How AutoAuth Leverages Agentic AI for Prior Authorization

The AutoAuth solution revolutionizes the Prior Authorization (PA) process by employing Agentic AI - intelligent, autonomous agents that can reason, plan, and execute complex multi-step workflows. This document details how agentic AI principles transform traditional PA processing from manual, error-prone workflows into intelligent, adaptive systems.

What is Agentic AI?

Agentic AI represents a paradigm shift from simple prompt-response patterns to intelligent agents capable of:

  • Autonomous Decision Making: Making complex decisions without human intervention
  • Multi-Step Reasoning: Breaking down complex problems into manageable steps
  • Adaptive Planning: Adjusting strategies based on intermediate results
  • Tool Integration: Dynamically using various tools and services to achieve goals
  • Self-Correction: Learning from failures and adapting approaches

Agentic Flow Architecture 

flowchart TD
    A[Start: PA Request & Clinical Docs] --> B[AI Agent: Clinical Data Extraction]
    B --> C[AI Agent: Query Formulation & Expansion]
    C --> D[AI Agent: Policy Retrieval via Hybrid Search]
    D --> E{AI Evaluator: Policy Relevance Assessment}

    E -- Insufficient --> C2[AI Agent: Query Refinement]
    C2 --> D

    E -- Sufficient --> F[AI Agent: Policy Summarization]
    F --> G[AI Agent: Structured Prompt Engineering]
    G --> H[AI Agent: Multi-Criteria Policy Evaluation]
    H --> I[AI Agent: Decision Generation with Rationale]
    I --> J[Return Structured Decision to Payor Portal]

    subgraph "Agentic RAG Pipeline"
        C
        D
        E
        C2
        F
    end

    subgraph "Agentic Auto-Determination"
        G
        H
        I
    end

    subgraph "AI Skills & Tools"
        K[Document Intelligence]
        L[Azure AI Search]
        M[Vector Embeddings]
        N[LLM Reasoning]
        O[Policy Validation]
    end

    B -.-> K
    C -.-> M
    D -.-> L
    H -.-> N
    E -.-> O

Core Agentic AI Components

1. Intelligent Document Processing Agent

Purpose: Autonomously extract, validate, and structure clinical information from unstructured documents.

Agentic Capabilities:

  • Named Entity Recognition (NER): Automatically identifies and categorizes medical entities (diagnoses, medications, lab results)
  • OCR Error Correction: Self-corrects common OCR mistakes using medical context
  • Data Validation: Ensures extracted information meets clinical standards
  • Multi-Modal Processing: Handles various document types (PDFs, images, clinical notes)

Key Features:

# Intelligent extraction with field-level validation
clinical_info = await agent.extract_clinical_data(
    documents=image_files,
    validation_schema=ClinicalInformation,
    error_correction=True
)

2. Agentic Query Expansion & Formulation

Purpose: Intelligently expand and refine search queries to maximize policy retrieval accuracy.

Agentic Capabilities:

  • Semantic Understanding: Understands medical terminology and relationships
  • Synonym Generation: Automatically includes medical synonyms and alternative terms
  • Context-Aware Expansion: Considers diagnosis, medication, and procedure contexts
  • Precision-Recall Optimization: Balances comprehensive coverage with relevance

Example Process:

# From the prompt templates
"Prior authorization policy for  for .
Related terms: , ,
dosage requirements, contraindications, prior therapy requirements."

3. Hybrid Retrieval-Augmented Generation (RAG) Agent

Purpose: Intelligently search and retrieve the most relevant PA policies using multiple search strategies.

Agentic Capabilities:

  • Hybrid Search Strategy: Combines vector semantic search with BM25 lexical search
  • Query Classification: Determines optimal search strategy (semantic vs. keyword)
  • Relevance Evaluation: Assesses retrieved policies for relevance and completeness
  • Adaptive Retrieval: Refines search strategy based on initial results

Search Flow:

class AgenticRAG:
    async def retrieve_policies(self, clinical_info):
        # 1. Query expansion with medical context
        expanded_query = await self.expand_query(clinical_info)

        # 2. Hybrid search execution
        policies = await self.hybrid_search(expanded_query)

        # 3. Relevance evaluation
        if not self.evaluate_relevance(policies):
            # 4. Adaptive refinement
            return await self.refine_and_retry(clinical_info)

        return policies

4. Policy Evaluation & Reasoning Agent

Purpose: Apply complex medical reasoning to evaluate PA requests against policy criteria.

Agentic Capabilities:

  • Multi-Criteria Analysis: Evaluates multiple policy requirements simultaneously
  • Evidence-Based Reasoning: Links clinical evidence to specific policy criteria
  • Uncertainty Handling: Identifies gaps and requests additional information when needed
  • Compliance Assessment: Ensures decisions align with regulatory requirements

Decision Framework:

# Multi-step reasoning process
decision_process = [
    "Extract all policy criteria",
    "Map clinical information to criteria",
    "Assess compliance for each criterion",
    "Identify gaps or missing information",
    "Generate evidence-based decision",
    "Provide detailed rationale"
]

5. Autonomous Decision Generation Agent

Purpose: Generate comprehensive, auditable decisions with detailed reasoning.

