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It’s complicated. A deep dive into the Viz/Medicare AI reimbursement model.

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By LUKE OAKDEN-RAYNER

In the last post I wrote about the recent decision by CMS to reimburse a Viz.AI stroke detection model through Medicare/Medicaid. I briefly explained how this funding model will work, but it is so darn complicated that it deserves a much deeper look.

To get more info, I went to the primary source. Dr Chris Mansi, the co-founder and CEO of Viz.ai, was kind enough to talk to me about the CMS decision. He was also remarkably open and transparent about the process and the implications as they see them, which has helped me clear up a whole bunch of stuff in my mind. High fives all around!

So let’s dig in. This decision might form the basis of AI reimbursement in the future. It is a huge deal, and there are implications.

Uncharted territory

The first thing to understand is that Viz.ai charges a subscription to use their model. The cost is not what was included as “an example” in the CMS documents (25k/yr per hospital), and I have seen some discussion on Twitter that it is more than this per annum, but the actual cost is pretty irrelevant to this discussion.

For the purpose of this piece, I’ll pretend that the cost is the 25k/yr in the CMS document, just for simplicity. It is order-of-magnitude right, and that is what matters.

A subscription is not the only way that AI can be sold (I have seen other companies who charge per use as well) but it is a fairly common approach. Importantly though, it is unusual for a medical technology. Here is what CMS had to say:

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The Medical AI Floodgates Open, at a Cost of $1000 per Patient

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By LUKE OAKDEN-RAYNER

In surprising news this week, CMS (the Centres for Medicare & Medicaid Services) in the USA approved the first reimbursement for AI augmented medical care. Viz.ai have a deep learning model which identifies signs of stroke on brain CT and automatically contacts the neurointerventionalist, bypassing the first read normally performed by a general radiologist.

From their press material:

Viz.ai demonstrated to CMS a significant reduction in time to treatment and improved clinical outcomes in patients suffering a stroke. Viz LVO has been granted a New Technology Add on Payment of up to $1,040 per use in patients with suspected strokes.

https://www.prnewswire.com/news-releases/vizai-granted-medicare-new-technology-add-on-payment-301123603.html

This is enormous news, and marks the start of a totally new era in medical AI.

Especially that pricetag!

Doing it tough

It is widely known in the medical AI community that it has been a troubled marketplace for AI developers. The majority of companies have developed putatively useful AI models, but have been unable to sell them to anyone. This has lead to many predictions that we are going to see a crash amongst medical AI startups, as capital runs out and revenue can’t take over. There have even been suggestions that a medical “AI winter” might be coming.

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Your Face is Not Your Own

By KIM BELLARD

I swear I’d been thinking about writing about facial recognition long before I discovered that John Oliver devoted his show last night to it.  Last week I wrote about how “Defund Police” should be expanded to “Defund Health Care,” and included a link to Mr. Oliver’s related episode, only to have a critic comment that I should have just given the link and left it at that.  

Now, I can’t blame anyone for preferring Mr. Oliver’s insights to mine, so I’ll link to his observations straightaway…but if you’re interested in some thoughts about facial recognition and healthcare, I hope you’ll keep reading.

Facial recognition is, indeed, in the news lately, and not in a good way.  Its use, particularly by law enforcement agencies, has become more widely known, as have some of its shortcomings.  At best, it is still weak at accurately identifying minority faces (or women), and at worst it poses significant privacy concerns for, well, everyone.  The fact that someone using such software could identify you in a crowd using publicly available photographs, and then track your past and subsequent movements, is the essence of Big Brother.  

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Health in 2 Point 00, Episode 115 | Olive, Bright.md and AristaMD

Today on Health in 2 Point 00, we have a no-nonsense April 1st episode—with deals this time! On Episode 115, Jess asks me about Olive raising $51 million for its AI-enabled revenue cycle management solution, Bright.md raising an $8 million Series C for its asynchronous telemedicine platform, and AristaMD raising $18 million for a different sort of telemedicine, eConsults, which allow primary care physicians to consult with specialists virtually. —Matthew Holt

Can AI diagnose COVID-19 on CT scans? Can humans?

