Pragmatic AI Adoption for Revenue Cycle Leaders
David Kelly frames AI adoption in revenue cycle as operationally necessary given ongoing talent and labor gaps, but cautions that a meaningful share of vendor claims has proven to be hype. His own organization entered a claims denial management development partnership focused on fighting payer DRG takebacks, requiring InfoSec and compliance sign-off and negotiating a 12-month development timeline after the vendor originally proposed six. His guidance: go in with eyes open, work with proven or rigorously vetted partners, and accept that implementation always takes longer than projected.
Key Takeaway
AI adoption in revenue cycle is increasingly non-negotiable given labor constraints, but vendor selection must be disciplined. Structured contracts, realistic development timelines, and compliance vetting protect organizations from both the risk of hype and the cost of standing still.
“You cannot be an ostrich on this and you cannot just do nothing.”
David Kelly, Chief of Ambulatory Operations & Vice President of Revenue Cycle, Mary Rutan Health
From the clip to strategy.
The themes our guests cover are the same ones our executive guides and ROI calculators are built around. If this clip resonated, here’s where to take it next.
Claims Denial Management, powered by agentic AI
How ArceeHQ catches denials before they happen, resolves them automatically, and gives your reviewers only the cases that need a human.
See the solutionReducing claim denials with agentic AI
A practical framework for revenue cycle leaders. Covers the operating model shift, what to measure, and where automation actually pays.
Read the guideClaims denial ROI calculator
Model the revenue impact of a first-pass rate improvement at your health system. Plug in your denial volume and payer mix.
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