Month: August 2022

ACR Grants NPPs’ Contrast Supervision

Posted on by Jake Fishman

The American College of Radiology (ACR) rolled out a significant change to its imaging contrast guidelines, allowing non-radiologists and non-physician practitioners (NPPs) to supervise intravenous CT and MRI contrast administration at accredited imaging centers.

A range of NPPs (NPs, PAs, RNs) and qualifying non-radiologist physicians will be able to directly supervise contrast administration under the “general supervision” of on-site radiologists, as long as it’s supported by state scope of practice laws. 

  • Superving radiologists must be available for “assistance or direction” and trained to handle acute contrast reactions/situations, but they won’t have to be in the same room as the patient.

These guidelines mirror the ACR’s new practice parameters for contrast supervision (adopted in May), and follow CMS’ recent efforts to expand more diagnostic tasks to non-physicians.

  • CMS granted radiology assistants the ability perform a range of imaging tasks in 2020 and permitted NPPs to directly supervise Level 2 tests in 2021 (like contrast-enhanced CT and MRI), in both cases requiring “general” radiologist supervision (on-site, but not in room… and virtual during the pandemic).

Although NPPs’ radiology expansion has historically sparked heated debates, the new ACR contrast supervision guidelines hasn’t faced many public objections so far. 

  • That’s potentially because some (busy) radiologists don’t view directly supervising contrast administration as a practical or efficient use of their time (even if they still have to drive to the imaging center), especially considering that technologists often spot adverse reactions before anyone else.
  • However, there’s surely plenty of radiologists who are concerned about whether these new guidelines might exacerbate scope creep, cut their earning potential (especially trainees), reduce radiologists’ patient-facing opportunities, and undermine patient care.

The Takeaway

The ACR’s decision to grant NPPs greater contrast supervision rights and loosen radiologists’ contrast supervision requirements might not be surprising to folks paying attention to recent ACR and CMS policies. That said, it’s still a notable step (and potential contributor) in the NPPs’ expanding role within radiology – and opinions might differ regarding whether that’s a good thing.

Prioritizing Length of Stay

Posted on by Jake Fishman

A new study out of Cedars Sinai provided what might be the strongest evidence yet that imaging AI triage and prioritization tools can shorten inpatient hospitalizations, potentially bolstering AI’s economic and patient care value propositions outside of the radiology department.

The researchers analyzed patient length of stay (LOS) before and after Cedars Sinai adopted Aidoc’s triage AI solutions for intracranial hemorrhage (Nov 2017) and pulmonary embolism (Dec 2018), using 2016-2019 data from all inpatients who received noncontrast head CTs or chest CTAs.

  • ICH Results – Among Cedars Sinai’s 1,718 ICH patients (795 after ICH AI adoption), average LOS dropped by 11.9% from 10.92 to 9.62 days (vs. -5% for other head CT patients).
  • PE Results – Among Cedars Sinai’s 400 patients diagnosed with PE (170 after PE AI adoption), average LOS dropped by a massive 26.3% from 7.91 to 5.83 days (vs. +5.2% for other CCTA patients). 
  • Control Results – Control group patients with hip fractures saw smaller LOS decreases during the respective post-AI periods (-3% & -8.3%), while hospital-wide LOS seemed to trend upward (-2.5% & +10%).

The Takeaway

These results were strong enough for the authors to conclude that Cedars Sinai’s LOS improvements were likely “due to the triage software implementation.” 

Perhaps more importantly, some could also interpret these LOS reductions as evidence that Cedars Sinai’s triage AI adoption also improved its overall patient care and inpatient operating costs, given how these LOS reductions were likely achieved (faster diagnosis & treatment), the typical associations between hospital long stays and negative outcomes, and the fact that inpatient stays have a significant impact on hospital costs.

Radiology’s Nonphysician Service Expansion

Posted on by Jake Fishman

A new Harvey L. Neiman study showed that the recent expansion of nonphysician practitioners (NPPs) across US radiology practices coincided with similar increases in NPP-billed services — services that have traditionally been performed and billed by radiologists.

