When AI Goes Wrong

What impact do incorrect AI results have on radiologist performance? That question was the focus of a new study in European Radiology in which radiologists who received incorrect AI results were more likely to make wrong decisions on patient follow-up – even though they would have been correct without AI’s help.

The accuracy of AI has become a major concern as deep learning models like ChatGPT become more powerful and come closer to routine use. There’s even a term – the “hallucination effect” – for when AI models veer off script to produce text that sounds plausible but in fact is incorrect.

While AI hallucinations may not be an issue in healthcare – yet – there is still concern about the impact that AI algorithms are having on clinicians, both in terms of diagnostic performance and workflow. 

To see what happens when AI goes wrong, researchers from Brown University sent 90 chest radiographs with “sham” AI results to six radiologists, with 50% of the studies positive for lung cancer. They employed different strategies for AI use, ranging from keeping the AI recommendations in the patient’s record to deleting them after the interpretation was made. Findings included:

  • When AI falsely called a true-pathology case “normal,” radiologists’ false-negative rates rose compared to when they didn’t use AI (20.7-33.0% depending on AI use strategy vs. 2.7%)
  • AI calling a negative case “abnormal” boosted radiologists’ false-positive rates compared to without AI (80.5-86.0% vs. 51.4%)
  • Not surprisingly, when AI calls were correct, radiologists were more accurate with AI than without, with increases in both true-positive rates (94.7-97.8% vs. 88.3%) and true-negative rates (89.7-90.7% vs. 77.3%)

Fortunately, the researchers offered suggestions on how to mitigate the impact of incorrect AI. Radiologists had fewer false negatives when AI provided a box around the region of suspicion, a phenomenon the researchers said could be related to AI helping radiologists focus. 

Also, radiologists’ false positives were higher when AI results were retained in the patient record versus when they were deleted. Researchers said this was evidence that radiologists were less likely to disagree with AI if there was a record of the disagreement occurring. 

The Takeaway 
As AI becomes more widespread clinically, studies like this will become increasingly important in shaping how the technology is used in the real world, and add to previous research on AI’s impact. Awareness that AI is imperfect – and strategies that take that awareness into account – will become key to any AI implementation.

The Perils of Worklist Cherry-Picking

If you’re a radiologist, chances are at some point in your career you’ve cherry-picked the worklist. But picking easy, high-RVU imaging studies to read before your colleagues isn’t just rude – it’s bad for patients and bad for healthcare.

That’s according to a new study in Journal of Operations Management that analyzes radiology cherry-picking in the context of operational workflow and efficiency. 

Based on previous research, researchers hypothesized that radiologists who are free to pick from an open worklist would choose the easier studies with the highest compensation – the classic definition of cherry-picking.

To test their theory, they analyzed a dataset of 2.2M studies acquired at 62 hospitals from 2014 to 2017 that were read by 115 different radiologists. They developed a statistical metric called “bang for the buck,” or BFB, to classify the value of an imaging study in terms of interpretation time relative to RVU level. 

They then assessed the impact of BFB on turnaround time (TAT) for different types of imaging exams based on priority, classified as Stat, Expedited, and Routine. Findings included:

  • High-priority Stat studies were reported quickly regardless of BFB, indicating little cherry-picking impact
  • For Routine studies, those with higher BFB had much lower reductions in turnaround — a sign of cherry-picking
  • Adding one high-BFB Routine study to a radiologist’s worklist resulted in a much longer increase in TAT for Expedited exams compared to low-BFB studies (increase of 17.7 minutes vs. 2 minutes)
  • The above delays could result in longer patient lengths of stay that translate to $2.1M-$4.2M in extra costs across the 62 hospitals in the study. 

The findings suggest that radiologists in the study prioritized high-BFB Routine studies over Expedited exams – undermining the exam prioritization system and impacting care for priority cases.

Fortunately, the researchers offer suggestions for countering the cherry-picking effect, such as through intelligent scheduling or even hiding certain studies – like high-BFB Routine exams – from radiologists when there are Expedited studies that need to be read. 

The Takeaway 

The study concludes that radiology’s standard workflow of an open worklist that any radiologist can access can become an “imbalanced compensation scheme” that can lead to poorer service for high-priority tasks. On the positive side, the solutions proposed by the researchers seem tailor-made for IT-based interventions, especially ones that are rooted in AI. 

