Tag: radiologists

Teleradiology Malpractice Risk

Posted on by Brian Casey

A new study in Radiology comes to an explosive conclusion: medical malpractice cases involving teleradiology interpretation of medical images more frequently involved patient death and had higher payment amounts. 

Perhaps no technology has wrought greater changes on the field of medical imaging than teleradiology. 

  • By leveraging radiology’s conversion to digital imaging and the rapid expansion of Internet bandwidth, teleradiology makes it possible for medical images to be interpreted independent of the radiologist’s location, with studies sometimes literally sent around the world. 

But teleradiology has had its share of unintended consequences, such as the emergence of nighthawk and specialty teleradiology firms that have seized hospital contracts from traditional radiology groups. 

But this week’s study in Radiology adds a new wrinkle, suggesting that teleradiology could actually have an additional malpractice risk. Researchers analyzed 3,609 malpractice claims, of which 135 involved teleradiology, finding that teleradiology cases…

  • Saw patient death occur more often (36% vs. 20%)
  • More frequently saw communication problems among providers (26% vs. 13%)
  • More often closed with indemnity payments (59% vs. 41%)
  • Had higher median indemnity payments ($339k vs. $214k) 

Why might problems be more frequent in teleradiology? The authors offered several reasons, including …

  • Teleradiologists may not have access to EMR and other patient data
  • Teleradiology interpretations are often provided at night and on weekends/holidays
  • Claims involving neurology and the emergency setting were more common, illustrating the challenges in these areas

Potential solutions could involve making sure that teleradiologists have access to EMR data, and by performing overreads of interpretations delivered on nights and weekends. 

The Takeaway
The findings have disturbing implications, not only for dedicated teleradiology providers but also for traditional radiology practices that use teleradiology as part of their service offerings. And as noted in an accompanying editorial, they could provide ammunition to teleradiology’s opponents, who continue to rail against the technology that has done so much to change radiology. 

Imaging and US Healthcare Costs

Posted on by Brian Casey

In the debate over rising US healthcare costs, medical imaging is often painted as a bad guy. But a new study in Health Affairs Scholar claims that since 2010, spending on imaging services has not grown at the same rate as other medical services. 

It’s no secret that the US spends far more on healthcare per capita than other developed countries, spending 16.6% of GDP as of 2022 according to OECD data. 

  • For point of reference, Germany spends 12.7%, France spends 12.1%, and most other developed countries spend under 12% of GDP. 

Reasons why the US is such an outlier have been blamed on a variety of factors, such as pharmaceutical prices, physician salaries, administrative costs, and the fragmented nature of the US healthcare system. 

  • But medical imaging is often singled out for criticism, perhaps due to the high cost of scanners and the explosion of imaging volume since the advent of cross-sectional technologies like CT and MRI in the 1970s and 1980s. 

This has led the US government to exert major pressure on imaging reimbursement in the Medicare and Medicaid systems, starting with the Deficit Reduction Act of 2005 and continuing to the present day, while private insurers have employed tools like prior authorization. 

The new study indicates that these efforts may have accomplished their mission. Researchers from the ACR’s Harvey L. Neiman Health Policy Institute analyzed imaging’s contribution to overall growth of medical costs from 2010 to 2021 in employer-sponsored insurance plans, finding …

  • Spending on medical imaging grew 36% 
  • Spending for all other healthcare services grew 64% 
  • Two-thirds of the growth in imaging spending was due to general price inflation
  • Only one-fifth was due to increased utilization
  • Imaging’s share of total US healthcare spending fell from 10.5% to 8.9%

The findings indicate that efforts by the US government and private payors to drive down imaging utilization are working … but at the price of overworked radiology staff.

  • Imaging cuts could also be leading to patient access issues, as the study found that the percentage of patients undergoing imaging fell from 46% in 2010 to 40% in 2021. 

The Takeaway

The new study reinforces what imaging advocates have been saying for years – that medical imaging isn’t a major cause for runaway healthcare spending in the US. The question is whether anyone outside of radiology is listening.