The Study – Researchers reviewed 2017-2019 data for Medicare claims-submitting nurse practitioners and physician assistants (together “NPPs”) who were employed by US radiology practices, finding that:

  • The number of radiology-employed NPPs who submitted claims increased by 16.3% between 2017 and 2019 (523 to 608 NPPs), while the number of US radiology practices that employed claims-submitting NPPs jumped by 14.3% (196 to 224 practices)
  • This NPP service expansion was driven by clinical evaluation and management services (E&M; +7.6% to 354), invasive procedures (+18.3% to 458), and image interpretation services (+31.8% to 112).
  • Meanwhile, total NPP wRVUs increased by 17.3%, similarly driven by E&M services (+40% to 111k wRVUs), invasive procedures (+5.6% to 189k), and image interpretation (+74% to 8,850 wRVUs)
  • Some radiologists might be concerned that image interpretation saw the greatest NPP headcount and wRVU growth (see +31.8% & +74% stats above), although imaging only represented a small share of overall NPP wRVUs (2.9% in 2019), and 86.7% of NPP-submitted imaging services were for either DEXA scans or swallowing studies. 

The Takeaway

Although roughly 87% of radiology practices still don’t employ NPPs who submit Medicare claims (as of 2019 anyway), this study reveals a clear trend towards NPPs performing more billable procedures — including image interpretation. 

Given previous evidence of NPPs’ growing employment in radiology practices and the major role NPPs play within other specialties, this trend is very likely to continue, leading to more blended radiology teams and more radiologist concerns about the NPP ‘slippery slope.’

AI Crosses the Chasm

Posted on by Jake Fishman

Despite plenty of challenges, Signify Research forecasts that the global imaging AI market will nearly quadruple by 2026, as AI “crosses the chasm” towards widespread adoption. Here’s how Signify sees that transition happening:

Market Growth – After generating global revenues of around $375M in 2020 and $400M and 2021, Signify expects the imaging AI market to maintain a massive 27.6% CAGR through 2026 when it reaches nearly $1.4B. 

Product-Led Growth – This growth will be partially driven by the availability of new and more-effective AI products, following:

  • An influx of new regulatory-approved solutions
  • Continued improvements to current products (e.g. adding triage to detection tools)
  • AI leaders expanding into new clinical segments
  • AI’s evolution from point solutions to comprehensive solutions/workflows
  • The continued adoption AI platforms/marketplaces

The Big Four – Imaging AI’s top four clinical segments (breast, cardiology, neurology, pulmonology) represented 87% of the AI market in 2021, and those segments will continue to dominate through 2026. 

VC Support – After investing $3.47B in AI startups between 2015 and 2021, Signify expects that VCs will remain a market growth driver, while their funding continues to shift toward later stage rounds. 

Remaining Barriers – AI still faces plenty of barriers, including limited reimbursements, insufficient economic/ROI evidence, stricter regulatory standards (especially in EU), and uncertain future prioritization from healthcare providers and imaging IT vendors. 

The Takeaway

2022 has been a tumultuous year for AI, bringing a number of notable achievements (increased adoption, improving products, new reimbursements, more clinical evidence, big funding rounds) that sometimes seemed to be overshadowed by AI’s challenges (difficult funding climate, market consolidation, slower adoption than previously hoped).  

However, Signify’s latest research suggests that 2022’s ups-and-downs might prove to be part of AI’s path towards mainstream adoption. And based on the steeper growth Signify forecasts for 2025-2026 (see chart above), the imaging AI market’s growth rate and overall value should become far greater after it finally “crosses the chasm.”

Intelerad’s Reporting Play

Posted on by Jake Fishman

Intelerad continued its M&A streak, acquiring radiology reporting company, PenRad Technologies, in a relatively small deal that might have a much bigger impact than some think.

PenRad has a solid share of the breast and lung cancer screening reporting segments, making it a target of a number of PACS vendors in recent years.

The acquisition is another example of Intelerad using its private equity backing to complete its informatics portfolio, following a series of deals that allowed its expansions into new clinical areas (cardiac, OB/GYN), regions (UK), technologies (cloud), and functionalities (image sharing, cloud VNA).

Adding PenRad will immediately give Intelerad three proven cancer screening reporting solutions to offer to its PACS customers, while bringing Intelerad into an untold number of PenRad accounts that it didn’t work with before now. 