Vidur Mahajan
Vasanth Venugopal

By VASANTH VENUGOPAL MD and VIDUR MAHAJAN MBBS, MBA

What can Artificial Intelligence (AI) do?

AI can, simply put, do two things – one, it can do what humans can do. These are tasks like looking at CCTV cameras, detecting faces of people, or in this case, read CT scans and identify ‘findings’ of pneumonia that radiologists can otherwise also find – just that this happens automatically and fast. Two, AI can do things that humans can’t do – like telling you the exact time it would take you to go from point A to point B (i.e. Google maps), or like in this case, diagnose COVID-19 pneumonia on a CT scan.

Pneumonia on CT scans?

Pneumonia, an infection of the lungs, is a killer disease. According to WHO statistics from 2015, Community Acquired Pneumonia (CAP) is the deadliest communicable disease and third leading cause of mortality worldwide leading to 3.2 million deaths every year.

Pneumonias can be classified in many ways, including the type of infectious agent (etiology), source of infection and pattern of lung involvement. From an etiological classification perspective, the most common causative agents of pneumonia are bacteria (typical like Pneumococcus, H.Influenza and atypical like Legionella, Mycoplasma), viral (Influenza, Respiratory Syncytial Virus, Parainfluenza, and adenoviruses) and fungi (Histoplasma & Pneumocystis Carinii).

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The FDA Needs to Set Standards for Using Artificial Intelligence in Drug Development

By CHARLES K. FISHER, PhD

Artificial intelligence has become a crucial part of our technological infrastructure and the brain underlying many consumer devices. In less than a decade, machine learning algorithms based on deep neural networks evolved from recognizing cats in videos to enabling your smartphone to perform real-time translation between 27 different languages. This progress has sparked the use of AI in drug discovery and development.

Artificial intelligence can improve efficiency and outcomes in drug development across therapeutic areas. For example, companies are developing AI technologies that hold the promise of preventing serious adverse events in clinical trials by identifying high-risk individuals before they enroll. Clinical trials could be made more efficient by using artificial intelligence to incorporate other data sources, such as historical control arms or real-world data. AI technologies could also be used to magnify therapeutic responses by identifying biomarkers that enable precise targeting of patient subpopulations in complex indications.

Innovation in each of these areas would provide substantial benefits to those who volunteer to take part in trials, not to mention downstream benefits to the ultimate users of new medicines.

Misapplication of these technologies, however, can have unintended harmful consequences. To see how a good idea can turn bad, just look at what’s happened with social media since the rise of algorithms. Misinformation spreads faster than the truth, and our leaders are scrambling to protect our political systems.

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Artificial Intelligence vs. Tuberculosis – Part 2

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By SAURABH JHA, MD

This is the part two of a three-part series. Catch up on Part One here.

Clever Hans

Preetham Srinivas, the head of the chest radiograph project in Qure.ai, summoned Bhargava Reddy, Manoj Tadepalli, and Tarun Raj to the meeting room.

“Get ready for an all-nighter, boys,” said Preetham.

Qure’s scientists began investigating the algorithm’s mysteriously high performance on chest radiographs from a new hospital. To recap, the algorithm had an area under the receiver operating characteristic curve (AUC) of 1 – that’s 100 % on multiple-choice question test.

“Someone leaked the paper to AI,” laughed Manoj.

“It’s an engineering college joke,” explained Bhargava. “It means that you saw the questions before the exam. It happens sometimes in India when rich people buy the exam papers.”

Just because you know the questions doesn’t mean you know the answers. And AI wasn’t rich enough to buy the AUC.

The four lads were school friends from Andhra Pradesh. They had all studied computer science at the Indian Institute of Technology (IIT), a freaky improbability given that only hundred out of a million aspiring youths are selected to this most coveted discipline in India’s most coveted institute. They had revised for exams together, pulling all-nighters – in working together, they worked harder and made work more fun.

