Technologists in the Spotlight

Radiographers and technologists were at the center of this week’s radiology news cycle, as three unrelated pieces highlighted the crucial role radtechs play, the significant challenges they face, and the actions required to help them succeed.

CXR Call to Action – After finding that nearly half of their portable chest X-ray images were “problematic,” a team of Stony Brook physicians issued a “call to action” to better support radiology technologists. Analysis of 500 portable CXRs found 231 problematic exams (46.2%), which most commonly occurred during overnight shifts (48%), and often stemmed from patient positioning issues. 

  • A focus group featuring six technologist department managers led the authors to propose three additional RT resources: (1) creating ongoing training programs focused on patient positioning, (2) assigning nurses to assist technologists during exams, 3) tasking internal medicine residents with reviewing CXRs before they’re sent to radiologists.

Big Teams, Little Training – The UK’s Society of Radiographers highlighted radiography managers’ struggles with high workloads and insufficient training (n = 200), finding that many of these leaders directly manage over 20 or 30 employees (52% & 40%… yikes) and never received manager training from their hospital (45%).

  • The authors called these huge team sizes and lack of training a “gross miscalculation,” warning that it will cause managers to “undoubtedly fail in their duty of care to their staff,” especially considering that managers are often pulled into clinical duties due to understaffing.

Patient Safety’s Last Step – The WHO partnered with the ISRRT and ISR to emphasize radiographers and radtechs’ role as “the last step” in patient safety and medicine delivery, filling in gaps missed by radiation and magnetic safety experts. The collaborative webinar addressed radiographers/technologists’ responsibilities for ensuring safe contrast and radiopharmaceutical use, maintaining pediatric imaging best practices, and ensuring that pre-administration processes are complete before medication delivery. 

The Takeaway

We talk a lot about modality-based approaches to improve radtech efficiency and reduce team burnout, and those are surely needed. However, this week’s news cycle was a solid reminder of HR’s role in technologist performance and what’s at stake if techs aren’t properly supported, trained, and staffed.

Intelerad Becomes the Image Exchange Leader

Radiology took a giant step towards actually #ditchingthedisk last week with Intelerad’s acquisition of image exchange rival, Life Image. Here’s why this could be a big deal…

Exchange Leadership – Acquiring Life Image makes Intelerad the “clear medical image exchange market leader,” combining two of the top three exchange companies (the other is Nuance), and creating a far more straightforward roadmap towards building a “true nation-wide, electronic image exchange network.”

Demand & Supply – Although imaging vendors always position their acquisitions as patient or clinician-centric (even if it’s debatable), this move actually does address one of radiology’s most glaring problems — it’s far too difficult for providers to share images with each other if they don’t use the same exchange platform.

The Exchange Network Effect – Because the clinical value of image exchanges multiplies as vendor market share increases, Intelerad now has a network effect advantage that you almost never see in medical imaging. If this deal increased Intelerad’s image exchange share to 70% (hypothetically), it would make Intelerad far more valuable to its current clients and far more attractive to its remaining prospects.

Defining “Open” – The announcement alluded to the creation of an “open” image exchange, which is consistent with Ambra/Intelerad’s philosophy. However, it’s unclear how or when that will happen – or whether Nuance and other competitors will decide to join.

Intelerad = Acquirer – This deal also solidifies Intelerad’s title as imaging informatics’ most active acquirer, buying at least seven companies in the last two years that expanded it into new clinical areas (cardiac, OB/GYN), regions (UK), technologies (cloud), and functionalities (image sharing, reporting, cloud VNA). 

The Takeaway

Intelerad’s combined Ambra and Life Image acquisitions should make it the undisputed leader of the image exchange segment. That’s a big deal considering that the value of image exchange software multiplies as market share increases, and because it could actually allow Intelerad to solve (not just improve) one of radiology’s most frustrating challenges.

Echo AI Detects More Aortic Stenosis

A team of Australian researchers developed an echo AI solution that accurately assesses patients’ aortic stenosis (AS) severity levels, including many patients with severe AS who might go undetected using current methods.

The researchers trained their AI-Decision Support Algorithm (AI-DSA) using the Australian Echo Database, which features more than 1M echo exams from over 630k patients, and includes the patients’ 5-year mortality outcomes.

Using 179k echo exams from the same Australian Echo Database, the researchers found that AI-DSA detected…

  • Moderate-to-severe AS in 2,606 patients, who had a 56.2% five-year mortality rate
  • Severe AS in 4,622 patients, who had a 67.9% five-year mortality rate

Those mortality rates are far higher than the study’s remaining 171,826 patients (22.9% 5yr rate), giving the individuals that AI-DSA classified with moderate-to-severe or severe AS significantly higher odds of dying within five years (Adjusted odds ratios: 1.82 & 2.80).

AI-DSA also served as a valuable complement to current methods, as 33% of the patients that AI-DSA identified with severe AS would not have been detected using the current echo assessment guidelines. However, severe AS patients who were only flagged by the AI-DSA algorithm had similar 5-year mortality rates as patients who were flagged by both AI-DSA and the current guidelines (64.4% vs. 69.1%).

