Why AI Governance Is Not a Bad Word in Revenue Cycle with Christina Slemp | Podcast Shorts from ArceeHQ

In this clip

Christina Slemp pushes back on the perfectionism trap in AI adoption and the organizational resistance to governance that often accompanies it. She argues that chasing 100 percent accuracy before moving is the enemy of progress, and that governance is not bureaucracy but the structure that keeps AI safe in an environment where the stakes involve patient data, compliance requirements, and claims going out the door. She makes the case that AI governance in revenue cycle deserves the same rigor applied to clinical governance, because the consequences of getting it wrong are just as real even though the work is financial rather than clinical.

Key Takeaway

AI governance in revenue cycle is not a barrier to adoption. It is the mechanism that ensures AI runs compliantly, consistently, and safely, and organizations that treat it as an obstacle rather than a foundation will pay for that choice in compliance exposure and process failures.

“Governance should never be a bad word. Governance should be: this is to make sure that we’re doing it compliantly, we’re doing it correctly, and that we’re doing it safely.”
Christina Slemp, System Vice President of Revenue Cycle Financial Services, UNC Health

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Christina Slemp

Season 2 · Episode 7 · 44 min

Rethinking Denials Management: AI-Driven Prediction, Sprint-Based Root Cause Resolution, and Trust as the Foundation of Revenue Cycle Change

Christina Slemp · System Vice President of Revenue Cycle Financial Services, UNC Health

Christina Slemp, System Vice President of Revenue Cycle Financial Services at UNC Health, brings a cross-systems perspective on claims denial management shaped by more than 25 years of leading revenue cycle operations across hospital systems, health plans, and payer contracting. She describes the multi-dimensional framework her teams use to approach denials, from six-week microtargeted sprint cycles with structured root cause analysis and cross-functional executive committee oversight, to the AI and RPA combination she considers best practice for automating appeals at scale. The conversation covers the organizational restructuring she guided at UNC Health, which consolidated professional and hospital billing under an enterprise AI and automation strategy, her view that predictive AI for anticipating payer behavior represents the next step beyond reactive denial management, and the emerging use of AI to analyze payer policy changes and quantify their financial impact before organizations fall behind. Christina closes with her perspective on the leadership behaviors that drive lasting change in revenue cycle, including why data alone does not move people and why trust is the only sustainable currency for change management.

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