A New Day for Breast Screening

In a breathtaking about-face, the USPSTF said it would reverse 14 years of guidance in breast screening and lower its recommended starting age for routine mammography to 40.

In a proposed guidance, USPSTF said it would recommend screening for women every other year starting at age 40 and continuing through 74. The task force called for research into additional screening with breast ultrasound or MRI for women with dense breasts, and on screening in women older than 75.

The move will reverse a policy USPSTF put in place in 2009, when it withdrew its recommendation that all women start screening at 40, instead advising women in their 40s to consult with their physicians about starting screening. Routine mammography was advised starting at age 50. The move drew widespread condemnation from women’s health advocates, but the USPSTF stuck to the policy even through a 2016 revision.

The task force remained steadfast even as studies showed that the 2009 policy change led to confusion and lower breast screening attendance. The change also gave fuel to anti-mammography extremists who questioned whether any breast screening was a good idea.

That all changes now. In its announcement of the 2023 guidance, USPSTF said it based the new policy on its review of the 2016 update. No new RCTs on breast screening have been conducted for decades (it’s considered unethical to deny screening to women in a control group), so the task force commissioned collaborative modeling studies from CISNET.

USPSTF said the following findings factored into its decision to change the guidance: 

  • Biennial screening from 40-74 would avert 1.3 additional breast cancer deaths per 1,000 women screened compared to biennial screening of women 50-74.
  • The benefits of screening at 40 would be even greater for Black women, at 1.8 deaths averted. 
  • The incidence rate of invasive breast cancer for women 40-49 has increased 2.0% annually from 2015-2019, a higher rate than in previous years. 
  • Biennial screening results in greater incremental life-years gained and mortality reduction per mammogram and better balance of benefits to harms compared to annual screening.

The Takeaway 

As with the FDA’s recent decision to require density reporting nationwide, the USPSTF’s proposal to move the starting age for mammography screening to 40 was long overdue. The question now is how long it will take to repair 14 years of lost momentum and eliminate confusion about breast screening.

Learning Curve in DBT Screening

Digital breast tomosynthesis continues to evolve. First introduced initially as a problem-solving tool in breast imaging, DBT is becoming the workhorse modality for breast screening as well. 

But DBT still requires some adjustment when used for screening. In a study of nearly 15k women in European Radiology, Swedish researchers describe how the false-positive recall rate for DBT cancer screening started higher but then fell over time as radiologists got used to the appearance of lesions on DBT exams.

The Malmö Breast Tomosynthesis Screening Trial was set up to compare one-view DBT to two-view digital mammography for breast screening. Unlike some DBT screening trials, the study did not use synthesized 2D DBT images. DBT images were acquired 2010-2015 with Siemens Healthineers’ Mammomat Inspiration system. 

Findings in the study included: 

  • DBT had a sharply higher false-positive recall rate in year 1 of the study compared to DM (2.6% vs. 0.5%)
  • DBT’s recall rate fell over the five-year course of the study, stabilizing at 1.5% 
  • Recall rates for DM varied between 0.5% and 1% over five years
  • Most of the DBT recalls (37.3%) were for stellate lesions, in which spicules radiate out from a central point or mass. With DM, only 24.0% of recalls were for stellate lesions
  • The number of stellate distortions being recalled with DBT declined over time, a trend the authors attributed to a learning curve in reading DBT images

The authors said that the DBT false-positive recall rate in their study was “in general low” compared to other European trials. They claimed that MBTST is among the first studies to analyze recall rates by lesion appearance, an important point because radiologists may see a different distribution of lesion types on screening DBT compared to what they’re used to with DM.

The Takeaway 

The Malmö Breast Tomosynthesis Screening Trial was one of the first to investigate DBT for breast screening, and previous MBTST research showed that DBT can also reduce interval cancers, which occur between screening rounds. 

The new findings offer further support for DBT breast screening and give hope that whatever shortcomings the technology might have early on in a screening role can be addressed through training and experience. It also confirms recent research indicating that DBT has become the new gold standard for breast screening.

Imaging Wire Q&A: Nanox.AI Thinks Big

With Zohar Elhanani
Nanox.AI, General Manager

The role of imaging AI continues to grow, as radiology workflows increasingly utilize these tools to prioritize patients and support diagnoses. This already represents a big change for healthcare, but it could be just the beginning of imaging AI’s far greater public health evolution that extends well beyond the radiology department and could change how and when many diseases are diagnosed.