The deal’s long-term impact will likely be dictated by how well Intelerad integrates and enhances its new PenRad technologies. If Intelerad is able to seamlessly integrate its PACS/worklist with PenRad’s dictation/reporting, it could create a truly unique advantage — especially if Intelerad expands its reporting capabilities beyond just cancer screening. 

Intelerad’s PenRad acquisition and Sirona’s unified radiology platform also highlight the differentiating role that integrated reporting might play in future enterprise imaging portfolios, although there aren’t many more reporting companies still available for acquisition.

The Takeaway

Informatics veterans might point out that it’s much easier to acquire a portfolio of companies than it is to integrate all that software — and they’d be correct. That said, most would also agree that Intelerad has assembled a uniquely comprehensive enterprise imaging portfolio and it would be extremely well-positioned if/when that portfolio becomes fully integrated.

RadNet’s Bellwether Briefing

Posted on by Jake Fishman

RadNet’s investor briefings have come to serve as a medical imaging industry bellwether, and last week’s Q2 call lived up to that reputation, providing key insights into how RadNet is approaching imaging’s biggest trends (and by proxy, where its hospital partners, competitors, and vendors are also likely focusing).

Here are some of the big takeaways…

Hospital system joint ventures remain core to RadNet’s strategy, as payors’ outpatient emphasis helped RadNet expand hospital JV agreements to 29% of its imaging centers (vs. 25% in 2021). It’s targeting 50% in the next 2-3 years.

RadNet acquired three imaging centers in 2022, but much of its short-term imaging center growth will likely come from the 15 net new locations that are under construction.

A long list of headwinds (reimbursements cuts, labor shortages, access to capital, recession) could lead to greater imaging center market consolidation, and RadNet believes it’s better equipped to take advantage of a downturn than its competitors. 

RadNet forecasts that the tight imaging center labor market is “here to stay” and “needs to be addressed with technology.” Following that advice, RadNet highlighted its efficiency-focused moves to adopt MRI DLIR software, launch a remote MRI technologist management solution, and transition its eRAD PACS to the cloud.

RadNet’s AI strategy remains focused on cancer detection / diagnosis leadership and it still views AI extremely optimistically, although the briefing served as a helpful reminder of how early we are in AI’s evolution:

  • Q2 AI revenues reached just $1.5M (that’s including Aidence & Quantib), while heavy investments led to a -$5.9M pre-tax loss for the AI division.
  • RadNet is rolling-out its DeepHealth mammography AI solution through Q4 2022 or Q1 2023, calling the implementation’s installation and training requirements a “tall task” (and they developed it…).
  • Nonetheless, RadNet is confident that the mammo AI solution will deliver immediate benefits to its team’s accuracy, productivity (up to 15-20%), and imaging center scan volumes.
  • RadNet also installed its ` prostate MRI solution at select imaging centers that perform prostate cancer screening, although its overall prostate and lung cancer AI adoption will come later.

The Takeaway

The main takeaway from RadNet’s Q2 call likely depends on your role within imaging. That said, its statements and activities certainly suggest that the major imaging center companies will get larger and more JV-centric, there’s still plenty of reasons to be optimistic about AI (and to be patient with it), and the demand for technologies that solve imaging’s efficiency problems continues to grow.

A Case for VERDICT MRI

Posted on by Jake Fishman

A new Radiology Journal study showed that VERDICT MRI-based analysis could significantly improve prostate cancer lesion characterization, and might solve PCa screening’s unnecessary biopsy problem.

Before we jump into the study… VERDICT MRI (Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumor) is a novel diffusion MRI modeling technique that estimates microstructural tissue properties, and has shown promise for cancer diagnosis and assessments. It can also be performed using standard 3T MRI exams.

The UK-based researchers had 165 men with suspected prostate cancer undergo mpMRI and VERDICT MRI (73 later confirmed w/ significant PCa). Over the 3.5yr study, they found that VERDICT MRI-based ‘lesion fractional intracellular’ volumes (FICs) have significant characterization advantages versus mpMRI-based apparent diffusion coefficient and PSA density measurements (ADC & PSAD):

  • VERDICT MRI-based FICs classified clinically significant prostate cancer lesions far more accurately than ADC and PSAD (AUCs: 0.96 vs. 0.85 & 0.74). 
  • VERDICT-based FICs also clearly differentiated clinically insignificant and significant prostate cancer among the study’s Likert 3 lesions (median FICs: 0.53 & 0.18) and Likert 4 lesions (median FICs: 0.60 & 0.28), while ADC and PSAD measurements couldn’t be used to show which of these lesions would be cancerous. 

