Ding Jinxia, Wang Jinfang, Wu Pengying, Huang Yan, Dong Yunya, Rong Ning, Wang Xiaotian
Department of Oncology, The First Affiliated Hospital of Anhui Medical University Hefei 230022, Anhui, China.
Information Center of The First Affiliated Hospital of Anhui Medical University Hefei 230022, Anhui, China.
Am J Transl Res. 2022 Oct 15;14(10):6953-6963. eCollection 2022.
We aim to improve the decision-making process of nursing evaluation, and the purpose of this paper was to introduce nursing outcome classifications based on standardized nursing language, as well as build a comprehensive nursing evaluation decision-making system model based on an artificial neural network and fuzzy comprehensive evaluations.
Based on the principle and method of the decision support system (DSS), this paper proposed a framework of DSS and developed an intelligent nursing decision support system which integrates expert systems, data, models and knowledge.
Taking cancer patients as examples, based on the analysis and comparison of cancer stressors and their frequency of occurrence, this paper found that the 5 major factors for cancer patients' stress events were lack of privacy, attitude of the medical workers, unfamiliar medical workers and uncomfortable temperature in wards. In addition, through the single factor analysis of the stressors, it was found that "the impact of hospitalization on individuals and their families", "the professional level and service attitude of medical workers", and "partial loss of free social contact in the hospital" were all positively correlated with stress level. The degree of cancer patients' participation in treatment decision-making was lower than the expectation of the patients. There was a statistically significant difference between the actual participation and the anticipated participation of cancer patients in nursing decision-making (P < 0.0001). In addition, the system helped patients adapt to the hospital environment as quickly as possible, so that they could feel comfortable in the hospital environment, as well as a relaxed and pleasant with the humanistic environment.
Cancer patients have a variety of stressors, and the pressure is high. Our computer decision support nursing system assisted nurses to help patients to take positive coping measures to relieve pressure as soon as possible, so as to improve their quality of life.
旨在改进护理评估的决策过程，本文旨在介绍基于标准化护理语言的护理结局分类，并构建基于人工神经网络和模糊综合评价的综合护理评估决策系统模型。
基于决策支持系统（DSS）的原理和方法，本文提出了DSS框架，并开发了一个集成专家系统、数据、模型和知识的智能护理决策支持系统。
以癌症患者为例，通过对癌症应激源及其发生频率的分析比较，发现癌症患者应激事件的5大因素为隐私缺失、医护人员态度、医护人员陌生感及病房温度不适。此外，通过对应激源的单因素分析发现，“住院对个人及其家庭的影响”“医护人员的专业水平和服务态度”以及“在医院部分失去自由的社交联系”均与应激水平呈正相关。癌症患者参与治疗决策的程度低于患者的期望。癌症患者在护理决策中的实际参与度与预期参与度之间存在统计学显著差异（P < 0.0001）。此外，该系统帮助患者尽快适应医院环境，使其在医院环境中感到舒适，以及在人文环境中感到轻松愉快。
癌症患者存在多种应激源，压力较大。我们的计算机决策支持护理系统协助护士帮助患者尽快采取积极的应对措施缓解压力，从而提高其生活质量。
