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AI As Malpractice Safety Net | Late-Stage Breast Cancer
December 12, 2024
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Together with
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“The QA utilization of AI algorithms can save millions of dollars annually.”
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vRad CMO Benjamin Strong, MD, on the use of AI as a radiologist backstop.
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One of the emerging use cases for AI in radiology is as a safety net that could help hospitals avoid malpractice cases by catching errors made by radiologists before they can cause patient harm. The topic was reviewed in a Sunday presentation at RSNA 2024.
Clinical AI adoption has been held back by economic factors such as limited reimbursement and the lack of strong return on investment.
- Healthcare providers want to know that their AI investments will pay off, either through direct reimbursement from payors or improved operational efficiency.
At the same time, providers face rising malpractice risk, with a number of recent high-profile legal cases.
- For example, a New York hospital was hit with a $120M verdict after a resident physician working the night shift missed a pulmonary embolism.
Could AI limit risk by acting as a backstop to radiologists?
- At RSNA 2024, Benjamin Strong, MD, chief medical officer at vRad, described how they have deployed AI as a QA safety net.
vRad mostly develops its own AI algorithms, with the first algorithm deployed in 2015.
- vRad is running AI algorithms as a backstop for 13 critical pathologies, from aortic dissection to superior mesenteric artery occlusion.
vRad’s QA workflow begins after the radiologist issues a final report (without using AI), and an algorithm then reviews the report automatically.
- If discrepancies are found the report is sent to a second radiologist, who can kick the study back to the original radiologist if they believe an error has occurred. The entire process takes 20 minutes.
In a review of the program over one year, vRad found …
- Corrections were made for about 1.5k diagnoses out of 6.7M exams.
- The top five AI models accounted for over $8M in medical malpractice savings.
- Three pathologies – spinal epidural abscess, aortic dissection, and ischemic bowel due to SMA occlusion – would have amounted to $18M in payouts over four years.
- Adding intracranial hemorrhage and pulmonary embolism creates what Strong called the “Big Five” of pathologies that are either the most frequently missed or the most expensive when missed.
The Takeaway
The findings offer an intriguing new use case for AI adoption. Avoiding just one malpractice verdict or settlement would more than pay for the cost of AI installation, in most cases many times over. How’s that for return on investment?
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How to Standardize CT Images
The quality and appearance of CT scans can vary considerably. In this white paper from Riverain Technologies, find out how image normalization can standardize CT images, making them easier to analyze and interpret.
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The Growth of AI in Pulmonology
Learn more about the capabilities of AI for chest imaging in this on-demand webinar from Blackford. You’ll hear pulmonology professionals discuss several promising areas, from acute imaging through chest X-ray analysis to lung cancer screening.
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Unifying All Your Diagnostic Images
The Mach7 eUnity enterprise diagnostic viewer can unify all your images and solve your integration needs under one universal viewing platform. Find out how you can upgrade your enterprise visualization strategy today.
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- Late-Stage Breast Cancer Rises: There’s been a disturbing rise in metastatic breast cancer that correlates to mammography screening disruptions seen during the COVID-19 pandemic. In a paper in Radiology, researchers analyzed metastatic cancer incidence rates for 71M-80M women from 2004 to 2021, finding that rates rose 1.16% annually for all women, but even faster for younger women 20-39 years (2.91%). For women 40-74 in mammography’s target range, the metastatic cancer growth rate accelerated from 2.1% from 2004-2010 to 2.73% from 2018-2021 – most likely due to COVID screening disruptions.
- Lung Screening Is Cost-Effective: A modeling study out of Australia found CT lung cancer screening is cost-effective – an important finding as the country is set to launch a nationwide screening program next year. Researchers modeled a variety of different screening scenarios, ultimately finding that screening every two years for current and former smokers with histories more than 30 years prevented 62 lung cancer deaths per 100k population with 8.4 quality-adjusted life-years per prevented lung cancer death, at $40,420 (USD) per QALY (U.S. benchmark is $50,000).
