Revenue Cycle AI Operations Analyst

Hospital for Special Surgery•New York, NY

1d•Onsite

About The Position

How you move is why we’re here. ® Now more than ever. Get back to what you need and love to do. The possibilities are endless... Now more than ever, our guiding principles are helping us in our search for exceptional talent - candidates who align with our unique workplace culture and who want to maximize the abundant opportunities for growth and success. If this describes you then let’s talk! HSS is consistently among the top-ranked hospitals for orthopedics and rheumatology by U.S. News & World Report. As a recipient of the Magnet Award for Nursing Excellence, HSS was the first hospital in New York City to receive the distinguished designation. Whether you are early in your career or an expert in your field, you will find HSS an innovative, supportive and inclusive environment. Working with colleagues who love what they do and are deeply committed to our Mission, you too can be part of our transformation across the enterprise.

Requirements

AI System Oversight & Compliance Auditing
Exception Management & Escalation
Vendor & Partner Quality Oversight
Risk Monitoring & Early-Warning Controls
Workflow Analysis & Process Improvement
Training, Communication, and Operational Enablement
Governance, Controls, and Audit Readiness

Responsibilities

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).
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.
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.
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.
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.
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.
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.