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Radiology Gets an “App Store” for its AI Tools | Ben Panter, Blackford Analysis

AI in radiology is not new. In fact, the field is swarming with various apps and tools seeking to find a place in the radiologist’s toolkit to get more value out of medical imaging and improve patient care. So, how does a radiology team pick which tools to invest in? Enter Blackford Analysis, a health tech startup that has, simply put, designed an “app store” for radiology departments that liberates access to life-saving tech for radiologists. CEO Ben Panter explains how the platform not only gives radiologists access to a curated group of best-in-class AI radiology tools, but does so en-mass to circumvent the need for one-off approvals from hospital administrators and procurement teams.

Filmed at Bayer G4A Signing Day in Berlin, Germany, October 2019.

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Explain yourself, machine. Producing simple text descriptions for AI interpretability

By LUKE OAKDEN-RAYNER, MD

One big theme in AI research has been the idea of interpretability. How should AI systems explain their decisions to engender trust in their human users? Can we trust a decision if we don’t understand the factors that informed it?

I’ll have a lot more to say on the latter question some other time, which is philosophical rather than technical in nature, but today I wanted to share some of our research into the first question. Can our models explain their decisions in a way that can convince humans to trust them?

Decisions, decisions

I am a radiologist, which makes me something of an expert in the field of human image analysis. We are often asked to explain our assessment of an image, to our colleagues or other doctors or patients. In general, there are two things we express.

  1. What part of the image we are looking at.
  2. What specific features we are seeing in the image.

This is partially what a radiology report is. We describe a feature, give a location, and then synthesise a conclusion. For example:

There is an irregular mass with microcalcification in the upper outer quadrant of the breast. Findings are consistent with malignancy.

You don’t need to understand the words I used here, but the point is that the features (irregular mass, microcalcification) are consistent with the diagnosis (breast cancer, malignancy). A doctor reading this report already sees internal consistency, and that reassures them that the report isn’t wrong. An common example of a wrong report could be:

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RSNA 2019 AI Round-Up

Shah Islam
Hugh Harvey

By HUGH HARVEY, MBBS and SHAH ISLAM, MBBS

AI in medical imaging entered the consciousness of radiologists just a few years ago, notably peaking in 2016 when Geoffrey Hinton declared radiologists’ time was up, swiftly followed by the first AI startups booking exhibiting booths at RSNA. Three years on, the sheer number and scale of AI-focussed offerings has gathered significant pace, so much so that this year a decision was made by the RSNA organising committee to move the ever-growing AI showcase to a new space located in the lower level of the North Hall. In some ways it made sense to offer a larger, dedicated show hall to this expanding field, and in others, not so much. With so many startups, wiggle room for booths was always going to be an issue, however integration of AI into the workflow was supposed to be a key theme this year, made distinctly futile by this purposeful and needless segregation.

By moving the location, the show hall for AI startups was made more difficult to find, with many vendors verbalising how their natural booth footfall was not as substantial as last year when AI was upstairs next to the big-boy OEM players. One witty critic quipped that the only way to find it was to ‘follow the smell of burning VC money, down to the basement’. Indeed, at a conference where the average step count for the week can easily hit 30 miles or over, adding in an extra few minutes walk may well have put some of the less fleet-of-foot off. Several startup CEOs told us that the clientele arriving at their booths were the dedicated few, firming up existing deals, rather than new potential customers seeking a glimpse of a utopian future. At a time when startups are desperate for traction, this could have a disastrous knock-on effect on this as-yet nascent industry.

It wasn’t just the added distance that caused concern, however. By placing the entire startup ecosystem in an underground bunker there was an overwhelming feeling that the RSNA conference had somehow buried the AI startups alive in an open grave. There were certainly a couple of tombstones on the show floor — wide open gaps where larger booths should have been, scaled back by companies double-checking their diminishing VC-funded runway. Zombie copycat booths from South Korea and China had also appeared, and to top it off, the very first booth you came across was none other than Deep Radiology, a company so ineptly marketed and indescribably mysterious, that entering the show hall felt like you’d entered some sort of twilight zone for AI, rather than the sparky, buzzing and upbeat showcase it was last year. It should now be clear to everyone who attended that Gartner’s hype curve has well and truly been swung, and we are swiftly heading into deep disillusionment.

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