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Ayden Jacob

Is Reimbursement for AI Technology Stalling Clinical Adoption?

Adoption, ROI

The nature of the U.S. healthcare system dictates that technological advancements in medicine must offer both clinical benefits and financial incentives if they seek to gain widespread adoption within the healthcare ecosystem. A recent paper in JACR shared that reimbursement for AI in radiology may only become a reality when these technologies demonstrate undeniable value. Here, I make two brief arguments: 

  1. The value of AI in radiology has been proven. 
  2. Direct reimbursement for any AI tool assisting in the reading room need not be an impediment for readily adopting these technologies. 

Defining Value

Economists working at the juncture of healthcare have long debated how to define value within medicine. 

If an intervention provides better outcomes than the standard of care, but costs the patient significantly more than our current standard, is this intervention deemed “more” valuable? It’s debatable. 

If a novel technology is useful for some patients, but excludes a large portion of the population, would we value its utilization? Maybe. 

These are complex issues that often draw upon a myriad of fields to generate a thoughtful, transparent and trustworthy conclusion. When defining value within radiology, it may be the case that governments are not ready to reimburse hospitals and doctors for an AI-based tool, but the absence of this financial component does not call into question the value of the technology. 

I would argue that the fact that health systems are rapidly adopting these technologies without a clear path for reimbursement demonstrates that the value of AI in medicine is well beyond the confines of a CPT code. Aidoc is an always on service which provides an all-encompassing ROI, from better health outcomes to improved efficiencies. 

“When defining value within radiology, it may be the case that governments are not ready to reimburse hospitals and doctors for an AI-based tool, but the absence of this financial component does not call into question the value of the technology.”

Current Financial Landscape

The JACR article highlights several technical components of reimbursement. I would like to focus on one particular aspect the authors put forth. The Medicare Physician Fee Schedule runs on a budget-neutral basis. Inherently, this restricts direct reimbursements for one innovation as it curtails payment to another.

A payment towards an AI solution would need to be offset by diminishing the reimbursement for another healthcare service. It is due to this that I believe the financial value of an AI solution like Aidoc must be demonstrated well beyond a direct reimbursement for the technology itself.

Clinical Value

Regardless of the current landscape of governmental and insurance reimbursement, there is undoubtedly a strong financial ROI for AI products in medicine. Waiting for AI-specific reimbursement rates to develop within the reading room will only delay the highest quality of care currently available.

Granted, the entire field is advocating for reimbursements, as it is an integral part of advancing our field forward. Nevertheless, the time to adopt an AI solution in the reading room was yesterday.

Realistic Reimbursement

We can all agree that doctors should be rewarded financially for the work they do. Within healthcare, physicians are grateful that the RUC committee holistically educates our partners in government about appropriate measures of revenue for a given healthcare intervention. A technology that amplifies a physician’s capability to treat patients is worthy of appropriate reimbursement. 

However, It would be inconceivable and fiscally irresponsible to assume that an artificial intelligence software which runs off of computer vision on hundreds of thousands of scans  should be reimbursed on a fee-for-service methodology. A value-based imaging approach may be best suited for reimbursement. 

We are living in an era where radiological imaging has unfortunately become akin to another lab value. In this setting, there is explosive and unrestrained growth in imaging volume. The majority of both hospital-based and outpatient imaging is negative. Insurance companies and government agencies are not likely to reimburse for algorithms on each scan. 

Therefore, as the value of AI in radiology is clearly rooted in improving clinical outcomes and workforce efficiencies, there should be no hesitancy to adopting AI while reimbursement strategies are slowly parsed together to catch up to a movement in healthcare that is changing the future of patient care. 

Learn more about the financial realities surrounding AI adoption and its potential for ROI by exploring our ebook “Demystifying the ROI of Clinical AI: Leveraging real-world data to determine your facility’s ROI potential.” Click here to download your free copy.

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