Company-Sancho Maria Consuelo, González-Chordá Victor M, Isabel Orts-Cortés Maria
Health Promotion Service, Canary Islands Health Service, Directorate General for Public Health, Las Palmas de Gran Canaria, 35003, Spain.
Nursing and Healthcare Research Unit (Investén-isciii), Instituto de Salud Carlos III, Madrid, 28029, Spain.
BMC Nurs. 2025 Jul 1;24(1):738. doi: 10.1186/s12912-025-03304-5.
To find out whether the information that the nursing process provides (functional patterns and the NANDA-NIC-NOC taxonomy), presented through clinical histories, influences predictions of total healthcare costs.
The nursing process, is not included in the systems that calculate expenditure in the Spanish healthcare system. Such an omission can result in suboptimal resource allocation.
Analytical and retrospective observational study of a population of 1,691,075 people over the age of 15. The explanatory variables were age, sex and nursing process data, with total healthcare cost as the outcome variable. A bivariate analysis and a multiple regression were performed for the multivariate analysis. To improve prediction accuracy and account for non-linear relationships, the analysis was completed using two machine learning models.
58% (n = 980,437) of the population presented some data from the nursing process, for individuals with an assessed pattern, the average cost was €2304.17 compared with €950.93 for those who had none; with a nursing diagnosis, the average cost was €1,666 versus €840 without it. Having created the best model for the analysis using neural networks and XGBOOST, an average coefficient of determination of R = 21.45% was obtained.
The variability in total healthcare costs can be explained in more than 21% of cases by the model created, including sex, age, and the information related to the nursing process.
Demonstrating the influence of nursing care on total patient costs will facilitate its inclusion in management programs, promoting the use of nursing data in risk adjustment models and healthcare planning.
探究通过临床病史呈现的护理程序所提供的信息（功能模式以及北美护理诊断协会-护理干预分类-护理结局分类法）是否会影响对总医疗费用的预测。
护理程序未被纳入西班牙医疗系统的费用计算体系。这种遗漏可能导致资源分配不合理。
对1691075名15岁以上人群进行分析性回顾性观察研究。解释变量为年龄、性别和护理程序数据，以总医疗费用作为结局变量。进行双变量分析和多元回归以进行多变量分析。为提高预测准确性并考虑非线性关系，使用两种机器学习模型完成分析。
58%（n = 980437）的人群呈现了护理程序的一些数据，对于有评估模式的个体，平均费用为2304.17欧元，而没有评估模式的个体平均费用为950.93欧元；有护理诊断的个体平均费用为1666欧元，无护理诊断的个体平均费用为840欧元。使用神经网络和XGBOOST创建了最佳分析模型，平均决定系数R = 21.45%。
所创建的模型在超过21%的病例中可以解释总医疗费用的变异性，该模型包括性别、年龄以及与护理程序相关的信息。
证明护理对患者总费用的影响将有助于将其纳入管理计划，促进在风险调整模型和医疗规划中使用护理数据。
