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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