In this Imaging Wire Q&A we sat down with Nanox.AI General Manager, Zohar Elhanani, to discuss Nanox.AI’s view of how imaging AI is helping healthcare today and how AI’s role in public health could be much bigger than many of us imagine.

You had a front row seat during two key periods in the medical imaging industry’s evolution. What are the major themes that connect those periods and how are they shaping imaging’s future?

I started my career in medical imaging right when we were shifting from analog to digital. My company’s products moved images between healthcare facilities, radiologists, teleradiologists, and referring physicians. That was step one of the digital evolution.

Fast forward 20 years, we’re now seeing a digital image volume evolution, as medical images are being produced, analyzed, and stored at a massive scale. Volumes have grown so much it’s been hard for radiologists to keep up.

This digital image volume growth also made imaging AI possible, which is becoming a larger part of the radiology workflow, and helping radiologists interpret images as efficiently and accurately as possible.

So for me, it’s gone full circle, from the start of the digital imaging evolution and into the imaging AI evolution.

How do you view the next phase of the AI evolution?

AI is already becoming a driving force in medical imaging diagnostics. It’s becoming commonly used across healthcare facilities and providers, and not only in radiology. This is really a tectonic shift in healthcare.

The COVID pandemic and the focus on clinical and revenue cycle efficiency has made AI much more than just a buzzword. AI is actually becoming more focused on validated use cases and generating real tangible ROI.

For Nanox.AI, as an medical imaging AI pioneer, this has been a journey. We initially targeted detection of low prevalence findings, triaging acute conditions, and improving turnaround times for radiologists. That was a very good entry point. It was a valuable way to substantiate how AI can detect abnormalities and prioritize reads.

During our AI journey, we also realized that although these are valuable use cases, they don’t necessarily always present a clear ROI. As part of our evolution, we’re now looking to expand and we’ve already introduced products targeting larger populations at scale, focusing on high prevalence, chronic conditions that have not been detected.

We feel that promoting preventative care for treatable illnesses will expand AI to broader populations and more use cases, while supporting the shift from fee-for-service models to value-based care.

We’re committed to population health AI. We’re building out our population health product offering and roadmap and we’ll introduce more solutions over time, in addition to our coronary calcium scoring and vertebral compression fracture solutions. We think that’s a path for the future and an area that AI can play a bigger role.

We don’t hear AI companies talk about population health very often. Can you tell me more about how AI supports population health?

The pathway to value-based care involves making healthcare systems more efficient and offering patients preventative care, rather than waiting for undetected diseases to get worse.

Our population health solutions focus on catching diseases that have the highest rates of morbidity and mortality. Coronary heart disease and osteoporosis are silent killers, and they get worse over time.

Radiologists don’t always note or look for these findings. Generally, someone walks in for a specific condition, like a broken rib, and incidental findings are not necessarily caught or communicated.

Our solutions yield more information from existing CT scans and EMR data. By applying these algorithms, we can spot undetected diseases and alert physicians to initiate a pathway to care that improves patient health and reduces costs for healthcare systems.

This is where the whole shift to value-based care is heading and we think that’s an area where AI and Nanox.AI could play a bigger role.

How does AI economics work for population health programs?

So obviously there are two sides.

First, there’s a revenue cycle side that involves the actual income from providing medical care. And obviously, in value based care systems, these are capitated programs.

Second, there’s the cost reduction side, achieved through early intervention and avoiding expensive care for under-treated and non-treated conditions.

So the idea is to create enough incentive for both payer and provider to look at AI as a way to reduce cost but also manage patient risk.

The radiologists need to be motivated and incented to identify and confirm these findings. So that’s one area that needs to be looked at. The medical imaging AI industry has been struggling to find the right way to make radiologists more motivated to look into findings that are different from the purpose of the original study. Nanox.AI is always at the forefront of finding solutions and we aim to do that here too.

Who would be involved in evaluating and implementing AI-based population health initiatives?

In our population health projects, we generally work with chief revenue officers and chief population health officers, who look at the breadth of cost and quality of care across their population. The two have to go hand-in-hand. What is the cost and what is the quality?

There also needs to be buy-in at the point of care by the radiologist. That’s where the finding is detected. But in terms of the program as a whole, it’s orchestrated by the chief revenue officer, chief population health officer, and the chief medical officer. They prescribe the pathway to care and define what needs to trigger that pathway based on AI-detected incidental findings.

