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Final denial rates just hit 14% — a $48.4B revenue hit, up more than 25% year over year. As payer AI gets more sophisticated, a reactive mid-revenue cycle isn't just inefficient. It's a compounding liability.
We surveyed 81 senior revenue cycle leaders and sat down with five C-suite executives to find out what separates health systems building durable mid-cycle performance from those falling behind. The findings reveal a clear ambition-execution gap — and a repeatable playbook for closing it.
"I feel like we're always at the payer's mercy…Even if we're so perfect internally, you still are up against incorrect denials from a payer…It's trying to stay a step ahead of them when you never really are." — VP of Revenue Cycle, Regional IDN
Every health system leader we talked to is wrestling with the same question: how do you build an AI strategy that's more than a pilot, more than a buzzword, and actually durable against an opponent—the payer—that's scaling its own AI faster than most provider organizations were built to handle?
This report gives you the benchmark data, the maturity framework, and the executive perspective to answer it.
A framework for benchmarking your own organization against the MRCM AI Maturity Curve—Traditional, Developing, and AI-Unified
Data on how clinical-revenue cycle alignment (joint training, shared KPIs, co-developed CDI standards) correlates with AI maturity
A clear-eyed look at what outsourcing actually costs you in visibility—and how some systems are reclaiming control without reclaiming headcount
The ROI metrics revenue cycle leaders say actually justify AI investment—and the ones that don't hold up over time
Direct quotes and sequencing logic from C-suite revenue cycle executives at leading health systems
Walk through what clinical AI surfaces in your own data.
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