In the context of continuous optimization and improvement of healthcare system, medical institutions play an increasingly prominent role in medical insurance fund supervision. To effectively curb inappropriate fund expenditures, medical institutions actively utilize management advantages and advanced technologies to conduct pre-auditing of medical insurance expenses. However, traditional manual auditing methods is difficult to meet the regulatory requirements of medical insurance management departments, while emerging embedded intelligent auditing systems face limitations such as system delays and impact on core business operations in high-concurrency scenarios at large tertiary hospitals. Addressing these problems, this study explores new models for intelligent medical insurance auditing, independently developing a distributed auxiliary tool for intelligent medical insurance auditing. This tool employs a non-embedded design that fully considers the actual capacity of large tertiary medical institutions, significantly enhancing the automation and intelligence of the auditing process while avoiding impacts on hospital information systems' availability and sustainability, thereby achieving optimization of auditing efficiency and accuracy. This research elaborates on the independent distributed medical insurance intelligent audit auxiliary model, systematically analyzes the technical solutions and implementation paths of the auxiliary tool, and comprehensively evaluates its actual operation combined with key data. It summarizes a set of replicable practical experience to better serve medical insurance fund supervision work.