Ask your clients where the money goes and you’ll hear the same three answers: musculoskeletal, oncology, cardiovascular. All three show up in the same place first: the spine MRI comes before the surgical referral; the chest CT before the oncology claim; and the cardiac risk sits on scans ordered for something else entirely.
But showing up isn’t the same as being seen. Each imaging study answers the one question it was ordered for, then gets filed. The earliest warning system in your clients’ plans sits in the dark. AI is what switches it on, at scale, across scans already paid for.
In a webinar built exclusively for Marsh, we’ll walk through three paybacks: unit cost, which is table stakes and where most radiology programs stop; diagnostic accuracy, which almost no one measures before it sets the referral or the surgery; and early detection, the largest and least used. Measured at scale, accuracy converts into downstream savings you can count across the following twelve months, with an actuarial-validated methodology behind the numbers.
On September 17, we’ll walk through how one program collects all three:
- The day-one math: how the unit-cost savings work and how the program layers onto existing plan and vendor design
- The twelve-month layer: how diagnostic accuracy is measured and how it converts into countable downstream savings
- The data lever: how AI applied to completed imaging studies surfaces surgical risk, hidden cancers, and cardiac findings while the care decision is still open, running at scale today with employers like Walmart and AT&T
You’ll leave with a three-question framework to run in any client meeting (what did their imaging cost, was it right, and what did it warn them about), plus a Covera Health toolkit and live Q&A.
Covera Health is the platform for advancing radiology quality and diagnostic accuracy. Through its AI-powered quality platform, which connects payors, providers and employers, Covera deploys programs including a nationwide Radiology Centers of Excellence network as well as an AI-supported quality oversight program to reduce diagnostic errors, support early detection, improve patient outcomes, and lower the total cost of care.
