Role: Healthcare AI Implementation & Operations Lead - Generator Health AI

Operationalizing predictive risk scoring to shift prior authorization from reactive denial management to proactive intervention.

The Situation

Prior-authorization teams often responded to denials only after they occurred, creating avoidable administrative rework, treatment delays, and operational inefficiency. Predictive AI created an opportunity to identify authorizations at elevated risk of denial before submission, but model outputs required operational validation and workflow integration to ensure they reflected real payer requirements and supported frontline decision-making.

The engagement focused on translating predictive risk signals into a practical, proactive denial-prevention operating model.

The Approach

I led a two-month operational validation and workflow implementation initiative focused on predictive AI–enabled denial prevention.

I mapped current-state denial-management workflows, identified recurring denial drivers, and defined operational use cases for predictive risk scoring. Using production prior-authorization cases, payer requirements, and frontline workflows, I evaluated model outputs for prediction quality, false positives, false negatives, workflow fit, and operational edge cases.

In partnership with Clinical Intelligence, Engineering, Product, and Strategy & Operations, I translated validation findings into prioritized model and product improvements. I then designed standardized intervention workflows that connected predictive risk scores to proactive actions before authorization submission. To support deployment readiness and continued model improvement, I established feedback loops linking frontline outcomes back to the product and clinical intelligence teams.

The Impact

• Improved operational readiness of predictive AI models before enterprise deployment
• Shifted prior authorization workflows from reactive denial management to proactive denial prevention
• Increased alignment between predictive risk outputs and real-world payer requirements
• Standardized intervention workflows connecting AI recommendations to frontline operational actions
• Reduced the risk of downstream administrative rework by enabling earlier intervention
• Strengthened collaboration across Operations, Engineering, Product, Clinical Intelligence, and Strategy & Operations
• Increased user confidence and adoption readiness by translating predictive AI outputs into clear, actionable workflows