外科手术患者分类模型的构建及在护理人力资源配置中的应用研究

doi:10.3761/j.issn.0254-1769.2025.15.014

摘要/Abstract

摘要：

目的构建外科手术患者分类模型，并在护理人力资源配置中应用，为护理人力资源管理提供参考依据。方法采用便利抽样法，回顾性选取2022年7—11月苏州市某三级甲等综合医院6个外科护理单元收治的5 431例住院手术患者作为调查对象，测量其护理工时，分析相关影响因素，并采用决策树算法建立外科手术患者分类模型。便利选取2022年8—10月同一所医院3个外科护理单元收治的1 527例住院手术患者，基于外科手术患者分类模型、实际工时测量法、床护比法、8 h连续性排班模式配置法计算每日需配置的最少护士人数，以实际工时测量法为标准，评价外科手术患者分类模型在护理人力资源配置中的应用效果。结果外科手术患者分类模型中包括住院天数、病组权重、有无二级护理医嘱、手术级别、麻醉方式、年龄、有无病重医嘱7个分类指标，将患者共分为14个组，对护理工作量总体变异的可解释比例为90.5%。根据该模型计算得出的外科护理单元需配置的最少护士人数与实际工时测量法最接近，优于床护比法、8 h连续性排班模式配置法的计算结果。结论外科手术患者分类模型能较准确地反映此类患者的护理工作量，且分类指标简便易得，能够指导外科护理单元的人力资源配置。

关键词: 护理工作量, 患者分类, 决策树, 人力资源管理, 护理管理研究

Abstract:

Objective To construct a classification model for surgical patients and apply it in the allocation of nursing human resources，providing a reference for nursing human resource management. Methods A convenience sampling method was used to retrospectively select 5，431 hospitalized surgical patients admitted to 6 surgical nursing units of a tertiary general hospital in Suzhou from July to November 2022 as the subjects of this study. The nursing hours were measured，and related influencing factors were analyzed. A decision tree classification method was used to establish a classification model for surgical patients. From August to October 2022，1，527 hospitalized surgical patients admitted to 3 nursing units of the same hospital were conveniently selected. The minimum number of nurses required daily was calculated using the surgical patient classification model，actual nursing hours measurement method，nurse-to-bed ratio method，and 8-hour continuous shift scheduling method. The application effect of the surgical patient classification model in nursing human resource allocation was evaluated with the actual nursing hours measurement method as the standard. Results The surgical patient classification model includes 7 classification indicators：length of hospital stay，diagnosis-related group weight，presence or absence of secondary care orders，surgical grade，anesthesia method，age，and presence or absence of critical illness orders. Patients were divided into 14 groups，and the model explained 90.5% of the total variance in nursing workload. The minimum number of nurses required in surgical nursing units calculated based on this model was closest to the result of the actual nursing hours measurement method and was superior to the results of the nurse-to-bed ratio method and the 8-hour continuous shift scheduling method. Conclusion The surgical patient classification model can accurately reflect the nursing workload of such patients. The classification indicators are simple and easy to obtain，and can guide the allocation of human resources in surgical nursing units.

Key words: Nursing Workload, Patient Classification, Decision Tree, Human Resource Management, Nursing Management Research

李慧霞, 张丽娜, 蒋银芬, 谭丽萍, 张雪梅, 黄隽英, 黄慧, 陶小娟. 外科手术患者分类模型的构建及在护理人力资源配置中的应用研究[J]. 中华护理杂志, 2025, 60(15): 1884-1891.

LI Huixia, ZHANG Lina, JIANG Yinfen, TAN Liping, ZHANG Xuemei, HUANG Juanying, HUANG Hui, TAO Xiaojuan. Construction of a classification model for surgical patients and its application in nursing human resource allocation[J]. Chinese Journal of Nursing, 2025, 60(15): 1884-1891.

图/表 5

表1 外科手术患者的一般资料及护理工作量影响因素的单因素分析结果

Table 1 General characteristics of surgical patients and univariate analysis of nursing workload

表2 自变量赋值方式

Table 2 Assignment of independent variables

表3 外科手术患者护理工作量的分位数回归结果（t 值，n=5 431）

Table 3 Quantile regression results of nursing workload for surgical patients （t vaule ，n=5 431）

表4 外科手术患者分类模型（n=5 431）

Table 4 Decision tree model for the classification of surgical patients（n=5 431）

表5 基于不同计算方式得出的各外科护理单元每日需配置的最少护士人数（名）

Table 5 Theoretical number of nurses assigned to surgical patients in nursing units（case）

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