文献类型：期刊文献

英文题名：IMQC: A Large Language Model Platform for Medical Quality Control

作者：Ye, Qi[1]; Yu, Guangya[1]; Liu, Jingping[1]; Chen, Erzhen[2]; Dong, Chenjie[2]; Lin, Xiaosheng[3]; Liu, Zelei[4]; Yu, Han[5]; Ruan, Tong[1]

机构：[1] School of Information Science and Technology, East China University of Science and Technology, Shanghai, China; [2] Shanghai Medical Quality Control Management Center, Shanghai, China; [3] Xinhong Community Health Service Center, Minhang District, Shanghai, China; [4] Unicom [Shanghai] Industrial Internet Co.,Ltd., Shanghai, China; [5] College of Computing and Data Science, Nanyang Technological University, Singapore

年份：2025

卷号：39

期号：28

起止页码：28810

外文期刊名：Proceedings of the AAAI Conference on Artificial Intelligence

收录：EI(收录号：20251818351537)

语种：英文

外文关键词：Biomedical engineering - Damage detection - Electronic health record - Model predictive control - Patient treatment

摘要：Medical quality control (MQC) indicators are essential for evaluating the performance of healthcare institutions to ensure high-quality patient care. In this paper, we report the design, implementation, and deployment of the Intelligent EMR-LLM platform for Medical Quality Control (IMQC), a large language model (LLM)-empowered system for automatically computing MQC indicators for enhancing the quality of medical services in Shanghai. It consists of an LLM (i.e., EMR-LLM) developed using electronic medical records (EMRs). With EMR-LLM, IMQC translates existing MQC indicators into a standardized representation language and automatically computes them based on EMRs. Since its deployment in February 2024, IMQC has been adopted by the Shanghai Medical Quality Management Center and associated hospitals. So far, it has processed 1,245 medical quality indicators for secondary- and tertiary-level hospitals, achieving an MQC evaluation accuracy of 93.31%, which is comparable to human experts. It has significantly improved efficiency, increasing from 10 EMRs per hour per human expert to over 1,000 EMRs per hour on average using one single H800 GPU. Over the first round of deployment in Shanghai, it is estimated that IMQC saves around 3.42 million RMB per month in manpower costs compared to traditional reporting methods. The successful deployment of IMQC sets a precedence for other regions to adopt similar AI-driven solutions to enhance medical quality control. Copyright ? 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.