What’s the best way for these population health executives to involve the radiology department?

There needs to be some kind of economic benefit for the radiologist to take action on these findings. One incentive is obviously just quality of care and the breadth of the report itself, but a financial incentive is also required. That’s part of the equation and that’s something that needs to be sorted at the IDN level between the payer and the provider as part of a value based care paradigm.

When population health programs use imaging AI to identify incidentals at scale, follow-up management becomes really important. What’s the best way to do follow-up management in a program like this?

The emphasis here has to be on establishing pathways to care from the point that the AI and the radiologists confirm a finding. And I think that’s again part of the shift to a value-based care paradigm where these findings make their way to actual treatment, which reduces costs and improves patient care.

That’s exactly where we’re focusing our efforts in order to make sure that a finding doesn’t just stay there in the report itself. It actually triggers a call for action to take the finding to the right stakeholder at the provider level or beyond.

That’s a critical part of it. What is the pathway to care and what are the incentives around that pathway under a value-based care program or plan?

Would population health programs achieve any benefits from AI that they weren’t expecting?

Definitely. We’ve run our own tests on data to compare what’s written in the EMR and patient records, and we found many new findings that did not exist. And that’s simply by running algorithms retrospectively on existing data and substantiating the value of AI.

So definitely, the response has been very, very favorable to the fact that things go missed and are under-reported and there’s value there.

Now, the question is how to deploy that at scale and how to create the actions and the pathway to care from these detections?

Do you have any advice for healthcare systems considering using AI to support their own population health efforts?

One of our larger customers recently shared with us that his three priorities for AI-enabled population health are improving patient care, reducing liability risk, and adding financial value.

I completely agree. Combining the improvement of patient care, the financial value for the system, and reduction of liability risk is critical.

I think that’s something the industry as a whole is still looking for. How do you substantiate the value of AI in terms of the financial benefit? How does it really improve patient care as a whole? And specifically, if we look at triage solutions, how do they really impact low prevalence acute findings versus what we see in population health with high prevalence chronic illnesses?

That’s the goal for this whole pathway that we’re discussing. AI for population health isn’t here to replace referring physicians or regular checkups. It’s here to serve as kind of an early warning signal for chronic disease. That’s really the idea, serving as a safety net for any finding that exists and is not detected or is under reported. It’s another layer that would augment whatever is done by the primary care physicians or any ongoing radiologist interpretations.

Long term, obviously it provides better cost structure for the entire system and offers comprehensive preventative care for the patient. And as I said earlier, we won’t be simply looking at a handful of conditions. It will involve a longer pathway to covering many incidentals and making sure that they’re all accounted for in terms of at least knowing that they’re there and considering potential care pathways to ensure that nothing is ignored or under-treated.

As a whole, it’s another layer of detection that it doesn’t currently exist. That’s how we see AI playing a big role in the population health domain.

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Imaging Wire Q&A: Arterys’ AI Journey

With John Axerio-Cilies, PhD
CEO & Co-Founder

It’s been quite a decade for AI and cloud technology in the healthcare space, with some major milestones and learning moments along the way.

Arterys had a front seat view for many of these milestones, as the industry’s first cloud-native imaging AI developer and one of the only companies that serves as both an AI developer and a multi-vendor AI marketplace platform provider.

In this Imaging Wire Q&A we sat down with Arterys CEO and co-founder, John Axerio-Cilies, PhD, to discuss medical imaging’s AI and cloud evolution and how Arterys works with its Center of Excellence partners to make AI real.

Arterys’ 10-year anniversary makes you imaging AI veterans. What are some of the key milestones you’ve witnessed during this journey?

When we started back in 2011, imaging AI as we know it wasn’t really a thing.

At the time you could say launching Arterys was a leap of faith, based on our vision for patient-driven insights, data-driven medicine, and a commitment to the cloud.

AI and machine learning have been around for decades, and some imaging vendors began exploring machine learning-like approaches in the early 2000s. However, back in 2011, the forward-looking part of the healthcare industry was mainly focused on precision health and big data. Those were the key buzzwords and cloud wasn’t even part of the equation.

It was still extremely early for cloud, especially in healthcare. Early on, we even had an IT leader at a major academic medical center tell us that they would “never do cloud.” It took 10 years, but now everyone who’s educated on the subject recognizes cloud’s benefits, even the IT team from that same academic medical center. At that same center if it’s not cloud enabled, they will not consider it.

