A Multi-Agent Autonomous AI Framework for End-to-End Healthcare  Insurance Claims Processing

Ravi Chandragiri and Anusha Naganandini

A Multi-Agent Autonomous AI Framework for End-to-End Healthcare Insurance Claims Processing

Authors

Ravi Chandragiri and Anusha Naganandini

Keywords:

Agentic AI, Multi-Agent Systems, Healthcare Insurance, Claims Processing, Medical Coding Automation, Fraud Detection, Reimbursement Optimization, Large Language Models, Autonomous Reasoning, Health Informatics, ICD-10, CPT, Graph Neural Networks, Asynchronous Workflows.

Abstract

Health insurance claims management has been among the most judgementally inaccurate andcostly administrative functions in the medical sector. Inaccurate medical coding, billing discrepancies and reimbursement

References

Gao Y, Sun J, Li F. Automated ICD coding using transformer-based architectures. J Biomed Inform. 2023;137:104323.

Kumar A, Li X. Machine learning approaches for healthcare fraud detection. Health Inf Syst. 2020;45(2):145-60.

Downloads

Published

2024-12-20

How to Cite

Ravi Chandragiri and Anusha Naganandini. (2024). A Multi-Agent Autonomous AI Framework for End-to-End Healthcare Insurance Claims Processing . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 7643–7653. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4811