a tool that could be implemented clinically – finding …
  • Clinical setting had the strongest association in predicting non-adherence, with patients seen in the inpatient or emergency setting far more likely skip follow-up compared to outpatients (OR=7.3 and 8.6)
  • Patients on Medicaid were more likely to skip follow-up compared to those on Medicare (OR=2)
  • On the other hand, patients with high-risk nodules were less likely to skip follow-up compared to those at low risk (OR=0.25) 
  • Comorbidity was the only one of the 13 factors that was not predictive of follow-up 

The authors hypothesized that the strong association between clinical setting and follow-up was due to the different socio-demographic characteristics of patients typically seen in each environment. 

  • Patients in the outpatient setting often have access to more resources like health insurance, transportation, and health literacy, while those without such resources often have to resort to the emergency department or hospital wards when they become sick enough to require care.

In the next step of the study, the data were fed into four types of machine learning algorithms; all turned in good performance for predicting follow-up adherence, with AUCs ranging from 0.76-0.80. 

The Takeaway

It’s not hard to see the findings from this study ultimately making their way into clinical use as part of some sort of commercial machine-learning algorithm that helps clinicians manage incidental findings. Stay tuned.

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