Revenue Cycle AI Operations Analyst

Core Competencies & Responsibilities

• Audit routine QA and compliance audits on AI-assisted and AI-autonomous outputs across Revenue Cycle workflows.
• Verify outputs against payer rules, federal/state requirements, and internal policies, ensuring decisions are documented and defensible.
• Ensure workflows meet governance principles of auditability, traceability, and reversibility as autonomy increases.
• Perform deep-dive validation in specific domains (e.g., verifying CPT/diagnosis codes accuracy, auditing clinical packet generation).
• Serve as the escalation point for low-confidence, outlier, or high-risk cases flagged by AI, ensuring correct resolution and appropriate handoffs to human teams.
• Maintain an exceptions log, categorize failure modes, and drive corrective actions with owners.
• Conduct quality reviews on external vendor and technology partner performance against established operational standards and SLAs.
• Compile performance data to identify negative trends, outputting findings into operational dashboards and vendor scorecards.
• Partner with leadership to address vendor deficiencies through structured feedback and recommend corrective action plans.
• Monitor operational and financial signals to detect drift and emerging compliance risk, explicitly including denial trends, reimbursement impact, and case mix swings.
• Escalate patterns suggesting systematic error, over/under-treatment of policy logic, or patient financial harm risk.
• Analyze operational workflows end-to-end to identify bottlenecks, redundancies, and upstream clinical failure points that drive rework or compliance risk.
• Establish and maintain standardized procedures to reduce variability as the department shifts from manual processing to AI-augmented workflows.
• Maintain and continuously improve the department's knowledge base, ensuring all operational policies, escalation pathways, and decision trees are documented to support automated workflows.
• Provide targeted, at-elbow coaching and operational support to frontline staff adjusting to new automation tools and changing workflows.
• Develop and deliver brief training interventions or job aids based on validated performance data and identified knowledge gaps.
• Educate frontline teams and stakeholders on recurring error patterns, documentation/inputs that drive AI failures, and how to route/resolve exceptions.
• Coach and mentor staff through operational and technological change with empathy and accountability.
• Support and/or participate in enterprise AI governance processes including risk classification, documentation standards, and ongoing audit cadence aligned to risk tiering.
• Ensure evidence is auditor-ready: decision rationale, data lineage references, versioning of policy logic, and clear records of what changed, when, and why.

Pay

The base pay scale for this position is $72,000.00 - $109,500.00. In addition, this position will be eligible for additional benefits consistent with the role. The salary of the finalist selected for this role will be determined based on various factors, including but not limited to: scope of role, level of experience, education, accomplishments, internal equity, budget, and subject to Fair Market Value evaluation.

Schedule

Work Shift: Day