AI Dominates at RSNA 2023

Posted on by Brian Casey

Take a deep breath. You survived another RSNA conference.

While a few hardy souls are still enjoying educational sessions in the cozy confines of McCormick Place, the final day of the exhibit floor yesterday marks the end of RSNA 2023 for most attendees. And what a show it was. 

Predictions were that AI would dominate the scientific sessions at RSNA 2023, a forecast that largely panned out. A November 28 session was a case in point, in which a series of top-quality papers were presented on one of the most promising use cases of AI, for breast screening:

  • A homegrown AI algorithm that analyzed screening breast ultrasound exams in addition to FFDM and DBT mammograms boosted sensitivity for detecting cancer in 12.5k patients, with better sensitivity for women with dense breasts (71% vs. 60%) and non-dense breasts (79% vs. 63%)
  • AI did a good job of detecting breast arterial calcification (BAC) when used prospectively to analyze screening mammograms in 16k women across 15 sites.  It found 15% of women had BAC, a possible marker for atherosclerotic disease
  • Swedish researchers used their VAI-B validation platform to compare three AI algorithms (Therapixel, Lunit, and Vara) in 34k women, finding that using AI with a single radiologist boosted sensitivity 10-30% compared to double reading, with a slight loss in specificity (2-7%). VAI-B could be used to validate AI implementation and guide purchasing decisions
  • Why does AI miss some breast cancers? South Korean researchers addressed this question by analyzing 1.1k patients with invasive cancers in which AI had a miss rate of 14%. Luminal cancers were missed most often
  • Adding AI analysis of prior images to current studies with FFDM and DBT boosted sensitivity for cancer detection in 30k patients, with sensitivity the highest for two years of priors compared to no priors (74% vs. 70%)

The Takeaway

This week’s research points to an exciting near-term future in which AI will help make mammography screening more accurate while helping breast radiologists perform their jobs more efficiently. Landmark studies toward this end were published in 2023 – this week’s RSNA conference shows that we can expect the momentum to continue in 2024. 

Welcome to RSNA 2023

Posted on by Brian Casey

It’s off to the races at RSNA 2023 as radiology’s showcase conference kicked off on Sunday. 

“Leading Through Change” is the theme of this year’s meeting, and it’s an appropriate slogan for a specialty that seems on the cusp of disruption with the growing use of AI, deep learning, and other tools. 

  • AI is being featured prominently in scientific presentations and vendor exhibits in McCormick Place, with a particular focus on whether large language models like ChatGPT can find practical application in radiology. Early research is promising but still inconclusive.

Another major focus at RSNA 2023 has been lung cancer screening, with Sunday afternoon sessions investigating how screening can be expanded: 

  • Researchers mined a database of 32k women who got screening mammography to find eligible candidates for lung screening, finding 5% who met screening criteria. 
  • Using the USPTSF’s 2021 guideline revision to find screening candidates led to shorter smoking histories (42 vs. 29 pack-years) and slightly more women being eligible (48% vs. 46%). 
  • ChatGPT gave more correct answers than Google Bard to non-expert questions on lung screening (71% vs. 52%).
  • ChatGPT, GPT-4, and Bard needed multiple iterations to produce reports readable by patients. 

AI is also proving its value for selecting screening candidates and identifying lung pathology: 

  • An AI algorithm analyzed chest X-rays to determine whether an individual would benefit from CT lung cancer screening – even if they don’t smoke. In 17.4k patients, the model classified 28% as high risk, 2.9% of whom were later diagnosed with lung cancer, a higher level than the 1.3% six-year threshold at which guidelines recommend CT lung screening.
  • A deep learning algorithm analyzed chest X-rays in a cohort of 10k patients to predict who would develop type 2 diabetes, turning in better accuracy than a model that only looked at clinical factors like age, BMI and HbA1c levels (AUCs:  0.84 vs. 0.79). 

Looking for more coverage of RSNA 2023? Be sure to check out our videos from the technical exhibit floor, which you can find on our new Shows page. 

