Creating a Safe Autonomous Prior Authorization Agent

Overview

In this tutorial, we demonstrate how an autonomous AI system can simulate the end-to-end prior authorization workflow within healthcare Revenue Cycle Management (RCM). The agent continuously monitors incoming surgery orders, gathers required clinical documentation, submits requests to payer systems, tracks their status, and intelligently responds to denials with automated analysis and appeals.

Workflow Highlights

The system acts conservatively and responsibly, escalating to a human reviewer when uncertainty crosses a defined threshold. Our design intentionally mirrors real-world workflows, ensuring the logic is transferable to production environments while maintaining safety and clarity. This implementation uses mocked EHR and payer portals strictly for simulation purposes and is not a substitute for clinical judgment or payer policy interpretation.

Setting the Execution Environment

We set up the execution environment and install minimal dependencies:

!pip -q install "pydantic>=2.0.0" "httpx>=0.27.0"

Optional OpenAI Integration

To safely utilize OpenAI, we configure optional usage in a fail-open manner:

USE_OPENAI = False
OPENAI_AVAILABLE = False
 
the
....

Domain Models

We define strongly typed domain models to mirror real healthcare RCM structures:

class DocType(str, Enum):
    H_AND_P = "history_and_physical"
    ...

This avoids ambiguity and reduces errors in automated decision-making.

Simulating EHR Systems

An EHR system simulates the emission of surgery orders:

class MockEHR:
    def seed_data(self, n_orders: int = 5):
        ...

Payer Portal Behavior

We also model payer-side behavior, including...

class MockPayerPortal:
    def submit(self, pa: PriorAuthRequest) -> PayerResponse:
        ...

Handling Denials

The agent handles denials by analyzing responses from the payer:

def handle_denial(self, pa: PriorAuthRequest, payer_ref: str, denial_resp: PayerResponse) -> AgentDecision:
    ...

Conclusion

We illustrated how agentic AI can mitigate administrative friction in healthcare by automating prior authorization tasks while keeping human oversight for complex decisions. This architecture aligns with healthcare’s safety-critical nature, emphasizing observability and compliance.