- Riverain Gets CAC Clearance: Riverain Technologies is expanding into a new market niche after the FDA cleared its ClearRead CT CAC solution for coronary artery calcium analysis. The application is available as part of the company’s ClearRead CT suite and detects and quantifies CAC from ungated non-contrast chest CT scans. In other news, Riverain highlighted an RSNA 2024 study in which its ClearRead CT vessel suppression technology reduced interpretation times for pulmonary nodules on chest CT by 6.9 minutes for residents and 1.4 minutes for attending physicians.
- Support for Triple-Rule-Out Exams: A study in Academic Radiology supports triple-rule-out CT exams for detecting multiple emergent conditions. Researchers reviewed triple-rule-out scans from 2021-2022 for 1.3k patients, finding that 65% were at risk for two or more clinical conditions. Prevalence rates were 30% for obstructive CAD, 7.1% for acute pulmonary embolism, and 0.5% for acute aortic syndrome. After 30 days, 2.2% had acute myocardial infarction, 7% had acute PE, and 0.5% had acute aortic syndrome – suggesting the scans should be standard of care.
- Stationary CT Reduces Artifacts: In a Thursday presentation at RSNA 2024, researchers from China presented work on a prototype CT scanner they called stationary CT. Rather than a helical CT gantry rotating around the patient, the CompoundEye 24 stationary CT scanner from Nanovision Medical Technology uses two separate rings, one with 24 X-ray sources and the other with 10.2k detector channels in 288 rows. In tests on 50 patients, radiologists said ultra-high-resolution stationary CT images had better visibility of lung structures than helical CT.
- Visualizing CT Productivity: In a modern twist on the classic time-motion study of worker productivity, researchers in a paper at RSNA 2024 installed an NVIDIA AI supercomputer with a camera in a CT suite to record patient prep and scanning. A computer vision algorithm analyzed the time involved for each task, calculating a cycle time of 7.3 minutes per patient, broken down into preparation (2.01 minutes), scanning (3.65 minutes), and discharge (1.67 minutes). The estimates closely matched manual recordings and could be used to improve productivity.
- Qure Extends Erasmus Collaboration: Qure.ai has extended its collaboration with Erasmus Medical Center of the Netherlands. The extension includes a new research study that will investigate detecting and following up incidental pulmonary nodules using AI analysis of chest X-rays. Qure’s qXR algorithm has already gone into clinical use at Erasmus reviewing chest X-rays, and under the extension they will also implement Qure’s new qTrack lung nodule management program.
- Ziosoft Launches Viewer: Advanced visualization firm Ziosoft launched a new image viewer called Atrena as a complement to its Ziostation Revoras platform at last week’s RSNA 2024 conference. Atrena is a vendor-neutral solution for 3D stereoscopic viewing of DICOM images and is designed for improved image analysis for surgical and interventional radiological procedures. Atrena is commercially available in Japan and was shown as a work-in-progress at RSNA by Ziosoft, which was acquired last year by Japanese contrast company Nemoto.
- RADIN Partners with Ikonopedia: RADIN Health is partnering with Ikonopedia, which will integrate its breast imaging reporting and tracking software with RADIN’s cloud-based RIS, PACS, reporting, and workflow software. In other news, RADIN reported that teleradiology provider Total Medical Imaging has adopted the RADIN platform across its team of 50 subspecialized radiologists who read over 50k imaging exams a month.
- HAP Scores First Calif. Client: Healthcare Administrative Partners added Imaging Healthcare Specialists of San Diego to its portfolio of revenue cycle management clients. IHS operates nine sites in Southern California, and HAP will perform all core revenue cycle management services for the company, including billing, coding, carrier credentialing, business intelligence, and MIPS measure assurance services. IHS is HAP’s first customer in California.
- Vista’s CMR Success: Vista announced two-year clinical results showing that its FDA-cleared Vista Cardiac MRI image acquisition software significantly improved workflow efficiency and ensured high-quality CMR imaging consistency by automating MRI technologist tasks. Over the 26-month trial at Mass General Brigham, Vista Cardiac resulted in 26% faster scans, 50% reduction in scan time variability, and 50% more scan slots, enabling 900 additional scans without machine or personnel increases.
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Leading the Way in AI Transparency
There’s a need to better inform radiologists about AI’s role when interpreting images and generating measurements. Visage Imaging’s Visage 7 can display text in the viewer indicating that AI was used as a diagnostic aid – find out how it works today.
