Kim Seong-Kwang, Kim Eun-Joo, Kim Hye-Kyeong, Song Sung-Sook, Park Bit-Na, Jo Kyoung-Won
Department of Nursing, Gangneung-Wonju National University, Wonju City 20403, Republic of Korea.
Department of Nursing, Inha University, Incheon 22212, Republic of Korea.
Healthcare (Basel). 2023 May 28;11(11):1583. doi: 10.3390/healthcare11111583.
Nurse turnover is a critical issue in Korea, as it affects the quality of patient care and increases the financial burden on healthcare systems. To address this problem, this study aimed to develop and evaluate a machine learning-based prediction model for nurse turnover in Korea and analyze factors influencing nurse turnover. The study was conducted in two phases: building the prediction model and evaluating its performance. Three models, namely, decision tree, logistic regression, and random forest were evaluated and compared to build the nurse turnover prediction model. The importance of turnover decision factors was also analyzed. The random forest model showed the highest accuracy of 0.97. The accuracy of turnover prediction within one year was improved to 98.9% with the optimized random forest. Salary was the most important decision factor for nurse turnover. The nurse turnover prediction model developed in this study can efficiently predict nurse turnover in Korea with minimal personnel and cost through machine learning. The model can effectively manage nurse turnover in a cost-effective manner if utilized in hospitals or nursing units.
护士离职率在韩国是一个关键问题，因为它影响患者护理质量，并增加了医疗系统的经济负担。为了解决这个问题，本研究旨在开发和评估一个基于机器学习的韩国护士离职率预测模型，并分析影响护士离职的因素。该研究分两个阶段进行：构建预测模型和评估其性能。对决策树、逻辑回归和随机森林这三种模型进行了评估和比较，以构建护士离职率预测模型。还分析了离职决策因素的重要性。随机森林模型显示出最高的准确率，为0.97。通过优化的随机森林，一年内离职预测的准确率提高到了98.9%。薪资是护士离职最重要的决策因素。本研究开发的护士离职率预测模型可以通过机器学习，以最少的人力和成本有效地预测韩国的护士离职率。如果在医院或护理单元中使用，该模型可以以具有成本效益的方式有效管理护士离职率。
