Tag: JACR

Study Shows AI’s Economic Value

Posted on by Brian Casey

One of the biggest criticisms of AI for radiology is that it hasn’t demonstrated its return on investment. Well, a new study in JACR tackles that argument head on, demonstrating AI’s ability to both improve radiologist efficiency and also drive new revenues for imaging facilities. 

AI adoption into radiology workflow on a broad scale will require significant investment, both in financial cost and IT resources. 

  • So far, there have been few studies showing that imaging facilities will get a payback for these investments, especially as Medicare and private insurance reimbursement for AI under CPT codes is limited to fewer than 20 algorithms. 

The new paper analyzes the use of an ROI calculator developed for Bayer’s Calantic platform, a centralized architecture for radiology AI integration and deployment. 

  • The calculator provides an estimate of AI’s value to an enterprise – such as by generating downstream procedures – by comparing workflow without AI to a scenario in which AI is integrated into operations.

The study included inputs for 14 AI algorithms covering thoracic and neurology applications on the Calantic platform, with researchers finding that over five years … 

  • The use of AI generated $3.6M in revenue versus $1.8M in costs, representing payback of $4.51 for every $1 invested
  • Use of the platform generated 1.5k additional diagnoses, resulting in more follow-up scans, hospitalizations, and downstream procedures
  • AI’s ROI jumped to 791% when radiologist time savings were considered
  • These time savings included a reduction of 15 eight-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time  

Although AI led to additional hospitalizations, it’s possible that length of stay was shorter: for example, reprioritization of stroke cases resulted in 264 fewer hospital days for patients with intracerebral hemorrhage. 

  • Executives with Bayer told The Imaging Wire that while the calculator is not publicly available, the company does use it in consultations with health systems about new AI deployments. 

The Takeaway

This study suggests that examining AI through the lens of direct reimbursement for AI-aided imaging services might not be the right way to assess the technology’s real economic value. Although it won’t settle the debate over AI’s economic benefits, the research is a step in the right direction.

Predicting Patient Follow-Up for Imaging Exams

Posted on by Brian Casey

There’s nothing more frustrating than patients who don’t comply with follow-up imaging recommendations. But a new study in JACR not only identifies the factors that can lead to patient non-compliance, it also points the way toward IT tools that could predict who will fall short – and help direct targeted outreach efforts.

The new study focuses specifically on incidental pulmonary nodules, a particularly thorny problem in radiology, especially as CT lung cancer screening ramps up around the world.

  • Prevalence of these nodules can range from 24-51% based on different populations, and while most are benign, a missed nodule could develop into a late-stage lung cancer with poor patient survival. 

Researchers from the University of Pennsylvania wanted to test a set of 13 clinical and socioeconomic factors that could predict lack of follow-up in a group of 1.6k patients who got CT scans from 2016 to 2019. 

  • Next, they evaluated how well these factors worked when fed into several different types of homegrown machine learning models – precursors of a tool that could be implemented clinically – finding …
  • Clinical setting had the strongest association in predicting non-adherence, with patients seen in the inpatient or emergency setting far more likely skip follow-up compared to outpatients (OR=7.3 and 8.6)
  • Patients on Medicaid were more likely to skip follow-up compared to those on Medicare (OR=2)
  • On the other hand, patients with high-risk nodules were less likely to skip follow-up compared to those at low risk (OR=0.25) 
  • Comorbidity was the only one of the 13 factors that was not predictive of follow-up 

The authors hypothesized that the strong association between clinical setting and follow-up was due to the different socio-demographic characteristics of patients typically seen in each environment. 

  • Patients in the outpatient setting often have access to more resources like health insurance, transportation, and health literacy, while those without such resources often have to resort to the emergency department or hospital wards when they become sick enough to require care.

In the next step of the study, the data were fed into four types of machine learning algorithms; all turned in good performance for predicting follow-up adherence, with AUCs ranging from 0.76-0.80. 

The Takeaway

It’s not hard to see the findings from this study ultimately making their way into clinical use as part of some sort of commercial machine-learning algorithm that helps clinicians manage incidental findings. Stay tuned.

Out-of-Network Radiology Claims Fall

Posted on by Brian Casey

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.

Economic Barriers to AI

Posted on by Brian Casey

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.

Get every issue of The Imaging Wire, delivered right to your inbox.

You might also like..

Select All

You're signed up!

It's great to have you as a reader. Check your inbox for a welcome email.

-- The Imaging Wire team

You're all set!