Imaging AI as we know it primarily got its start because of deep learning, beginning with a key paper that came out in 2012, and leading up to the current surge in industry interest that started in 2017. We’re now seeing the AI hype curve slow down into more of a reality. There haven’t been any monumental events that immediately changed the way people in healthcare think about AI. Instead, imaging AI is slowly going through the expected adoption cycles, making its way from early adopters and towards the laggards.

How often do you come across radiologists who are concerned that AI will endanger their job?

I rarely hear concerns that AI could eliminate radiologists’ jobs among the physicians who I work with, but these folks are already interested in AI. Still, there is certainly a pool of radiologists who are concerned that AI might replace them.

However, this happens in every industry and it’s still very early in AI’s evolution. I think it’s going to be decades before AI would realistically challenge radiologist’s current jobs, plus radiologists’ jobs are going to keep evolving along with AI.

It’s been far more common to see radiologists realize that AI can accelerate their workflow and help them day-to-day, and we expect that trend to continue.

How has Arterys’ own AI platform evolved?

In the last few years we’ve opened up our platform to support more and more use cases, including more modalities and more top service lines. The most notable expansions have been in cardiovascular imaging, neuroimaging, women’s health, and within acute and X-ray service lines.

We had to add more functionality to our underlying platform in order to support this portfolio expansion, opening up our platform to the point that it’s almost self-serve. By opening the platform we’ve seen expanded adoption from not only our 40+ AI vendor partners but also healthcare institutions using the Arterys platform to deploy, share, and refine their own AI tools, fully in their control.

That’s exciting because we want to have an entire ecosystem to support early innovators, researchers, and academic medical centers. Even larger IDNs have strategic initiatives around AI and are funding researchers to develop AI models that they want to integrate.

We want to help support that evolution and push these models from research into clinical practice and to ultimately become commercial products. We’re helping manage that too, because we have folks that can help for regulatory support, commercial go-to-market support, and the entire commercialization trajectory.

We also continue to refine these products and their implementations, thanks in part to our Center of Excellence program.

What inspired you to create Arterys’ Center of Excellence program?

The imaging industry lacks clinical evidence, and the data to prove the value proposition of products. Healthcare marketing folks say all these grandiose statements but when you double-click on these statements, there’s often not a lot of data to support them.

This is also where most AI providers and users are lacking, and it’s the reason we created the Center of Excellence program.

Through the Center of Excellence program, we work with our major medical institution customers to go one step deeper and make sure their AI adoption is happening and it’s actually impacting whatever needs to be impacted. These improvement targets usually include patient outcomes and efficiency, so we’re often trying to create an infrastructure that solves both of those problems.

With our Centers of Excellence we translate actual data to show clients how we were able to accomplish their AI goals because we worked with them to change their workflow and helped them guide behavioral changes.

AI success is so much more than a working product. People talk about AUC, sensitivity, and specificity, but that’s less than 5% of the problem. You still need to have the infrastructure and the clinical workflow and the behavioral change to adopt this stuff.

Who would be involved in a Center of Excellence partnership?

Every partnership starts with clinical users, but the things we measure and improve would be very different depending on the specific product, its users, and the organization.

For example, our X-ray product targets ED physicians and to a lesser extent radiologists, giving
them a tool to quickly triage, treat, or discharge patients. We’d work with that partner to confirm that the X-ray solution actually improves outcomes and helps treat patients faster.

It’s very different with our cardiac product, which is used by cardiologists and radiologists, and is absolutely required for diagnosis. With these partners, we’d work with them to help confirm that the cardiac product works as needed.

In any scenario, the clinical users would be a starting point but we’d also work closely with senior leadership like CIOs, CMIOs, and CFOs to make sure institutional goals are being met.

We’re actively looking for more Center of Excellence partners, especially partners in the neuroimaging and in the oncology space.

How are your Center of Excellence partners’ improvements communicated?

We’ve done a good job making this as non-intrusive as possible. Because we’re completely cloud-based we can usually integrate with our partners in a few minutes, and we can also collect more detailed clinical information for partners interested in understanding their patients’ pre- and post-imaging pathways.

We provide Center of Excellence partners with all outputs from each patient session to any of their imaging IT or EMR platforms, allowing them to monitor and analyze their progress.

