Payers are increasingly using AI to scan claims, flag anomalies, and trigger denials or audits automatically—often without transparency and long before anyone on the hospital side understands what happened. As these systems accelerate, hospitals face growing exposure, especially when documentation, data flow, and internal AI tools aren’t aligned with how payer models interpret clinical and financial information. This session unpacks the mechanics behind automated decision-making, why even accurate claims can be flagged, and how missing audit trails or inconsistent documentation can undermine appeals.

We’ll examine real-world scenarios where automation created unexpected delays, recoupments, and compliance risk, and discuss how emerging policies and industry reforms are pushing hospitals toward greater visibility, traceability, and governance. The session concludes with a practical oversight playbook to help leaders strengthen defensibility across clinical, revenue cycle, and IT operations in a world where algorithms often make the first move.

Learning Objectives

By the end of this session, attendees will be able to:

  1. Explain how payer AI models drive automated denials, audit triggers, and prior authorization decisions—and why hospitals lose visibility without oversight.
  2. Identify the internal vulnerabilities created by missing audit trails, documentation inconsistencies, siloed workflows, and unmonitored AI tools used in coding or documentation.
  3. Apply a foundational AI oversight framework—including data governance, interoperability readiness, and cross-functional governance—to reduce revenue and compliance risk.

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