Yuan Xiaoyan, Wang Peng, Liu Jiwen
Closed Loop Management of Emergency Infection, Gongli Hospital, Shanghai Pudong New Area, Shanghai, 200135, China.
Sci Rep. 2025 Jul 2;15(1):22639. doi: 10.1038/s41598-025-06329-7.
Hospital-acquired infections (HAIs) pose a severe and pervasive threat to patient safety and impose immense pressure on medical resources. These infections often occur during the treatment of other problems due to prolonged hospital stays and can lead to severe complications and increased mortality rates. The spread of HAIs not only compromises patient safety but also stresses medical resources, increasing treatment costs and placing additional demands on healthcare staff. Traditional infection control measures often struggle to deal with the complex and dynamic nature of the infection risks in regional hospitals where decision-making is necessary. The existing frameworks are unable to handle the ambiguity, stakeholder management conflicts and limitations involved in the infection control. So, this study introduces a novel intelligent approach by defining the combined compromised solution (COCOSO) method within the complex picture fuzzy (CPF) framework technology that combines expert evaluation methods with fuzzy reasoning to evaluate and rank different infection control prevention in regional general hospitals by integrating multidisciplinary collaboration as it is the cornerstone of intervention strategies. By incorporating expertise across healthcare professionals such as physicians, nurses, microbiologists, infection control specialists, and data analysts, this approach harnesses collective insight for more effective infection control. Moreover, this study employs robust multi-criteria group decision-making (MCGDM) approach by using a hypothetical case study which shows that antibiotic stewardship program ranks first and offers a scalable and adaptable model to systematically prioritize and optimize infection prevention measures across various healthcare departments and offers a safer and more resilient environment.
医院获得性感染（HAIs）对患者安全构成严重且普遍的威胁，并给医疗资源带来巨大压力。这些感染通常因住院时间延长在治疗其他疾病过程中发生，可能导致严重并发症和死亡率上升。医院获得性感染的传播不仅危及患者安全，还使医疗资源紧张，增加治疗成本，并给医护人员带来额外负担。在需要进行决策的地区医院，传统的感染控制措施往往难以应对感染风险的复杂多变性。现有的框架无法处理感染控制中涉及的模糊性、利益相关者管理冲突和局限性。因此，本研究引入了一种新颖的智能方法，即在复杂图像模糊（CPF）框架技术内定义组合折衷解决方案（COCOSO）方法，该方法将专家评估方法与模糊推理相结合，通过整合多学科协作（这是干预策略的基石）来评估和排序地区综合医院不同的感染控制预防措施。通过纳入医生、护士、微生物学家、感染控制专家和数据分析师等医疗专业人员的专业知识，这种方法利用集体智慧实现更有效的感染控制。此外，本研究通过一个假设案例研究采用了强大的多准则群体决策（MCGDM）方法，该研究表明抗生素管理计划排名第一，并提供了一个可扩展且适应性强的模型，以系统地对各医疗部门的感染预防措施进行优先级排序和优化，从而提供一个更安全、更具弹性的环境。