Can you tell me about your most successful Center of Excellence partners?

The most successful Centers of Excellence really care about making AI real and they are willing to dive in, run assessments, and perform trials to make sure that we’re actually impacting whatever we set out to improve. UMass Memorial Health Care here in the U.S.A. and Centre Hospitalier de Valenciennes in France are a couple great examples of sites who are doing this.

These most successful Centers of Excellence truly had clinical pain points that hurt bad enough for them to make solving them a priority. For example, we’ve had some partners who kept ED patients waiting for X-ray results for hours or had to discharge patients without their results. That’s a massive pain point and it’s enough to make hospitals get serious about finding solutions.

The opposite of that is hospitals who say, “oh, let’s get AI in here” but aren’t sure about what’s their clinically unmet need or if they even have one. The fact that these hospitals don’t know where they can improve suggests that they have a lot of ways to improve, but they have to identify these challenges and commit to addressing them before they are ready to become a Center of Excellence.

What should healthcare institutions ask themselves when considering being a Center of Excellence?

The first thing they should ask themselves is if they are committed to making AI real. I think that’s a really important question. Because if they are not, and they’re not truly invested in actually helping the patients or improving workflow, that’s not an ideal candidate. I don’t care about marking an AI adoption checkbox. What I care about is working with our partners to make their AI adoption impactful.

Potential partners should also understand their goals and confirm that they are ready to work together to achieve those goals, because many improvements come from outside of the software, and continuous improvement is a collaborative process.

About Arterys

Arterys is the market leader and the world’s first internet platform for medical imaging. Its objective is to transform healthcare by transforming radiology. The Arterys platform is 100% web-based, AI-powered, and FDA-cleared, unlocking simple clinical solutions.

Winners Announced for 2020 Imaging Wire Awards

The Imaging Wire is thrilled to announce the winners of the 2020 Imaging Wire Awards, honoring this year’s most outstanding contributors to radiology.

The following Imaging Wire Award winners were nominated by their peers and selected by a panel of judges for their efforts to evolve radiology and improve the lives of clinicians and patients:

COVID Hero: Byron Christie, MD; Associate Chief Medical Officer of Integrations, Radiology Partners

When the pandemic hit, Dr. Christie and nine radiologists from RP’s SEAL team traveled across the U.S. to provide care in hard hit regions. After recovering from a COVID-19 infection that he contracted while treating patients in Florida, Dr. Christie increased his efforts to fight COVID-19 through his work at RP, continued plasma donations, and by educating medical students.

Diagnostic Humanitarian: Daniel J. Mollura, MD; President and CEO, RAD-AID International

Dr. Mollura is the Founder and CEO of RAD-AID International, a nonprofit organization dedicated to expanding radiology care to underserved and resource-poor communities. Over the last 12 years, Dr. Mollura grew RAD-AID to nearly 14,000 members serving over 80 hospitals in 35 countries. Among many accomplishments this year, RAD-AID’s residency program in Guyana will graduate its first class of radiologists.

AI Activator: Jon T. DeVries, CEO; Qlarity Imaging

Under Jon’s leadership, Qlarity Imaging has made significant progress developing the company’s QuantX software, which integrates images from multiple modalities to assist radiologists in the assessment and characterization of breast abnormalities. DeVries continues to expand QuantX’s capabilities and market reach with an innovative approach to product development and partnerships.

Burnout Fighter: Marla B.K. Sammer, MD; Associate Professor of Pediatric Radiology, Texas Children’s Hospital

Faced with Texas Children’s Hospital’s massive imaging volume growth, Dr. Sammer introduced a new initiative to optimize workflow, balance distribution across teams, and improve radiologists’ workdays. These changes reduced Texas Children’s average turnaround for X-rays by 25% and other modalities by over 27%, while helping its radiologists reliably predict their workday, fostering a sense of fairness and control, and reducing burnout.

Insights to Action: Syed Zaidi, MD, MBA; Associate Chief Medical Officer for Integrations, Radiology Partners

Dr. Zaidi has consistently tackled imaging waste throughout his career, participating in Choosing Wisely and serving as a leader in the ACR’s Imaging 3.0 initiative. Dr. Zaidi also developed a utilization management program at his local hospital to limit unnecessary chest CT scans for pulmonary embolism, while helping to roll out a best practice recommendations program across Radiology Partners.

