Podcast Short 90 sec July 21, 2026From Season 2, Episode 5

Why Mid-Revenue Cycle Must Be a Proactive Control Point

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Tami McMasters Gomez·Executive Director of Mid-Revenue Cycle, UC Davis Health
In this clip

Tami McMasters Gomez explains how she prioritizes mid-revenue cycle operations at UC Davis Health around a single objective: preventing revenue leakage before claims are ever dropped or coded. Her framework focuses upstream on authorization accuracy, medical necessity documentation, and real-time CDI support during the actual patient admission rather than retrospective review, with downstream denial prevention backed by root cause analysis and payer policy intelligence targeting high-impact areas like outpatient procedures, infusions, and imaging.

Key Takeaway

Mid-revenue cycle teams that anchor priorities around real-time authorization accuracy, clinical documentation integrity, and payer policy intelligence become proactive control points that stop revenue from becoming at risk, rather than reactive functions that recover it after it is lost.

“My goal for the mid-revenue cycle is to act as a control point that protects revenue before it even becomes at risk from an AR perspective.”

Tami McMasters Gomez, Executive Director of Mid-Revenue Cycle, UC Davis Health

Claims Denial ManagementAgentic AI
From the full episode

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Tami McMasters Gomez
Season 2 · Episode 5 · 37 min

Denials Prevention at the Source: Autonomous Coding, CDI, and AI Governance in the Mid-Revenue Cycle

Tami McMasters Gomez · Executive Director of Mid-Revenue Cycle, UC Davis Health

Tami McMasters Gomez describes how she leads the mid-revenue cycle at UC Davis Health as a proactive control point focused on denials prevention at the source, stopping revenue loss before claims are ever coded or dropped. She explains the framework she uses to anchor priorities around authorization accuracy, real-time clinical documentation integrity, and upstream claims denial management, and describes the bold decision to discontinue the CDI and coding reconciliation process, which drove a 33 percent increase in productivity alongside coding accuracy near 99.9 percent. The conversation covers two active autonomous coding implementations across radiology and outpatient evaluation and management coding, the human-in-the-loop quality assurance model that governs AI accuracy thresholds and direct-to-bill decisions, and the write-off avoidance task force she built to create cross-functional alignment among patient access, clinical, mid-cycle, and back-end teams. Tami also shares her perspective on where agentic AI will create transformational impact in the mid-revenue cycle through denials management automation and embedded clinical workflow intelligence, the governance challenges introduced by California legislation restricting AI in clinical decision-making, and the leadership philosophy she draws from trust-based relationships and empathic change management.

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