Full Job Description
Emp Status
Regular Full time
Work Shift
Day (United States of America)
Compensation Range
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. The hiring range listed is a good faith determination of potential compensation at the time of this job advertisement and may be modified in the future.
What you will be doing
Core Competencies & Responsibilities
AI System Oversight & Compliance Auditing
Conduct routine QA and compliance audits on AI-assisted and AI-autonomous outputs across Revenue Cycle workflows (e.g., eligibility/coverage determinations, authorization routing, claim edits, denials workflows, payment/financial assistance interactions, and other AI-mediated decisions).
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.
Depending on departmental assignment, perform deep-dive validation in specific domains (e.g., verifying that system-generated CPT/diagnosis codes accurately match clinical documentation, or auditing automated clinical packet generation for prior authorizations).
Exception Management & Escalation
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 (policy gap vs. data issue vs. workflow design vs. model behavior), and drive corrective actions with owners
Vendor & Partner Quality Oversight: Conduct quality reviews on external vendor and technology partner performance against established operational standards and Service Level Agreements (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.
Risk Monitoring & Early-Warning Controls
Monitor operational and financial signals to detect drift and emerging compliance risk-explicitly including denial trends, reimbursement impact, and case mix swings (and analogous indicators for non-coding AI such as auth turnaround, inappropriate routing or patient balance errors).
Escalate patterns that suggest systematic error, over/under-treatment of policy logic, or patient financial harm risk.
Workflow Analysis & Process Improvement: 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.
Training, Communication, and Operational Enablement: Provide targeted, at-elbow coaching and operational support to frontline staff adjusting to new automation tools and changing workflows.
Assist in developing and delivering 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.
Governance, Controls, and Audit Readiness
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.