Continued Care: Jinghong Li, MD, PhD, University of California San Diego

Dr. Jinghong Li is an attending physician and associate professor specialized in pulmonary diseases and critical care at University of California San Diego. While caring for COVID-19 patients in UCSD’s ICU, Dr. Li also worked with engineers and scientists to develop a wearable ultrasonic patch to allow continuous bedside ultrasound monitoring. This patch would alleviate infection control concerns associated with manual bedside imaging, while helping predict respiratory failure due to COVID-19 pneumonia.

Cornerstone: Karen Holzberger, SVP and GM; Healthcare Diagnostics, Nuance Communications

As the leader of Nuance’s healthcare diagnostics team, Karen’s top focus is to drive innovations that advance the practice of radiology. That was on display this year, as Ms. Holzberger led the development of new capabilities that prioritize and add insights to COVID-related exams, delivered on Nuance’s promise to enable “AI at scale” through the Nuance AI Marketplace, and continued to enhance PowerScribe One.

Diversity & Inclusion: Kristina Elizabeth Hawk, MD; President, Matrix East Pod A, Radiology Partners

Dr. Kristina Elizabeth Hawk is a founding member of the RP Belonging Committee, which designs tracks and programs intended to amplify the roles of minority groups in the practice. Dr. Hawk has led outreach to diverse radiology residents and fellows, and serves on the ACR’s Commission for Women and Diversity, Stanford’s Radiology Diversity committee, and Ambra’s #Radxx board.

Congratulations to this year’s Imaging Wire Award winners and nominees, who’s efforts to elevate radiology is truly inspiring. Also, thanks to this year’s amazing judges and everyone who nominated these very deserving imaging professionals!

The 2020 Imaging Wire Award judges include: Bill Algee of Columbus Regional Hospital, Dr. Jared D. Christensen of Duke University Health, Dr. Keith J. Dreyer of Partners Healthcare, Dr. Allan Hoffman of Commonwealth Radiology Associates, Dr. Terence A.S. Matalon of Einstein Healthcare Network, and Dr. Syam Reddy of University of Chicago Ingalls Memorial.

Imaging Wire Q&A: Evolving With Hitachi VidiStar

With John Stock, MD, FACC
Pediatric Cardiologist
Pediatric Cardiac Care of Arizona

The role of imaging in pediatric cardiology has evolved tremendously in recent years, so in order for these practices to operate successfully, their PACS systems have to evolve at the same pace. That can be easier said than done, but it’s exactly what happened with Pediatric Cardiac Care of Arizona and its VidiStar PACS system.

In this Imaging Wire Q&A, we sat down with Dr. John Stock of Pediatric Cardiac Care of Arizona to discuss the evolving role of imaging in his practice, how Hitachi’s VidiStar PACS has evolved along with it, and what pediatric cardiology practices should look for in their own PACS systems.

Tell us about your practice and how you use imaging.

We perform and interpret approximately 3,000 pediatric studies per year. I interpret all the cardiac ultrasound studies independently after reviewing and confirming measurements.

From there, VidiStar generates a report that is often faxed to the referring physician. The studies are digitally archived on our server and in the cloud, with reports maintained in the electronic medical records. We follow all appropriate use guidelines and quality assurance initiatives, and we are an IAC accredited lab.

How has your practice been impacted by the COVID-19 pandemic?

COVID-19 has definitely affected my practice, but not how some might think. We experienced a 20% to 30% drop in patient volumes during the shutdown’s peak months. There appears to have been a rebound, as children and adolescents returned back to their pediatricians, schools, and sports.

Pediatric patients with congenital heart disease have a higher risk of complications. As a result, we are cautious in our follow up and in some cases evaluating for possible findings related to COVID-19. There is also a subset of the disease called multi-inflammatory syndrome of childhood (MIS-C), which can result in decreased ventricular function and coronary artery dilation. This requires prompt management and follow-up.

You’ve been using VidiStar for quite a while, can you share how you use it?

I use VidiStar on a daily basis for interpreting and completing reports on my pediatric, adult congenital, and fetal cardiology patients. This includes looking at the study as the sonographer performs an evaluation, followed by an independent review with measurements confirmed by the VidiStar reporting package, and then saving the study to our server.

How has VidiStar changed over time?

VidiStar has come a long way since Hitachi acquired the platform two-plus years ago, turning it into a system that is affordable, user friendly, and can support the simplest and most complex pediatric cases.

