by
John W. Mitchell, Senior Correspondent | July 30, 2018
A just-published paper delves into the uncertainties of how AI overhead costs will change imaging medical economics, but there may be a silver lining — especially for radiologists in private practice who rely on the professional payment component.
“When we look at it (AI), we don’t necessarily look at if we can get more done faster. If I can get my same volume of work done instead of in a ten-hour day, in a nine-hour day, that gives me another hour I can work with patients, the hospitals, and administrators to further patient care and actually achieve better outcomes," Dr. Kurt Schoppe, the author and chair of the Reimbursement Committee of the American College of Radiology Economics Commission told HCB News.
Schoppe, who is also a practicing radiologist in a large North Texas private practice, just published his paper, “Artificial Intelligence: Who Pays and How”, in the recent issue of the
Journal of the American College of Radiology.
To be sure, there are real challenges around how the incremental costs of AI development will impact the imaging specialty, AI vendors, and payers. While AI costs – and even efficiencies – are likely to be disruptive, the rise of AI in radiology is inevitable, according to Schoppe.
As reported numerous times recently by HCB News, big data, machine AI learning has already demonstrated it can often read images more efficiently than humans. But for radiologists, this means they will get more time – and enjoyment, according to Shoppe – back in their practice. This is because radiologists will be relieved of many repetitive and mundane reading tasks.
However, one of the big questions he asks is: how will such efficiencies be reflected in payment policies to radiologists?
“For artificial intelligence, there may not be a physician work component, so the RUC and Medicare likely won't acknowledge the technical component of the reimbursement," Schoppe explained. “This is why vendors need to understand the nuance here, because it affects how they calculate potential returns on investment.”
It could be that AI development costs cannot be recouped; it might just be a cost of doing business in the future. But the trade-off may come in lower malpractice costs, lower dose, and improved quality metrics, Schoppe said. Another issue is how will AI costs and efficiencies fit into the drive toward value-based healthcare delivery and payments? Such questions need to be parsed out ahead of time, Schoppe maintained.
“AI is different in a lot of ways,” he said. “We’re adding a new cost into the system that must be accounted for. I’m a militant pragmatist – we have to wade through the hype (of AI).”
