Imaging Wire Q&A: Looking Forward to SIIM21

With Christopher Roth, MD, MMCI, CIIP
Duke University, Vice Chair of Radiology for Clinical Informatics and IT

Duke Health, Director of Imaging Informatics Strategy

Like many in healthcare, informatics professionals just made their way through an unprecedented year that included plenty of imaging problems to solve and lessons to learn, but very few opportunities to meet with their peers and improve together. That’s about to change at the upcoming SIIM21 annual meeting, and there’s going to be a lot to talk about.

In this Imaging Wire Q&A, we sat down with Duke’s Christopher Roth, MD, to discuss what he’s expecting at SIIM21, what makes this year’s meeting different, and how informatics professionals can make the most of it.

Tell us about your work and what you do at SIIM.

At Duke University, I’m the Vice Chair of IT and Informatics in the Department of Radiology and the Director of Imaging Informatics Strategy for the health system.

At SIIM, I chair the Annual Meeting Program Committee and I’m currently a chair of the HIMSS-SIIM Enterprise Imaging Community.

I’m also on the RSNA Informatics Committee and the ACR Informatics Commission.

What can we expect from the SIIM21 Annual Meeting?

SIIM21 will be a virtual meeting held from May 24th to the 27th.

The first day will be kicked off by Dr. Kimberly Dyan Manning, Associate Vice Chair of Diversity, Equity & Inclusion at Emory University, followed by strong didactic lectures, interactive roundtables, research and applied informatics sessions, and some Global Track sessions to close out the day.

Day two will follow a new format. In the past, it’s been a continuation of those kinds of educational sessions, but the second day of this year’s meeting will focus on the interface between industry and the people on the clinical frontlines. It’s going to focus very hard on getting people together to fuse ideas and learn from each other .

The second day will kick off with a keynote address by Dr. Jonathan Rothberg, a serial entrepreneur, and co-founder of companies such as 4catalyzer, Butterfly Network, and Hyperfine.

In addition, we’re going to have roundtables, but they won’t just be clinical leaders describing problems and imaging vendors at the table participating, it’s going to be a joint conversation. For example, an imaging vendor would partner with clinical thought leaders to approach a difficult problem and then discuss it with the community.

We’ll also have #AskIndustry Sessions where a thought leader will actually present a difficult problem to three or four industry vendors to get their perspectives on it. The point of this is to really attack the middle ground, the interstitial space between industry and users who could be physicians, scientists, analysts, or the technological staff that we all employ to take care of patients.

Day three will start with a keynote address by Harold F. Wolf III, President & CEO of HIMSS, and will largely focus on the breadth of imaging informatics specialties around the health system. So, as I said, that would include pathology, dermatology, obstetrics, cardiology, ophthalmology, and different specialties who create multimedia. Those groups are going to share their successes and their failures.

The highlight and the grand finale of day four will be the Samuel L. Dwyer Lecture, named after one of the real pioneers in imaging informatics, and presented by Dr. Adam Flanders, Professor of Radiology & Rehabilitation Medicine and Enterprise Vice-Chair for Imaging Informatics at Thomas Jefferson University.

Going back to the second day of SIIM21, what are the types of roles that would represent the vendor and frontline sides of these discussions?

The breadth of people and roles at SIIM are what makes it special. We have everyone from the frontline entry-level analysts at our hospitals up through chief technology officers. We have very early-stage members in training who are still residents all the way up to CMIOs.

The second day isn’t going to be so role-focused as it will be expertise-focused. You will hear from some really terrific speakers with engaging personalities and subject matter expertise in the areas that SIIM is interested in sharing. That could even be someone who lives on the procurement side, let’s say, who can speak to a successful RFP as opposed to an unsuccessful one.

Does SIIM21’s virtual format create any other opportunities to do things differently?

Yes. This meeting will be held under the theme of ‘Connecting Without Boundaries,’ supporting one of SIIM’s strategic goals to broaden its international reach, and the virtual format will definitely support this goal. Many of the meetings that we tend to go to have a distinctly North American flavor, and that’s really not the way we should be working.

We developed a global imaging track where thought leaders from around the world will bring experts from their own neck of the woods to share their approach to problems. We’ll have thought leaders from six geographies – South America, Europe, Africa, Middle East, Asia, and Australia – who will have their own opportunities to lecture and meet before, during, and after SIIM21.

The intent of those meetings is to facilitate collaborations between the regional experts and educate SIIM members who just don’t get the opportunity to learn how other parts of the world approach difficult problems.