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Keep Patients Engaged with Your Healthcare System
After using PocketHealth, 94% of patients are more confident about their healthcare experience. Learn how to increase follow-up adherence, improve communication, and provide screening tools to keep your patients on track with PocketHealth Patient Connect.
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Revolutionizing Medical Imaging Data Management
Enlitic has acquired Laitek, and the combination creates new possibilities to revolutionize medical imaging data management. Learn more about Laitek and how its advanced migration services can benefit your radiology practice.
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- Fully Automated AI Echo vs. 3D and Human Readers: While 3D echo is becoming more accurate, 2D still dominates clinical care. A new study evaluates agreement in measures of LV volume and function between human readers, echo AI from Us2.ai, and the 3D Heart Model.
- Top-Tier Care at Rural Hospitals: Holzer Health System in Jackson, OH, treats local patients like family. In this video, learn how United Imaging equipped Holzer Health with its uMR 570 MRI scanner, helping them to offer top-tier care.
- Unprecedented Insights Made Possible with AI: With the largest normative dataset of whole-body imaging in the world, Prenuvo’s AI researchers partner with the best academic minds to understand – like never before – what “normal” aging means. Learn about their work today.
- Taking a Holistic Medical Imaging Approach: Blanchard Valley Health System in Ohio built a comprehensive holistic imaging strategy, focused on optimizing clinical workflows and easing user adoption. Learn how they did it with support from Merge in this white paper.
- Serving the Needs of Image-Driven Clinicians: TeraRecon serves the needs of image-driven clinicians and users across the entire healthcare enterprise. Schedule a demo today to explore the latest updates to industry-leading advanced visualization solutions and learn about new AI capabilities.
- Reach New Heights with Enterprise Imaging Cloud: Embrace the potential of AGFA HealthCare’s Enterprise Imaging Cloud, a fully managed Software-as-a-Service (SaaS) solution that will transform the handling, storage, and accessibility of medical imaging data. Learn how EI Cloud can help you today.
- Imaging Workflows that Actually Work: Not a fan of medical image exchange on discs? Then check out Clearpath and find out how it’s removing obstacles to better radiology workflow. Request a demo today.
- Presenting Unboxing AI: Check out CARPL’s video series, Unboxing AI, featuring experts discussing AI and its future in radiology. The next episode on December 13 features Rakesh Mullick of GE HealthCare – reserve your seat today.
- What Is Cloud Computing? What do you know about cloud computing and its role in healthcare? Check out this article by Sham Sokka, PhD, of DeepHealth to learn how cloud computing is helping hospitals adopt new AI technologies that enhance patient care.
- Quality in Photon-Counting CT: Quality is the cornerstone of Siemens Healthineers’ photon-counting CT technology. They’ve invested in every step of the process, from X-ray tubes to detectors and workflow. Discover how they strive for the highest levels of quality in photon-counting CT.
- Start at the Source to Improve MRI: Looking for ways to improve MRI speed and image quality while addressing broader concerns in healthcare? The answer may lie in proven MRI physics in your existing scanner – learn how to unlock it with STAGE from SpinTech MRI.
- LDCT Screening: To Scan or Not to Scan? Lung cancer accounts for more cancer deaths than prostate, ovarian, and breast cancer combined, but low-dose CT lung screening is catching on. Learn how tools like Intelerad’s InteleScreen can help you provide lung cancer screening with better nodule tracking and navigation.
- Feel the Freedom of Helium-Free MRI: Lift limitations and experience MRI excellence with Philips BlueSeal, the industry’s lightest, vent pipe-free, high-performance, helium-free 1.5T scanner. Save on helium and energy costs, achieve precise AI-enhanced diagnoses, enjoy faster scans, and optimized workflows. Learn more today.
- Automated Weight-Bearing Foot Measurements: Learn how Gleamer’s BoneMetrics AI software was able to provide automated measurements on weight-bearing radiographs with high levels of accuracy in a paper published in Skeletal Radiology.
- AI for Lung Cancer Diagnosis and Screening: Check out this comprehensive new eBook from Calantic by Bayer on the role of AI in lung cancer diagnosis and screening. It explores AI’s potential role in improving lung cancer screening strategies, identifying high-risk individuals, and enhancing diagnostic accuracy. Download it today.
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