One of the emerging use cases for AI in radiology is as a safety net that could help hospitals avoid malpractice cases by catching errors made by radiologists before they can cause patient harm. The topic was reviewed in a Sunday presentation at RSNA 2024.
Clinical AI adoption has been held back by economic factors such as limited reimbursement and the lack of strong return on investment.
- Healthcare providers want to know that their AI investments will pay off, either through direct reimbursement from payors or improved operational efficiency.
At the same time, providers face rising malpractice risk, with a number of recent high-profile legal cases.
- For example, a New York hospital was hit with a $120M verdict after a resident physician working the night shift missed a pulmonary embolism.
Could AI limit risk by acting as a backstop to radiologists?
- At RSNA 2024, Benjamin Strong, MD, chief medical officer at vRad, described how they have deployed AI as a QA safety net.
vRad mostly develops its own AI algorithms, with the first algorithm deployed in 2015.
- vRad is running AI algorithms as a backstop for 13 critical pathologies, from aortic dissection to superior mesenteric artery occlusion.
vRad’s QA workflow begins after the radiologist issues a final report (without using AI), and an algorithm then reviews the report automatically.
- If discrepancies are found the report is sent to a second radiologist, who can kick the study back to the original radiologist if they believe an error has occurred. The entire process takes 20 minutes.
In a review of the program over one year, vRad found …
- Corrections were made for about 1.5k diagnoses out of 6.7M exams.
- The top five AI models accounted for over $8M in medical malpractice savings.
- Three pathologies – spinal epidural abscess, aortic dissection, and ischemic bowel due to SMA occlusion – would have amounted to $18M in payouts over four years.
- Adding intracranial hemorrhage and pulmonary embolism creates what Strong called the “Big Five” of pathologies that are either the most frequently missed or the most expensive when missed.
The Takeaway
The findings offer an intriguing new use case for AI adoption. Avoiding just one malpractice verdict or settlement would more than pay for the cost of AI installation, in most cases many times over. How’s that for return on investment?
