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.
