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Lauren Dubinsky, Senior Reporter | November 04, 2024
Artificial intelligence is already disrupting many aspects of our lives, and you don't need to work in an imaging facility to know a transformation in radiology is well underway.
As it stands today, the FDA lists 950 AI- and machine learning-enabled devices that are cleared for commercialization. These tools can do everything from detect cardiovascular abnormalities on CT scans to assist nonspecialists in acquiring diagnostic-quality ultrasound images — but there's a problem: only a small fraction are eligible for reimbursement.
“If we take those AI algorithms that are approved today, you can count on your two hands the number that actually have any form of reimbursement associated with them,” said Peter Shen, head of digital and automation – North America for Siemens Healthineers. “Even amongst the few that do get reimbursement, the vast majority don’t actually get enough reimbursement to cover the cost of the technology.”
For some algorithms, the hospital can apply for a reimbursement code that’s complementary to the procedure. For example, if a CT exam is performed, there might be an opportunity to apply for a reimbursement code for additional calculations. Other algorithms receive a dedicated reimbursement code because CMS recognizes them as a newer, innovative technology, but the downside of those codes is that they are temporary.
“You might have one algorithm that is getting some sort of reimbursement through one of those different pathways and you might have another algorithm that is doing something similar to that original algorithm, but it doesn’t get any reimbursement at all,” said Shen. “This inconsistency and unpredictability makes it hard for health systems to adopt this technology with a full level of confidence that they'll get any sort of reimbursement associated with making that investment in an AI solution.”
He attributes this inconsistency to the rapid evolution of AI over the last couple of years. CMS may be struggling to keep up with the pace of innovation, finding it difficult to determine the best way to provide reimbursement for these technologies.
A dialogue in Washington
Shen has testified numerous times before Congress on various issues related to AI, and during the most recent hearing in February he identified reimbursement as the major obstacle to AI adoption. One of the main things that he wanted to get across is that there are many types of AI, and a productive dialogue about reimbursement means knowing what kind is at issue.
