LiRong Xu
Central sterile supply Department,Shaanxi Provincial People's Hospital, Xi'an 710068, ShaanXi Province, China
Hui Jun Si
Central sterile supply Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, ShaanXi Province, China
Li Yuan Chen
Central sterile supply Department, TangDu Hospital, Xi'an 710038, ShaanXi Province, China
Qi Wang
Central sterile supply Department, XiJing Hospital, Xi'an 710032, ShaanXi Province, China
Rong Zhou
Central sterile supply Department, Xi'an No.3 Hospita, Xi'an 710018, ShaanXi Province, China
Ai Qun Zhu
Central sterile supply Department, XianYang Central Hospital, XianYang 712000, ShaanXi Province, China
Abstract:
The quality of the Disinfection supply center work is intrinsically linked to the proper functioning of the hospital, since this department is responsible for cleaning, disinfecting, sterilizing, preserving, and distributing all surgical devices that will be reused in the hospital. Medical equipment is retrieved, cleaned, disinfected, sterilized, stored, and distributed by the hospital's disinfection Supply center, which serves as the center's basis and foundation. The process of cleaning and disinfecting is highly technical and demanding, with direct implications for patient safety and the prevalence of hospital-acquired illnesses. Therefore, supply-center procedures must be managed methodically and effectively. For this study, the data has been collected from Santai Hospital Affiliated to North Sichuan Medical College, Mianyang. The hospital's daily operations suffer from the shortcomings of the conventional management style. The hospital's disinfection supply center plays a difficult role in preventing the spread of disease and maintaining high standards of medical treatment. An advanced management mode with an artificial intelligence algorithm may significantly enhance the quality of risk management, which in turn improves efficiency and overall work quality. In this study, artificial intelligence algorithms are investigated to enhance risk management. Experiments demonstrate the algorithm model can decrease adverse events and improve disinfection.
Keywords:Artificial intelligence, risk management, Disinfection supply center, technology based, hospital.