Liu Bin, Gu Wei
Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030.
China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, 200030.
Zhongguo Yi Liao Qi Xie Za Zhi. 2023 May 30;47(3):346-350. doi: 10.3969/j.issn.1671-7104.2023.03.024.
In order to improve the compatibility of the hospital resource planning (HRP) system for the whole life cycle of medical consumables, and to improve the management and control capabilities of hospital institutions on medical consumables.
Based on the traditional HRP system, a secondary development and design of a medical consumables whole life-cycle artificial intelligence module was conducted, and a neural network machine learning algorithm module was introduced to enhance its big data integration and analysis capabilities.
The simulation analysis found that after adding this module, the proportion of minimum inventory, the proportion of procurement cost difference and the expiration rate of consumables all decreased significantly, and the differences were statistically significant (<0.05).
The whole life cycle module of medical consumables based on HRP system can effectively improve the management efficiency of hospital medical consumables, adjust the warehouse inventory management ability, and improve the overall management level of medical consumables.
为提高医院资源规划（HRP）系统对医用耗材全生命周期的适配性，提升医院机构对医用耗材的管控能力。
在传统HRP系统基础上，对医用耗材全生命周期人工智能模块进行二次开发与设计，并引入神经网络机器学习算法模块，增强其大数据整合与分析能力。
模拟分析发现，添加该模块后，耗材的最低库存比例、采购成本差异比例及过期率均显著下降，差异具有统计学意义（<0.05）。
基于HRP系统的医用耗材全生命周期模块能有效提高医院医用耗材管理效率，调整仓库库存管理能力，提升医用耗材整体管理水平。
