Medical-Insurance AI Agent System: Automating Administrative Processes Between Clinical and Insurance Using Multi-Agent Architecture

This project uses a multi-LLM agent architecture to address pain points in administrative processes between medical and insurance sectors. It automates links such as symptom analysis, diagnostic assistance, and claims decision-making, significantly reducing processing delays, lowering error rates, and enhancing operational transparency—creating value for medical institutions, insurance companies, and patients alike.

Administrative processes between medical and insurance systems have long suffered from issues like cumbersome paperwork, repeated data entry, and cross-system information transfer—leading to low efficiency and frequent claim delays or denials due to human errors. The open-source project "Automated Administrative Workflow" offers an innovative solution: using a multi-agent architecture to connect clinical diagnosis and treatment with insurance claims processes, optimizing the entire workflow.

The system's core is a multi-agent collaboration framework:

1. Symptom Analysis Agent: Extracts patient symptoms, assesses severity, and generates standardized reports;
2. Diagnostic Assistance Agent: Provides differential diagnosis suggestions, recommends examination items, and matches diagnosis codes;
3. Claims Decision Agent: Extracts treatment cost information, determines claim eligibility, and generates claim documents. All agents are coordinated by a central orchestrator, and automatically escalate to manual review when encountering unhandleable situations.

• LLM-driven: Fine-tuned with medical data, featuring medical knowledge understanding, multi-turn dialogue, reasoning and decision-making, and document generation capabilities;
• Security & Privacy: Data desensitization, role-based access control, audit trails, compliant with HIPAA/GDPR regulations;
• System Integration: Supports EHR/EMR integration, insurance API protocols, HL7/FHIR standards, and has a scalable architecture.

• Medical institutions: Reduce administrative burden, improve diagnostic accuracy, and accelerate fund recovery;
• Insurance companies: Lower fraud risks, enhance claims efficiency, and support data-driven decision-making;
• Patients: Simplify medical procedures, speed up claim disbursement, and increase process transparency.

• Communication Protocols: Adopt message queues for asynchronous agent communication, ensuring loose coupling and high availability;
• Knowledge Base: Integrate clinical guidelines, drug databases, ICD codes, etc., with vector storage supporting semantic retrieval;
• Continuous Learning: Fine-tune models through manual review feedback to optimize agent decision-making capabilities.

This system demonstrates the transition of LLMs from chat tools to productivity tools, driving digital transformation in the medical insurance industry. Future expansions can include: integrating medical image analysis, accessing wearable device data, combining blockchain for evidence storage, and further optimizing end-to-end automation.