I’ve benefited most from the improvements to VidiStar’s pediatric reporting package. At first, VidiStar’s pediatric package was very basic and utilized an adult format, requiring me to do a lot of work outside of the platform. Kids are not small adults. They have their own complexities. The reports need to reflect the variation in anatomy that can occur in congenital heart disease.

Hitachi came in, made a commitment to pediatrics, and VidiStar now fits the needs of most pediatric cardiology practices. In just the last two years, the pediatric package improved many measurement parameters and Doppler measurements, which allow me to perform comparisons over time.

What advice can you share for pediatric cardiologists evaluating new PACS systems?

Any independent pediatric cardiology provider considering a new PACS system should evaluate how each system would meet their clinical and workflow needs and whether it fits their budget.

Most important for me clinically, is the ability to track changes over time and knowing that I can be confident when I send out reports to some of the best centers in the country. The reports must also look professional, with appropriate identification of pertinent impressions, as well as documentation of pertinent positive and negative findings.

From a workflow perspective, it is also very important that the PACS system interfaces well with the electronic medical records, and that it’s easy for both the sonographer and the physician to use.

It’s also crucial that the PACS system works consistently. By that, I mean that the system always works and its output is reproducible and consistent over time, which isn’t guaranteed with some platforms.

Pediatric cardiologists should also look for reporting packages that clearly document Z scores and Doppler velocities, which are necessary for appropriate billing. Incorporation of 3-D and strain will also be necessary going forward.

About Pediatric Cardiac Care of Arizona

Based in Tempe, Arizona, PCCA’s mission is to partner with patients, families, and referring physicians in order to provide excellent outpatient cardiac care in an environment of trust, openness, and professionalism.

Dr. John Stock has cared for patients with congenital and acquired heart disease for over 20 years, after receiving his medical degree from Upstate Medical Center, completing his pediatric residency at Phoenix Children’s Hospital, and undergoing fellowship at Oregon Health Sciences University.

About Hitachi VidiStar

The Hitachi VidiStar Platform gives physicians and healthcare providers the ability to read and interpret diagnostic studies over the internet for timely interpretation, improved patient diagnosis, clinical decision support, and advanced patient data analytics and notification.

VidiStar provides highly customizable infrastructure for multi-modality viewing, reporting, and analytics while interfacing with existing IT systems for one seamless solution.

Nominations Open for the 2020 Imaging Wire Awards

Nominations are now open for the 2020 Imaging Wire Awards, honoring this year’s most outstanding contributors to radiology practice and outcomes.

The 2020 Imaging Wire Awards will be presented to seven imaging professionals for achievements in the following categories:

  • COVID Hero: for excellence in COVID-19 care and research
  • Insights to Action: recognizes efforts to reduce unnecessary imaging
  • Diagnostic Humanitarian: for achievements supporting equity in patient care
  • AI Activator: recognizes actions to use artificial intelligence to improve patient care
  • Continued Care: honoring efforts to maintain patient care throughout the COVID-19 emergency
  • Burnout Fighter: for addressing inefficient work practices that lead to physician burnout
  • Cornerstone: honoring non-physicians for outstanding contributions to the practice of radiology
  • Diversity and Inclusion: recognizing efforts to improve diversity and inclusion in imaging

Those interested in applying or nominating a colleague for one of the above Imaging Wire Awards can do so until November 5th through this link .

Winners will be selected by a panel of industry leaders and recognized during RSNA 2020.

The 2020 Imaging Wire Awards judges committee includes:

  • Bill Algee, FAHRA CRA – Columbus Regional Hospital
  • Jared D. Christensen, MD, MBA – Duke University Health
  • Keith J. Dreyer, DO, PhD, FACR, FSIIM – Partners Healthcare
  • Allan Hoffman, MD – Commonwealth Radiology Associates
  • Terence Matalon, MD, FACR, FSIR – Einstein Healthcare Network
  • Syam Reddy, MD – University of Chicago Ingalls Memorial, Radiology Partners Chicago

About The Imaging Wire

The Imaging Wire is a newsletter and website dedicated to making it easy for the people of medical imaging to be well informed about their specialty and industry. Read twice weekly by thousands of global radiology professionals, The Imaging Wire is the first publication from business news company, Insight Links, which is dedicated to expanding news literacy across healthcare. For more information: https://theimagingwire.com/.

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-- The Imaging Wire team