How Autonomous Coding Tackles High-Volume Radiology Billing

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.