AI is now deeply embedded across the revenue cycle, but without oversight, it can quietly introduce new compliance risks, data blind spots, and ethical gray areas. This session explores a practical framework for governing AI in healthcare revenue operations — one that balances automation efficiency with accountability and transparency.
We’ll unpack what an AI oversight structure looks like in action, including the governance layers that ensure models align with payer policies, data privacy laws, and organizational values. Attendees will gain a playbook for establishing clear roles, maintaining auditability, and defining thresholds where human judgment must step in.
From establishing AI review committees to building explainability into predictive tools, this session provides a blueprint for compliance leaders and finance executives to control the risks and realize the promise of automation.
Learning Objectives
By the end of this session, attendees will be able to:
- Establish Governance: Learn how to structure AI oversight across finance, compliance, and operations teams.
- Ensure Transparency: Identify what data and audit controls are needed to maintain trust and compliance with automated decisions.
- Balance Human and Machine Judgment: Define when and where human review should complement AI agents to prevent bias and maintain control.
Request a demo of our revenue solutions to learn more.