Dear AuntMinnie Insider,
Distinguishing polyps from haustral folds isn't the only brain teaser confronting today's virtual colonoscopy practice. Billing has been a reliable source of doubt and confusion over the years, and recent changes seem only to have complicated matters.
At last month's International Symposium on Virtual Colonoscopy, Dr. Matthew Barish from the Harvard University School of Medicine in Boston took a thoughtful look at the state of VC billing and reimbursement in the U.S. today.
What he found was a patchwork of confusing rules reflecting a very unfinished state of regulatory affairs. Screening VC has yet to be approved for Medicare payment under the Center for Medicare and Medicaid Services' national coverage decision (NCD) for colorectal cancer screening, for example. As a result, patients often pay for VC screening out of pocket, except when they don't.
Other indications for virtual colonoscopy are clearly reimbursable, at which point the job is to find a method of coding and billing that will both satisfy the rules and get the doctor paid. Here the questions become more interesting, the answers more elusive, and the art of medicine more of an afterthought.
You'll find valuable tips for navigating the billing maze in this issue's Insider Exclusive, published first for our Virtual Colonoscopy Digital Community subscribers.
















![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)



