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Radiology AI’s ROI Mismatch | Cleerly Effective
February 23, 2022
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Together with
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“The challenge is matching the ‘return’ to the ‘investor.’ When these two parties are mismatched, the cost justification for one group’s investment for another group’s benefit rarely occurs.”
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Emory’s Hari Trivedi MD explaining radiology AI’s economic incentive problem.
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A thought-provoking JACR editorial by Emory’s Hari Trivedi MD suggests that AI’s slow adoption rate has little to do with its quality or clinical benefits, and a lot to do with radiology’s misaligned incentives.
After interviewing 25 clinical and industry leaders, the radiology professor and co-director of Emory’s HITI Lab detailed the following economic mismatches:
- Private Practices value AI that improves radiologist productivity, allowing them to increase reading volumes without equivalent increases in headcount. That makes triage or productivity-focused AI valuable, but gives them no economic justification to purchase AI that catches incidentals, ensures follow-ups, or reduces unnecessary biopsies.
- Academic centers or hospitals that own radiology groups have far more to gain from AI products that detect incidental/missed findings and then drive internal admissions, referrals, and procedures. That means their highest-ROI AI solutions often drive revenue outside of the radiology department, while creating more radiologist labor.
- Community hospital emergency departments value AI that allows them to discharge or treat emergency patients faster, although this often doesn’t economically benefit their radiology departments or partner practices.
- Payor/provider health systems (e.g. the VA, Intermountain, Kaiser) can be open to a broad range of AI, but they especially value AI that reduces costs by avoiding unnecessary tests or catching early signs of diseases.
The Takeaway
People tend to paint imaging AI with a wide brush (AI is… all good, all bad, a job stealer, or the future) and we’ve seen a similar approach to AI adoption barrier editorials (AI just needs… trust, reimbursements, integration, better accuracy, or the killer app). However, even if each of these adoption barriers are solved, it’s hard to see how AI could achieve widespread adoption if the groups paying for AI aren’t economically benefiting from it.
Because of that, Dr. Trivedi encourages vendors to develop AI that provides “returns” to the same groups that make the “investments.” That might mean that few AI products achieve widespread adoption on their own, but a diverse group of specialized AI products achieve widespread use across all radiology settings.
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The AIR Recon DL Revolution
Check out this Imaging Wire Show featuring GE Healthcare’s US & Canada MRI leader, Brian Murphy, discussing MRI’s evolution and how AIR Recon DL is eliminating MRI’s signal, speed, and resolution compromise.
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- COVID’s Mobile X-Ray Radiation Impact: The COVID pandemic led to significantly more mobile X-ray exams, but it didn’t significantly increase radiographers’ radiation exposure. That’s from a new study of two large Australian hospitals, finding that their mobile X-ray use increased by 1.7-fold during the pandemic but their radiographers’ personal radiation dose exposure monitors showed no significant increases from 2019 to 2020.
- Cleerly’s AI Stenosis Evaluation: A new JACC study stated a solid case for Cleerly’s coronary CTA AI solution, showing that it can accurately identify or exclude high-grade stenosis. The researchers used Cleerly to analyze Coronary CTAs from 303 patients, accurately identifying patients with ≥50% and ≥70% stenosis (94% & 94% sensitivity, 68% & 82% specificity). Cleerly AI and core lab-interpreted quantitative CTA results detected stenosis with high correlation (per-patient & per-vessel both = 0.73) and predicted FFR <0.8 with similar accuracy (86.2% & 85%), although Cleerly achieved these results with a 10.3-minute average turnaround time.
- NHS’s CDC Staffing Problem: NHS England’s plans to improve imaging backlogs by opening over 160 community diagnostic centers (CDCs) across the country is well intended, but these CDCs might not be well staffed. The NHS revealed that the new CDCs will staff 6,000 imaging professionals (3,500 radiographers, 2,000 rads, 500 advanced practitioners), which might require poaching them from already short-staffed NHS hospitals.
- Riverain’s MDRs: Riverain Technologies joined the exclusive group of AI vendors to receive Europe’s more-demanding Medical Device Regulations (MDR) certification, achieving MDRs for all of its products. The EU launched its MDR certification program last year, requiring healthcare AI products to attain higher risk classifications (IIa, IIb, or III… no longer class I) and provide far more evidence, setting a 2024 deadline for existing products. Although this might be more relevant for European readers, it’s still significant in the US given the widespread belief that the FDA’s AI regulatory standards have room for improvement.