The Takeaway
The RSNA has always been known as the Super Bowl of radiology, and this year’s meeting is off to a great start. Be sure to check back on our Twitter/X, LinkedIn, and YouTube pages for more coverage of this week’s events in Chicago.

Predicting the Future of Radiology AI

Posted on by Brian Casey

Making predictions is a messy business (just ask Geoffrey Hinton). So we’re always appreciative whenever key opinion leaders stick their necks out to offer thoughts on where radiology is headed and the major trends that will shape the specialty’s future. 

Two of radiology’s top thought leaders on AI and imaging informatics – Curtis Langlotz, MD, PhD, and Paul Chang, MD – gaze into the crystal ball in two articles published this week in Radiology as part of the journal’s centennial celebration. 

Langlotz offers 10 predictions on radiology AI’s future, briefly summarized below:

  • Radiology will continue its leadership position when it comes to AI adoption in medicine, as evidenced by its dominance of FDA marketing authorizations
  • Virtual assistants will help radiologists draft reports – and reduce burnout
  • Radiology workstations will become cloud-based cockpits that seamlessly unify image display, reporting, and AI
  • Large language models like ChatGPT will help patients better understand their radiology reports
  • The FDA will reform its regulation of AI to be more flexible and speed AI authorizations (see our article in The Wire below)
  • Large databases like the Medical Imaging and Data Resource Center (MIDRC) will spur data sharing and, in turn, more rapid AI development

Langlotz’s predictions are echoed by Chang’s accompanying article in Radiology in which he predicts the future of imaging informatics in the coming age. Like Langlotz, Chang sees the new array of AI-enabled tools as beneficial agents that will help radiologists manage growing workloads through dashboards, enhanced radiology reports, and workflow automation. 

The Takeaway

This week’s articles are required reading for anyone following the meteoric growth of AI in radiology. Far from Hinton’s dystopian view of a world without radiologists, Langlotz and Chang predict a future in which AI and IT technologies assist radiologists to do their jobs better and with less stress. We know which vision we prefer.

AI Experiences & Expectations

Posted on by Jake Fishman

The European Society of Radiology just published new insights into how imaging AI is being used across Europe and how the region’s radiologists view this emerging technology.

The Survey – The ESR reached out to 27,700 European radiologists in January 2022 with a survey regarding their experiences and perspectives on imaging AI, receiving responses from just 690 rads.

Early Adopters – 276 the 690 respondents (40%) had clinical experience using imaging AI, with the majority of these AI users:

  • Working at academic and regional hospitals (52% & 37% – only 11% at practices)
  • Leveraging AI for interpretation support, case prioritization, and post-processing (51.5%, 40%, 28.6%)

AI Experiences – The radiologists who do use AI revealed a mix of positive and negative experiences:

  • Most found diagnostic AI’s output reliable (75.7%)
  • Few experienced technical difficulties integrating AI into their workflow (17.8%)
  • The majority found AI prioritization tools to be “very helpful” or “moderately helpful” for reducing staff workload (23.4% & 62.2%)
  • However, far fewer reported that diagnostic AI tools reduced staff workload (22.7% Yes, 69.8% No)

Adoption Barriers – Most coverage of this study will likely focus on the fact that only 92 of the surveyed rads (13.3%) plan to acquire AI in the future, while 363 don’t intend to acquire AI (52.6%). The radiologists who don’t plan to adopt AI (including those who’ve never used AI) based their opinions on:

  • AI’s lack of added value (44.4%)
  • AI not performing as well as advertised (26.4%)
  • AI adding too much work (22.9%)
  • And “no reason” (6.3%)

US Context – These results are in the same ballpark as the ACR’s 2020 US-based survey (33.5% using AI, only 20% of non-users planned to adopt within 5 years), although 2020 feels like a long time ago.

The Takeaway

Even if this ESR survey might leave you asking more questions (What about AI’s impact on patient care? How often is AI actually being used? How do opinions differ between AI users and non-users?), more than anything it confirms what many of us already know… We’re still very early in AI’s evolution, and there’s still plenty of performance and perception barriers that AI has to overcome.

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