Are there certain things that you’ve learned at previous meetings that have been particularly helpful in your day-to-day job?

This happens at every single meeting.

When I go to these meetings, it’s almost a joke with my team at Duke that I will come back from SIIM with a wish list of four or five new things. It’s almost an every-year thing that I’ll see something that I get psyched about and I’ll bring it back. We’ll start talking about these ideas and employ several of them over the course of the year.

In addition to that, I go to see the industry partners because I want to know what they’ve been doing over the past year. They’re busy getting new customers, they’re busy doing new development, they’re making new inter-corporate partnerships. I want to know who they’re working with, what they’re doing, and if there’s anything that is beneficial to me.

I love to be able to say to them, “I think this feature is really, really cool. If you did this it’d be even cooler. Can you do that, or can we do that with you?” Really advocate for the things that we need to have in the imaging space to be successful.

In addition to that, SIIM is where I do my shopping. If I know I’m in the market for something, SIIM is probably the first place I’m going to go look for it, because it’s a very practical meeting. There are other industry meetings that are humongous and difficult to navigate. SIIM is a more intimate, practical place to have some of those difficult conversations.

What should people planning to go to SIIM do to make the most out of this year’s event?

To have success in any in-person meeting you have to plan ahead. You need to know when the sessions are going to be and make sure you block off the time to be there.

It’s the same thing with a virtual meeting. You have to look ahead and be sure you’re not going to be doing a biopsy at the time that you’re supposed to be attending a session.

I would also think about your infrastructure, the tools that you have, the applications you use and if there’s any problems that you may be able to improve or solve at SIIM21. Be prepared to have challenging discussions with vendors.

Additionally, I would take a look at your imaging strategic plan and see what gaps there have been over the past year and a half as COVID has consumed so much of our day-to-day activities. So, there’s probably a lot of opportunity in your imaging strategic plan that you can catch up on and the industry partners that you would be engaging have been developing as well. You may have a lot more to learn than in typical years.

I would make time to visit the vendor booths and have individual meetings or group meetings with thought leadership from other hospitals who are all facing the same thing. I know that there are a few of us who all share the same viewer, the same vendor-neutral archive, the same image sharing platform. There’s nothing against getting a group of you together to go talk to that group of corporate entities to provide a little more oomph to the things that you’re asking.

I would also recommend talking to your clinical colleagues if you’re in the hospital. Find out what they like, what they don’t like, and take those things to those corporate partners to see if anything can be done about them.

What’s the type of person that should be going to SIIM21 but doesn’t realize that it applies to them?

I’ll steal an anecdote from a dermatologist, Veronica Rotemberg, from Memorial Sloan Kettering that I heard recently. She is essentially doing dermatology informatics, but she had no idea that she was doing it.

The people that should be coming to SIIM are the people that work in imaging, IT and clinical quality improvement and process improvement, people who are trying to handle the integrations of their images with the electronic health records. Those are informaticists and they oftentimes don’t even know that they are.

It could be derm, it could be path, it could be cardiology, it could be a number of different specialties. People that care about clinical care, care about integrating the images that they’re capturing and longitudinally following the problems that their patients bring to them. These are people that are doing imaging informatics but maybe haven’t been a part of SIIM before. And really, those are the kinds of people that we need to be engaging.

Similarly, there are industry and researchers and clinical people, like myself, who are doing artificial intelligence, who are creating algorithms and trying to deploy packages and get them into the point of care. Maybe they’re in pathology. Maybe they’re in dermatology. Maybe they’re looking at wounds on a smartphone using AI or they’re looking at a mole and evaluating whether or not its cancer.

Those are people that really have a home at SIIM but they just may not have gotten exposed to it yet. That’s really where the enterprise imaging community comes in.

That’s where a lot of SIIM’s educational outreach and liaison committee work comes in. We are trying to reach these people and help them realize that SIIM is the right place to come when you are looking to interact with and learn from people who face the same day to day challenges.

What are you most looking forward to at SIIM21?

What I look forward to most at every single SIIM meeting is seeing my friends. It’s quirky. It’s kind of cliché and folksy, but SIIM really has created a community of people who all see eye to eye. We’re all seeing the same problems. We’re all actually very similar type personalities. And it’s a special team to feel a part of, because it’s people and conversations that resonate with me.

The longer I’m at SIIM, the more I learn from them. I learn a lot from the other docs like me around the country and around the world who do some of this. And the deeper I go, the more I realize there’s a wealth of knowledge on the industry side that I’m still learning.

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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