Agentic Capabilities:

  • Structured Decision Making: Follows standardized evaluation frameworks
  • Rationale Generation: Provides clear explanations for each decision
  • Policy Citation: References specific policy sections supporting decisions
  • Transparency: Ensures decisions are auditable and explainable

Agentic AI Skills & Plugin Architecture

Skills Manager

The AutoAuth system uses a modular skills architecture where AI agents can dynamically load and use specialized capabilities:

# Available agentic skills
skills = [
    "retrieval",      # Policy search and retrieval
    "evaluation",     # Clinical assessment and validation
    "rewriting",      # Query refinement and optimization
    "main"           # Core workflow orchestration
]

Dynamic Function Calling

Agents can dynamically invoke tools and services based on context:

  • Document Intelligence: For OCR and document processing
  • Azure AI Search: For policy retrieval and semantic search
  • Vector Embeddings: For similarity matching and semantic understanding
  • LLM Reasoning: For complex medical reasoning and decision making
  • Validation Services: For data quality and compliance checking

Agentic Workflows in Action

Workflow 1: Intelligent Policy Matching

  1. Clinical Context Analysis: Agent analyzes diagnosis, medications, and patient history
  2. Query Formulation: Agent constructs optimized search queries with medical synonyms
  3. Multi-Strategy Search: Agent employs hybrid search techniques
  4. Relevance Assessment: Agent evaluates policy matches for completeness
  5. Adaptive Refinement: Agent refines approach if initial results are insufficient

Workflow 2: Automated Decision Making

  1. Policy Decomposition: Agent breaks down complex policies into discrete criteria
  2. Evidence Mapping: Agent maps clinical information to specific policy requirements
  3. Gap Analysis: Agent identifies missing information or unclear criteria
  4. Decision Synthesis: Agent synthesizes findings into clear approve/deny/more-info decisions
  5. Rationale Generation: Agent provides detailed, auditable explanations

Benefits of Agentic AI in Prior Authorization

🚀 Enhanced Accuracy

  • Contextual Understanding: Agents understand medical terminology and relationships
  • Multi-Perspective Analysis: Considers clinical, regulatory, and administrative factors
  • Error Reduction: Self-validates and corrects common mistakes

⚡ Improved Efficiency

  • Parallel Processing: Multiple agents work simultaneously on different aspects
  • Adaptive Learning: Agents improve performance based on outcomes
  • Automated Workflows: Reduces manual intervention requirements

🔍 Better Transparency

  • Explainable Decisions: Clear reasoning chains for all decisions
  • Audit Trails: Complete record of agent actions and reasoning
  • Policy Citations: Specific references to supporting policy sections

🎯 Regulatory Compliance

  • CMS 2026 Alignment: Built-in compliance with regulatory requirements
  • FHIR Integration: Supports healthcare interoperability standards
  • Documentation Standards: Maintains required documentation for audits

Technical Implementation

Agent Configuration 

agent = Agent(
    name="PA_Processing_Agent",
    instructions="Analyze prior authorization requests using clinical evidence and policy criteria",
    skills=["retrieval", "evaluation", "main"],
    execution_settings=OpenAIChatPromptExecutionSettings(
        temperature=0.1,  # Low temperature for consistent decisions
        max_tokens=4000,
        function_choice_behavior=FunctionChoiceBehavior.AUTO
    )
)

Agentic Pipeline Execution 

class PAProcessingPipeline:
    async def process_agentic_workflow(self, clinical_data):
        # 1. Agentic clinical extraction
        structured_data = await self.clinical_agent.extract(clinical_data)

        # 2. Agentic policy retrieval
        relevant_policies = await self.rag_agent.retrieve_policies(structured_data)

        # 3. Agentic decision making
        decision = await self.decision_agent.evaluate(
            clinical_data=structured_data,
            policies=relevant_policies
        )

        return decision

Future Enhancements

Advanced Agentic Capabilities

  • Multi-Agent Collaboration: Specialized agents for different medical domains
  • Continuous Learning: Agents that improve from feedback and outcomes
  • Predictive Analytics: Agents that anticipate approval likelihood
  • Real-Time Adaptation: Dynamic strategy adjustment based on success rates

Integration Opportunities

  • EHR Integration: Direct integration with Electronic Health Records
  • Provider Portals: Real-time PA status updates and recommendations
  • Quality Metrics: Automated tracking of decision accuracy and efficiency
  • Regulatory Reporting: Automated compliance reporting and analytics

The AutoAuth solution demonstrates how Agentic AI can transform complex healthcare workflows by providing intelligent, autonomous, and transparent processing capabilities that enhance both accuracy and efficiency while maintaining regulatory compliance.