Takeaway

There’s been a lot of promising echo AI research lately, but most studies have highlighted the technology’s performance in comparison to sonographers. This new study suggests that echo AI might also help identify high-risk AS patients who wouldn’t be detected by sonographers (at least if they are using current methods), potentially steering more patients towards life-saving aortic valve replacement procedures.

Multimodal NSCLC Treatment Prediction

Memorial Sloan Kettering researchers showed that data from routine diagnostic workups (imaging, pathology, genomics) could be used to predict how patients with non-small cell lung cancer (NSCLC) will respond to immunotherapy, potentially allowing more precise and effective treatment decisions.

Immunotherapy can significantly improve outcomes for patients with advanced NSCLC, and it has already “rapidly altered” the treatment landscape. 

  • However, only ~25% of advanced NSCLC patients respond to immunotherapy, and current biomarkers used to predict response have proved to be “only modestly helpful.”  

The researchers collected baseline diagnostic data from 247 patients with advanced NSCLC, including CTs, histopathology slides, and genomic sequencing. 

  • They then had domain experts curate and annotate this data, and leveraged a computational workflow to extract patient-level features (e.g. CT radiomics), before using their DyAM model to integrate the data and predict therapy response.

Using diagnostic data from the same 247 patients, the multimodal DyAM system predicted immunotherapy response with an 0.80 AUC. 

  • That’s far higher than the current FDA-cleared predictive biomarkers – tumor mutational burden and PD-L1 immunohistochemistry score (AUCs: 0.61 & 0.73) – and all imaging approaches examined in the study (AUCs: 0.62 to 0.64).

The Takeaway

Although MSK’s multimodal immunotherapy response research is still in its very early stages and would be difficult to clinically implement, this study “represents a proof of principle” that integrating diagnostic data that is already being captured could improve treatment predictions – and treatment outcomes.

This study also adds to the recent momentum we’re seeing with multi-modal diagnostics and treatment guidance, driven by efforts from academia and highly-funded AI startups like SOPHiA GENETICS and Owkin.

CADx’s Lung Nodule Impact

A new JACR study highlighted Computer-Aided Diagnosis (CADx) AI’s ability to improve lung nodule malignancy risk classifications, while stating a solid case for the technology’s potential clinical role.

The researchers applied RevealDx’s RevealAI-Lung CADx solution to chest CTs from 963 patients with 1,331 nodules (from 2 LC screening datasets, and one incidental nodule dataset), finding that RevealAI-Lung’s malignancy risk scores (mSI) combined with Lung-RADS would significantly improve…

  • Sensitivity versus Lung-RADS-only (3 cohorts: +25%, +68%, +117%)
  • Specificity versus Lung-RADS-only (3 cohorts: +17%, +18%, +33%)

Looking specifically at the study’s NLST cohort (704 nodules), mSI+Lung-RADS would have…

  • Reclassified 94 nodules to “high risk” (formerly false-negatives)
  • Potentially diagnosed 53 patients with malignant nodules at least one year earlier
  • Reclassified 36 benign nodules to “low-risk” (formerly false-positives)

The RevealDx-based malignancy scores also achieved comparable accuracy to existing clinical risk models when used independently (AUCs: 0.89 vs. 0.86 – 0.88).

The Takeaway

These results suggest that a CADx lung nodule solution like RevealAI-Lung could significantly improve lung nodule severity assessments. Considering the clinical importance of early and accurate diagnosis of high-risk nodules and the many negatives associated with improper diagnosis of low-risk nodules (costs, efficiency, procedures, patient burden), that could be a big deal.

Viz.ai Adds PE Stratification

Viz.ai announced the FDA clearance of its new RV/LV ratio algorithm, adding an important risk stratification feature to its pulmonary embolism AI module, while representing an interesting example of how triage AI solutions might evolve.

Triage + Stratification + Coordination Viz PE becomes far more comprehensive with its new RV/LV integration, helping radiologists detect/prioritize PE cases and assess right heart strain (a major cause of PE mortality), while equipping PE response teams with more actionable information. 

  • This addition might also improve clinicians’ experience with Viz PE, noting the risk of developing AI “alert fatigue” when all severity levels are treated the same.

Viz.ai is So On-Trend – Signify Research recently forecast that AI leaders will increasingly expand into new clinical segments, enhance their current solutions, and leverage platform / marketplace strategies, as AI evolves from point solutions to comprehensive workflows. Those trends are certainly evident within Viz.ai’s recent PE strategy…

  • Viz PE’s late 2021 launch was a key step in Viz.ai’s expansion beyond neuro/stroke
  • Adding RV/LV risk stratification certainly enhances Viz PE’s detection capabilities
  • Viz PE was developed by Avicenna.AI, arguably making Viz.ai a platform vendor
  • Viz PE’s workflow now combines detection, assessment, and care coordination

The same could be said for Aidoc, which previously added Imbio’s RV/LV algorithm to its PE AI solution (and also supports incidental PE), although few other triage AI workflows are this advanced for PE or other clinical areas.

The Takeaway

Viz.ai’s PE and RV/LV integration is a great example of how detection-focused AI tools can evolve through risk/severity stratification and workflow integration — and it might prove to be a key step in Viz.ai’s expansion beyond stroke AI.

ACR Grants NPPs’ Contrast Supervision

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

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

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

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.”

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-- The Imaging Wire team