A Brighter Future Lies Ahead for AI Funding in Radiology

Technologies based on artificial intelligence (AI) have been shown to both speed interpretation and elevate human performance throughout the radiology environment. Indeed, many such tools are already being used to improve workflows, reduce contrast usage and radiation dose, and shorten report turnaround times.

Nevertheless, questions surrounding payment models and return on investment (ROI) on the use of AI-based tools have been powerful barriers to their widespread implementation within radiology.

For example, will interpreting physicians pay out of their own pockets for a technology that triages emergent cases, facilitates interpretation by suggesting diagnoses, detects off-search findings, and validates reports? If the combination of clinician and machine improves outcomes, will—should—malpractice insurers support that teamwork? And could—should—payers award higher payouts to imaging practices that employ AI?

Developments of the past several years are beginning to answer these questions and provide cause for optimism in the radiology community.

In 2017, the Centers for Medicare and Medicaid Services (CMS) awarded a new technology ambulatory payment classification for Heartflow’s fractional flow reserve (FFR)/CT Analysis software. The CMS now reimburses practices $1,450.50 for the technical component of tests using the software, which helps to assess patients with suspected coronary artery disease.

In August, the CMS announced CPT^® code 9225X which, beginning Jan. 1, authorizes the use of, and reimbursement for, autonomous AI to diagnose diabetic retinopathy and macular edema in primary care and other healthcare settings. This step, specifically focusing on Digital Diagnostics’ FDA de novo-authorized IDx-DR, marks a major milestone for population health and the ability to expand specialty diagnoses into primary care settings.

Third, in a groundbreaking ruling issued in September, the CMS granted New Technology Add-on Payment (NTAP) status to Viz.ai’s Viz LVO, a deep learning (DL) algorithm that analyzes CT angiography exams for large vessel occlusion (LVO)-related stroke and immediately communicates that information to stroke treatment teams.

The NTAP program was established in 2001 (see sidebar) to support adoption of cutting-edge technologies that demonstrate substantial clinical improvement over existing tools and to ensure early availability to Medicare patients.

Viz.ai demonstrated that Viz LVO significantly reduces time to treatment and improves patient outcomes in stroke. As a result, Medicare will pay $1,040 over and above the standard DRG rate per use of the algorithm in stroke patients.

The company estimates about half of Medicare patients who receive Viz LVO analysis will qualify for payment; it also believes private payers may consider reimbursement in the future. Importantly, the standard reimbursement for CT interpretation is not affected by the NTAP. Other vendors that offer stroke-related AI applications, such as AIDOC and RAPID, are also eligible for, and should ultimately benefit from NTAP.

While CMS determinations have shifted the paradigm for AI developers to clinical end points like reduction of costs and downstream procedures, as well as outcomes, they also could lead to reimbursement for other potential AI-based applications, particularly those that improve care and enhance the patient experience. While they don’t open the floodgates to widespread reimbursement, these decisions do represent the first trickles of success in the pursuit of increased funding for AI in radiology.

To be sure, how to pay for AI implementation in radiology remains a complex issue with many considerations; reimbursement decisions by CMS and private insurers remain critical for some radiology practices. For now, at least, the ROI on tools that augment the scanning process will come from quality, efficiency, and the value of a better patient experience.

While CMS determinations have shifted the paradigm for AI developers to clinical end points like reduction of of costs and downstream procedures, as well as outcomes, they also could lead to reimbursement for other AI-based applications, particularly those that improve care and enhance the patient experience. While they don’t open the floodgates to widespread reimbursement, these decisions do represent the first trickles of success in the pursuit of increased funding for AI in radiology.