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作 者:李梦文 鱼星锋 魏梦瑶 韩斌如[2] 孙宁[1] 吴红娟[1] LI Mengwen;YU Xingfeng;WEI Mengyao;HAN Binru;SUN Ning;WU Hongjuan(Nursing Department,Shaanxi Provincial People's Hospital,Xi'an 710068,China;Nursing Department,Xuanwu Hospital Capital Medical University,Beijing 100053,China)
机构地区:[1]陕西省人民医院护理部,西安市710068 [2]首都医科大学宣武医院护理部,北京市100053
出 处:《护理管理杂志》2024年第4期351-354,359,共5页Journal of Nursing Administration
基 金:2022年陕西省人民医院科技发展孵化基金(2022HL-01)。
摘 要:目的探索DRGs权重与护理工作量的关系,利用DRGs相关指标构建外科病区护理人力资源配置模型。方法采用实地调研收集患者住院期间的总护理工作量及DRGs相关信息进行多元回归分析形成基于DRGs的外科病区护理人力资源配置模型。结果共收集450例患者(建模组)住院期间的护理工作量,年龄、入院自理能力评分、入院护理级别、麻醉方式以及DRGs权重是护理时间的影响因素,能够解释55.52%的变量。继续收集184例患者(验证组)信息带入护理人力资源配置模型,两组总工时比较差异无统计学意义(P>0.05)。结论形成的基于DRGs的外科病区护理人力资源配置模型较为科学、客观,能够将代表病种资源消耗与服务能力的DRGs权重与护理工作量进行联系,可为今后科学配置护理人力提供依据。Objective To explore the relationship between DRGs weight and nursing workload,and to construct a nursing human resource allocation model for surgical wards based on DRGs indicators.Methods The field research was conducted to collect the total nursing workload and DRGs related information of patients during hospitalization,and multiple regression analysis was performed to build a nursing human resource allocation model for surgical wards based on DRGs.Results A total of 450 patients(modeling group)were collected for their nursing workload during hospitalization.The multiple linear regression analysis showed that age,ADL admission,level of nursing admission,anesthesia method and DRGs weights were the influencing factors,which could explain 55.52%of the nursing time.And 184 patients(validation group)were collected for their information to be input into the nursing human resource allocation model.The model time and actual time of the validation group were obtained.The rank sum test was used to compare the total nursing time of the two groups.The results showed that there was no significant statistical difference(P>0.05).Conclusion The nursing human resource allocation model for surgical wards based on DRGs is scientific and objective.It can link the DRGs weight representing disease resource consumption and service capacity with nursing workload and provide a basis for scientific allocation of nursing human resources in the future.
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