On September 3, the U.S. Centers for Medicare and Medicaid Services (CMS) approved its first reimbursement for a radiology artificial intelligence (AI) software application, awarding Viz.ai with a new technology add-on payment (NTAP) for use of its ContaCT stroke detection algorithm in brain CT scans. Under the NTAP, CMS will pay up to $1,040 if certain conditions are met. This is in addition to the radiologist professional fee.
Reimbursement for AI software use has been one of the biggest Achilles' heels that the industry has had to deal with, so this was huge news. But if you haven't grasped the significance of this announcement, you're not alone. Educating the AI marketplace remains another major ongoing challenge for the sector.
Ineffective market education
A lack of financial resources isn't to blame. Flush with venture capital (VC) money, some of these companies have hired the biggest, baddest public relations and advertising agencies the VC money can buy. Sadly, many of these firms simply don't have a clue what AI is and how to promote it.
Despite many AI companies getting tens of millions of dollars in venture capital, the amount of money spent on market education by some of these firms would maybe buy lunch for the software developers who are getting top dollar to develop products no one may ever know about. Now, VC money is important for both product and company development. Few will debate that, especially in a still-developing marketplace.
But AI companies tend to invest nearly all the VC money in software development. The money is also often spent fairly autonomously, as well, with little to no oversight -- or at least not oversight by anyone who understands the market. Most VC firms would rather believe the optimistic projections that influenced them to dole out the money and not necessarily the reality of the situation in a slow-developing market.
Most VC companies also like to start to see a return on investment (ROI) at around two years. In AI, four years would be stellar and six about average -- if the company can hold on that long and the VC firms keep investing in the company. Just like what happened with PACS, excuses are inevitably given for not achieving the initially promoted goals. And these excuses are believed because the VC firms are often as clueless as many of the vendors are.
A bit harsh? Maybe, but I have seen this for several decades in the PACS marketplace with people suddenly scrambling to find new jobs when the VC firms finally pull the plug and say, "No mas." That's a shame because there were quite a few good products developed in PACS that failed due to having little to no marketing support.
No Field of Dreams
The AI marketplace isn't like the movie "Field of Dreams," in which Kevin Costner's character builds a baseball field on his farm and then several dead baseball players just show up to play.
In the AI market, nobody will show up to buy the software unless they know about the technology and why they should buy it. So market education is a must to overcome the plethora of mistakes that have already been made in the radiology AI sector.
How are these mistakes overcome? Apple's Steve Jobs said it best, "You need to start with the customer experience and work backwards to the technology. You can't start with the technology and try to figure out where you are going to sell it."
Talking about the technology and not the customer experience has been the approach AI has taken since its inception. That is very slowly starting to change, but it may be too little, too late to change some minds about AI.
Radiologists tend to view AI as a competitor, and hospitals have seen it as another cost center with value no better than the radiologist can provide. Both fail to grasp that AI and radiologists working together to help make the diagnosis are much better than either is apart.
It has always been my contention that AI is not a competitive technology to radiologists, but a complementary one. AI can help a radiologist provide a better diagnosis and offer a second opinion.
Finally getting paid for using AI also eliminates one of the primary barriers to using the technology. Yet paranoia still seems to exist relative to AI's mere existence, let alone its use.
As an industry, AI has shot itself in the foot for way too long. So, what is the significance of the industry finally getting reimbursement for studies read by AI? It's a way for hospitals and maybe the radiologist to make money -- pure and simple. And money is the name of the game.
If a hospital can make an additional $X using a stroke algorithm -- in addition to using the radiologist's diagnostic interpretation and being paid for it -- don't you think it'll use it? Hell, yes. The same holds true for every other AI algorithm out there.
Once payment for using algorithms is approved, you are going to see this market start to take off. The RSNA is holding a contest this year to see who can develop the best algorithm for pulmonary embolism. That, no doubt, will be the next area for reimbursement, followed closely by AI for screening mammography.
A compelling argument
Whether we like it or not -- or accept it and talk at length about improved patient care and all AI's other benefits -- one of the compelling reasons to use AI involves either making or saving money. Money makes the world go round. If you don't believe me, take a look at the "most read" posts on AuntMinnie.com's Forums. You'll see that many of them deal with money. Whether it is making, saving or, spending it, radiologists aren't immune to this thought process.
Don't want to use AI? Tough. You may not have a choice in the matter. Do I like the thought of radiologist assistants, physician assistants, and nurse practitioners reading studies instead of radiologists? No. That hasn't happened yet and hopefully may not at all, but realistically for some exams it may be inevitable. After all, you already have orthopedists, ob/gyns, and others conducting radiology studies in their offices, and soon emergency departments will be doing and interpreting their own ultrasound scans. How is this any different?
Now, what if you decide you just won't use the AI interpretation that is provided? That leaves the door open to the hospital setting its own ground rules, also known as, "Use it or else." And if, God forbid, you have to defend yourself in a lawsuit in which AI was right (or even wrong) and you didn't look at the AI interpretation because you didn't want to use AI? Open your checkbook and update your CV ... if you can find malpractice insurance that is.
Not about you
I know what I say isn't popular. Rick Warren started his book "The Purpose Driven Life" with four words that define where we are in medicine today as well -- "It's not about you." I don't like it any more than you do, but it is what it is. We are no longer in control of our own destiny. As technology advances, we become less and less in charge of it.
We need to learn more and understand and accept how AI works with an enterprise imaging system/PACS and what the future holds for both technologies. PACS is here to stay, and so too is AI. Some dismiss the financial aspect of AI, but making up to $1,040 per case by using an algorithm is far from a "zero-sum gain" and a "waste of money," as some have said.
Once again, it shows that those who fear AI just don't understand how the technology and application of it therein really works. Nowhere does it also say a radiologist will get paid less for doing the interpretation either -- at least not as of now. It just says that there will be additional payment for the use of AI.
Future financial impact
However, as has been seen in the past, once the radiologist's involvement in the diagnostic interpretation process is determined relative to that of the AI algorithm, then I'm sure the relative value units will be adjusted accordingly. If the AI companies are smart and position AI as an adjunct to the diagnostic interpretation process -- and not as a replacement -- they may be able to minimize any financial impact it will have on the radiologists. Will that happen? That again is anyone's guess.
Radiology tends to focus on the radiology world and not beyond it. Hospitals are also looking closely at how AI can assist cardiology, pathology, and even the emergency department physicians as well. Stroke is the No. 1 cause of adult disability in the United States and the No. 5 cause of death. Research has found that every minute a stroke patient's treatment is delayed leads to an additional one week of disability. This is why suspected strokes are stat cases requiring on average no more than a 15-minute interpretation. Although the AI algorithms being offered today are far from perfect, an average 90% accuracy rate beats 0% every day, especially in the emergency department, where seconds count.
Rick Warren also made another interesting comment in his book: "We are products of our past, but we don't have to be prisoners of it." If we are willing to accept the change and embrace AI just as we accepted that same change with PACS, then there remains hope. Without it, well...
Michael J. Cannavo is known industry-wide as the PACSman. After several decades as an independent PACS consultant, he worked as both a strategic accounts manager and solutions architect with two major PACS vendors. He has now made it back safely from the dark side and is sharing his observations.
His healthcare consulting services for end users include PACS optimization services, system upgrade and proposal reviews, contract reviews, and other areas. The PACSman is also working with imaging and IT vendors developing market-focused messaging as well as sales training programs. He can be reached at email@example.com or by phone at 407-359-0191.
The comments and observations expressed are those of the author and do not necessarily reflect the opinions of AuntMinnie.com.