呼吸内科核心疾病诊断相关分组对护理工作量预测的价值研究  

Research on the value of DRG on nursing workload prediction in respiratory medicine

作　　者：王蒙蒙 宋玉磊[1] 柏亚妹[1] 朱玉洁 汤黄梅[3] 张薛晴 高姣姣 徐桂华[1] WANG Mengmeng;SONG Yulei;BAI Yamei;ZHU Yujie;TANG Huangmei;ZHANG Xueqing;GAO Jiaojiao;XU Guihua(School of Nursing,Nanjing University of Chinese Medicine,Nanjing 210023,China;Department of Lung Disease,Changzhou Hospital of Traditional Chinese Medicine,Changzhou 213004,China;Department of Liver Disease,the Second Hospital of Nanjing,Nanjing 210037,China)

机构地区：[1]南京中医药大学护理学院,南京市210023 [2]常州市中医院肺病科,213004 [3]南京市第二医院肝病科,210037

出　　处：《护理管理杂志》2023年第10期781-785,共5页Journal of Nursing Administration

基　　金：国家自然科学基金青年项目(72004102);教育部人文社科青年基金项目(19YJCZH139);江苏省研究生科研与实践创新计划(SJCX22_0700)。

摘　　要：目的探究呼吸内科核心疾病诊断相关分组对护理工作量的影响,构建基于疾病诊断分组的护理工作量预测模型。方法选取2021年1月至12月某医院呼吸内科1121例住院患者作为研究对象,采用多元线性回归分析护理工时的影响因素,筛选预测指标,采用随机森林构建基于疾病诊断分组的护理工作量预测模型。结果纳入7个疾病诊断分组,其中呼吸系统感染/炎症、肺水肿及呼吸衰竭、慢性气道阻塞性肺疾病3个分组最为常见,肺水肿及呼吸衰竭分组护理工时最多,疾病诊断权重最高。影响护理工时的因素主要有年龄、入院途径、住院次数、呼吸机使用、抗菌药使用、疾病诊断相关分组权重、并发症与合并症程度7个因素。随机森林预测模型结果显示年龄、并发症与合并症程度、疾病诊断权重对护理工作量的预测价值较大。结论呼吸内科核心疾病诊断分组可成为护理工作量预测的重要指标,基于疾病诊断分组建立的呼吸内科护理工时预测模型科学合理,可为临床护理人力资源管理提供参考。Objective To investigate the influence of diagnosis related groups(DRG)of core diseases in respiratory medicine on nursing workload and construct a prediction model of nursing workload that based on DRG.Methods A total of 1121 inpatients in the respiratory department of a hospital from January to December 2021 were selected as the research objects.Multiple linear regression was used to analyze the influencing factors of nursing hours,screening predictive indicators,and construct a nursing workload prediction model based on disease diagnosis groups by random forest.Results Seven disease diagnosis groups were included,among them that respiratory infection/inflammation,pulmonary edema and respiratory failure,chronic airway obstructive pulmonary disease were the most common three groups,pulmonary edema and respiratory failure group had the most nursing hours and the highest diagnosis weight.There were 7 factors affecting the nursing hours,including age,the way of admission,the numbers of admission,the use of ventilator and antibiotics,the weight of the groups related to disease diagnosis and the degree of complications and comorbidities.The results of random forest prediction model showed that age,the degree of complications and comorbidities and the weight of disease diagnosis had greater predictive value on nursing workload.Conclusion The diagnostic grouping of core diseases in the respiratory department can be an important indicator for the prediction of nursing workload.The prediction model of respiratory nursing hours based on the diagnostic grouping of diseases is scientific and reasonable,it can provide a reference for human resource management of clinical nursing.

关 键 词：疾病诊断相关分组 护理工作量 随机森林 预测 

分 类 号：R473.5[医药卫生—护理学]