Design of a Medical IT Automated Auditing System Based on Multiple Compliance Standards

Abstract

This study proposes a three-step framework of “regulation quantification - conflict resolution - pipeline automation” and deploys real-world experiments in five medium-sized medical groups in the western United States. The results show that the auditing days are reduced by 80%, human resources are saved by 69.7%, the high-risk rectification rate reaches 100%, and the ROI is as high as 136:1, triggering reinsurance discounts from two regional insurers. The system relies on an open-source rule library and a CNN-based man-day prediction model, incorporating compliance tasks into the DevOps Kanban for the first time to achieve “left shift of compliance.” However, limitations such as the singularity of the sample region and payment model, insufficient support of cloud-native APIs for traditional architectures, and model regulation drift still need to be overcome. The lightweight proxy design has been verified in a non-K8s environment to demonstrate its cross-industry general potential, providing a replicable and verifiable automated compliance paradigm for the medical and other regulated industries.