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Is out-of-network billing – when a patient receives care outside their insurance network – still a problem in radiology? A new study in JACR shows that out-of-network commercial claims have dropped dramatically since 2007.

Out-of-network healthcare has been the focus of a number of legislative efforts in recent years as lawmakers try to protect patients from the financial sting of getting a big bill for services rendered outside their provider’s network.

Probably the centerpiece of this effort is the federal No Surprises Act, which went into effect in January 2022; not only did it cap the amount that patients can be billed for out-of-network services, but it created an independent dispute resolution mechanism for adjudicating disagreement between providers and payors over how much they should be paid.

The IDR mechanism has been the focus of legal wrangling in recent months, but the new study in JACR indicates that it might not be getting much use after all, at least in radiology.

Researchers from the ACR’s Harvey L. Neiman Health Policy Institute analyzed 80M commercial claims for radiology services from 2007 to 2021, finding…

Out-of-network radiology claims fell dramatically (to 1.1% vs. 13%)
Out-of-network claims fell for inpatient stays (to 1.4% vs. 10%)
Claims also fell for emergency visits (to 0.4% vs. 3.9%)
By modality, most claims were for X-ray (57%), followed by ultrasound and CT (15% each)
By 2021, radiologists practiced almost exclusively in-network

What’s the reason for the dramatic decline? The study authors credit good-faith negotiations between radiology practices and commercial payors, as well as the impact of state surprise billing laws (the study period occurred before the federal No Surprises Act went into effect).

Other possible factors include consolidation among practices, hospitals, and payors; expansion of academic centers into communities; and the COVID-19 pandemic.

The Takeaway

The JACR study is welcome news for both patients and radiology practices. Patients are less likely to be hit with surprise medical charges, while practices are less likely to have to fight through the IDR process to resolve claims. In the end, everybody wins – even insurance companies.

A new article in JACR highlights the economic barriers that are limiting wider adoption of AI in healthcare in the US. The study paints a picture of how the complex nature of Medicare reimbursement puts the country at risk of falling behind other nations in the quest to implement healthcare AI on a national scale.

The success of any new medical technology in the US has always been linked to whether physicians can get reimbursed for using it. But there are a variety of paths to reimbursement in the Medicare system, each one with its own rules and idiosyncrasies.

The establishment of the NTAP program was thought to be a milestone in paying for AI for inpatients, for example, but the JACR authors note that NTAP payments are time-limited for no more than three years. A variety of other factors are limiting AI reimbursement, including …

All of the AI payments approved under the NTAP program have expired, and as such no AI algorithm is being reimbursed under NTAP
Budget-neutral requirements in the Medicare Physician Fee Schedule mean that AI reimbursement is often a zero-sum game. Payments made for one service (such as AI) must be offset by reductions for something else
Only one imaging AI algorithm has successfully navigated CMS to achieve Category I reimbursement in the Physician Fee Schedule, starting in 2024 for fractional flow reserve (FFR) analysis

Standing in stark contrast to the Medicare system is the NHS in the UK, where regulators see AI as an invaluable tool to address chronic workforce shortages in radiology and are taking aggressive action to promote its adoption. Not only has NHS announced a £21M fund to fuel AI adoption, but it is mulling the implementation of a national platform to enable AI algorithms to be accessed within standard radiology workflow.

The Takeaway

The JACR article illustrates how Medicare’s Byzantine reimbursement structure puts barriers in the path of wider AI adoption. Although there have been some reimbursement victories such as NTAP, these have been temporary, and the fact that only one radiology AI algorithm has achieved a Category I CPT code must be a sobering thought to AI proponents.

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