AI-Powered Prior Authorization
Bringing AI to Healthcare's Most Frustrating Problem
Applied AI
Custom Applications
Healthcare
Three Problems, One Solution
The prior authorization process was broken for everyone:
Doctors drowning in 15-20 minute paperwork per prior authorization (PA)
Pharmaceutical manufacturers unable to scale field reimbursement support.
Patients waiting weeks for medication approvals with 40% denial rates. Clinical data existed in electronic medical records (EMRs) but extracting it, interpreting payer requirements, and crafting compliant narratives required expertise and time that practices didn’t have.
AI Agents with Drug-specific Knowledge
pod architected and built a multi-agent artificial intelligence system with two specialized agents: a Healthcare Provider Agent serving all medications (reading clinical data, answering prior authorization questions with confidence scores, generating narratives) and a Field Reimbursement Manager Agent providing manufacturer-specific optimization (brand-specific guidance, post-draft review, denial prediction, appeal strategies). The system operates in a two-phase workflow: pre-guidance injects brand-specific criteria, then post-review validates answers against payer requirements.
The Secret Sauce: Manufacturer Knowledge Bases
Generic prior authorization automation saves time. But brand-specific optimization improves approval rates. The Field Reimbursement Manager Agent uses manufacturer knowledge bases containing prior authorization guides, common denial patterns, and payer-specific criteria for each drug. When a doctor prescribes a GLP-1 medication, the agent knows that BlueCross requires ICD-10 code E66.01 (morbid obesity) rather than generic codes, that Aetna needs step therapy documentation within 90 days, and that UnitedHealth denies 60% of submissions missing comorbidity evidence. This intelligence—embedded at the point of prior authorization creation—transforms a generic form-filler into a payer-aware submission engine.
Why It Worked
Artificial intelligence complemented humans instead of replacing them. The confidence scoring system lets AI handle routine questions while flagging edge cases for clinical review—achieving both high accuracy and high automation. Manufacturers can now embed their prior authorization expertise at scale, something human field reimbursement managers could never achieve across thousands of practices.
The Results
Prior Authorization processing time reduced by answering PA questions directly from the patient EMR. Denial risks surfaced and mitigations suggested. Payer and manufacturer knowledgebase content surfaced in-line with questions to support agentic answers or inform user review. Human-in-the-loop working seamlessly with advanced agents.
Practice staff regain precious time to focus on patient care. Patients get their prescriptions faster and more often. Manufacturers gained scalable FRM capabilities and higher prescription fill rates.
Technical Approach
- Multi-agent artificial intelligence architecture with Healthcare Provider and Field Reimbursement Manager agents
- Azure AI Search integration for manufacturer knowledge bases
- Confidence scoring system (0-1 scale) for human-in-the-loop validation
- Event-driven integration with existing microservices
- Automated appeal generation with deadline tracking
- Denial code mapping to 27 standardized CMS reason codes