The Takeaway

Noting that up to 50% of men with positive PI-RADS scores or >3 Likert scores end up with negative biopsy results, these findings suggest that VERDICT MRI could reduce unnecessary prostate biopsies by a whopping 90%.

That makes this study a “massive leap forward” for prostate cancer diagnostics, and provides enough evidence to make VERDICT MRI just one successful large multi-center trial away from clinical adoption.

Prostate MR AI’s Experience Boost

Posted on by Jake Fishman

A new European Radiology study showed that Siemens Healthineers’ AI-RAD Companion Prostate MR solution can improve radiologists’ lesion assessment accuracy (especially less-experienced rads), while reducing reading times and lesion grading variability. 

The researchers had four radiologists (two experienced, two inexperienced) assess lesions in 172 prostate MRI exams, with and without AI support, finding that AI-RAD Companion Prostate MR improved:

  • The less-experienced radiologists’ performance, significantly (AUCs: 0.66 to 0.80 & 0.68 to 0.80)
  • The experienced rads’ performance, modestly (AUCs: 0.81 to 0.86 & 0.81 to 0.84)
  • Overall PI-RADS category and Gleason score correlations (r = 0.45 to 0.57)
  • Median reading times (157 to 150 seconds)

The study also highlights Siemens Healthineers’ emergence as an AI research leader, leveraging its relationship / funding advantages over AI-only vendors and its (potentially) greater focus on AI research than its OEM peers to become one of imaging AI’s most-published vendors (here are some of its other recent studies).

The Takeaway

Given the role that experience plays in radiologists’ prostate MRI accuracy, and noting prostate MRI’s historical challenges with variability, this study makes a solid case for AI-RAD Companion Prostate MR’s ability to improve rads’ diagnostic performance (without slowing them down). It’s also a reminder that Siemens Healthineers is serious about supporting its homegrown AI portfolio through academic research.

RevealDx & contextflow’s Lung CT Alliance

Posted on by Jake Fishman

RevealDx and contextflow announced a new alliance that should advance the companies’ product and distribution strategies, and appears to highlight an interesting trend towards more comprehensive AI solutions.

The companies will integrate RevealDx’s RevealAI-Lung solution (lung nodule characterization) with contextflow’s SEARCH Lung CT software (lung nodule detection and quantification), creating a uniquely comprehensive lung cancer screening offering. 

contextflow will also become RevealDx’s exclusive distributor in Europe, adding to RevealDx’s global channel that includes a distribution alliance with Volpara (exclusive in Australia/NZ, non-exclusive in US) and a platform integration deal with Sirona. 

The alliance highlights contextflow’s new partner-driven strategy to expand SEARCH Lung CT beyond its image-based retrieval roots, coming just a few weeks after announcing an integration with Oxipit’s ChestEye Quality AI solution to identify missed lung nodules.

In fact, contextflow’s AI expansion efforts appear to be part of an emerging trend, as AI vendors work to support multiple steps within a given clinical activity (e.g. lung cancer assessments) or spot a wider range of pathologies in a given exam (e.g. CXRs):

  • Volpara has amassed a range of complementary breast cancer screening solutions, and has started to build out a similar suite of lung cancer screening solutions (including RevealDx & Riverain).
  • A growing field of chest X-ray AI vendors (Annalise.ai, Lunit, Qure.ai, Oxipit, Vuno) lead with their ability to detect multiple findings from a single CXR scan and AI workflow. 
  • Siemens Healthineers’ AI-RAD Companion Chest CT solution combines these two approaches, automating multiple diagnostic tasks (analysis, quantification, visualization, results generation) across a range of different chest CT exams and organs.

The Takeaway

contextflow and RevealDx’s European alliance seems to make a lot of sense, allowing contextflow to enhance its lung nodule detection/quantification findings with characterization details, while giving RevealDx the channel and lung nodule detection starting points that it likely needs.

The partnership also appears to represent another step towards more comprehensive and potentially more clinically valuable AI solutions, and away from the narrow applications that have dominated AI portfolios (and AI critiques) before now.

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