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How Does Radiology AI Fit Into Value-Based Healthcare?

May 31, 2018 -- NATIONAL HARBOR, MD - Artificial intelligence (AI) technology shows considerable potential for aiding radiology's transition to value-based payment models, but these algorithms must yield quantifiable benefits to find a niche, according to presentations on May 30 at the Spring 2018 Data Science Summit.

To succeed in this new era, AI algorithms must help radiology practices show measurable improvements in quality and/or cost, said Dr. Gregory Nicola, chair of the American College of Radiology's (ACR) committee on the Medicare Access and CHIP Reauthorization Act (MACRA).

"[As a vendor], if your algorithm is not improving our population's health and reducing the cost of care, or at the very least keeping the cost of care the same, I think you're going to probably find very little traction for it in the end," he said. "This is not the fee-for-service environment where a PET scanner can be confused for a dryer or dishwasher and somebody would buy it. We're not in that market anymore."

He discussed the role of and challenges for AI in value-based payment paradigms at the summit, which was held by the ACR Data Science Institute and the Society for Imaging Informatics in Medicine (SIIM) in advance of SIIM's annual meeting this week.

A Radically Different Paradigm

Although they have various iterations, value-based payments models are radically different than fee-for-service models and have different implications for the adoption of AI in radiology, Nicola said. There are two universal aspects to both approaches, though.

"Whoever makes the AI technology needs to be paid, or you're not going to make the technology," Nicola said. "Whoever buys the technology to treat patients has to get paid, or they're not going to buy the technology."

The models differ, however, in who starts the payments. In fee-for-service environments, it's the patient or his or her insurance company. That's not the case under value-based payments.

Negative Consequences

The fee-for-service model has had its share of detractors. Nicola noted that there are absolute, negative consequences of paying everyone for every service they provide to patients.

For example, health data from the Organization for Economic Co-Operation and Development (OECD) show that the number of advanced imaging scanners and the number of scans performed on patients in the U.S. are roughly twice the OECD average -- with little change in outcomes, he said.

"And this is probably in part because of the fee-for-service system," Nicola said. "It has allowed lots of people to buy this technology, lots of vendors to make this technology, and plenty of patients and insurance companies to pay for it because of the methodology. This has led to high-cost healthcare with little outcomes. That means there's a lot of waste in our system, and a lot of it is overutilization of healthcare resources."

Gradual Transition

The passage of MACRA in 2015 advanced the concept of value-based reimbursement by establishing the Medicare Quality Payment Program (QPP). There are now two different payment paradigms: the Merit-Based Incentive Payment System (MIPS) -- a fee-for-service payment system with value measurements that increase or decrease