- Lumbar Spine MRI Non-Transparency: A new Harvey L. Neiman study revealed that just 50% of US hospitals that offer lumbar spine MRI are compliant with CMS’s cost transparency mandate (n = 523), while hospitals with high patient ratings are 70% more likely to be compliant. The study also showed that lumbar spine MRI costs can vary by up to 50x between different hospitals, but there’s no correlation between hospital quality metrics and lumbar spine MRI costs, suggesting that quality metrics should be added to transparency tools.
- Theragnostics NephroScan FDA: Theragnostics announced the FDA clearance of its NephroScan kidney nuclear imaging kit, which will be distributed by GE Healthcare (GE also distributes Theragnostics’ other agents/tracers). NephroScan supports the preparation of the Tc-99m DMSA radiopharmaceutical agent, which is used to assess kidney function and defects in SPECT, planar, and pinhole nuclear imaging exams.
- Scanned Report Follow-Ups: A new JAMIA study detailed an NLP model that’s able to automatically identify abnormalities in scanned radiology reports, potentially improving follow-up rates. The UTHealth team developed the ClinicalBERT-based pipeline on existing typed/dictated reports, and tested it against mammography, chest CT, and long-bone X-ray reports (n = 393, 305, 683). The NLP model identified abnormal reports with far higher F1 scores than a baseline string-matching approach (0.900, 0.905, 0.817 vs. 0.667).
- Gradient Health’s $2.5M: Image labeling startup Gradient Health closed a $2.5M funding round that the company will use to establish its position on the “picks and shovels” side of AI, alongside Centaur Labs and a growing field of international providers. Gradient Health maintains a 300M+ global medical image database and a team of radiologist labelers, streamlining the labeling step for AI developers and researchers.
- Half of Radiologists Are Burned Out: Medscape’s new burnout survey (n = 13k physicians, 300 rads) revealed that 49% of radiologists report being burned out (7th most), with the highest burnout levels among female radiologists (65% vs. 44% male). COVID appears to have exacerbated radiologist burnout (55% reported increased burnout since early pandemic), while radiologists’ top burnout drivers included a lack of professional respect (60%), overwork (50%), and a lack of control/autonomy (47%).
- AMRA’s Body Composition MRI 510(k): AMRA Medical announced the FDA clearance and MDEL licensing of its MRI-based BCP Scan (Body Composition Profile) assessment solution. BCP Scan analyzes muscle and fat biomarkers, producing body composition reports (e.g. muscle, fat, fat liver volume measurements) to help clinicians guide their patients’ overall health and wellness. That’s a very different use case than most solutions we cover, and it’s an interesting example of how imaging might be expanding.
- SMS Preferences: A new Intrado Healthcare survey of members of the College of Healthcare Information Management Executives found that 77% of respondents are investing in patient portals, while only 50% plan to invest in text message systems despite SMS being patients’ preferred communication method. The report stated that an over-reliance on standalone patient portals is “hindering healthcare efforts to connect with patients,” and that SMS systems could lead to higher engagement.
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Ramapo Radiology’s Case for Novarad CryptoChart
See how New Jersey’s Ramapo Radiology Associates overcame their CD burning problems and improved their physician and patient experiences with Novarad CryptoChart.
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- Variable heart rates and organ motion can make cardiac imaging a challenge for CT technologists. Discover how intelligent imaging guidance with Siemens Healthineers’ myExam Companion can help overcome these challenges, without compromising quality and consistency.
- The USPSTF guidelines for lung cancer screening were updated in May 2021, and driving compliance to such guidelines is a long, slow, repetitive process. Because of that, the Riverain team put together a kit to help hospitals and imaging centers educate either referring physicians or patients on the new guidelines either via branded tools or through the media.
- With radiation dose management now largely considered best practice, this Bayer white paper details the top five benefits of adopting contrast dose management.
- Do your radiologists want faster and less manual access to imaging studies? See how the Indiana Health Information Exchange (IHIE), the largest inter-organizational clinical data repository in the US, cut its imaging study retrieval time by 94% when it adopted Nuance PowerShare.
- Creating your AI adoption plan? This Arterys report details what clinical, efficiency, and regulatory factors to look for in radiology AI vendors.
- Change Healthcare’s cloud-native, zero-footprint Stratus Imaging PACS is now live in clinical use. See how Stratus Imaging PACS is helping radiology practices improve productivity and patient care, while eliminating the cost and resource constraints of on-premise systems.
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