Aslan Manar, Toros Ergin
Department of Nursing, Trakya University Faculty of Health Sciences, Edirne, Turkey.
J Eval Clin Pract. 2025 Feb;31(1):e70001. doi: 10.1111/jep.70001.
This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequacy of nurse staffing and the appropriateness of the nursing care delivery system.
Historical and current healthcare challenges, such as nurse shortages and increasing patient acuity, necessitate innovative approaches to nursing care delivery. For instance, the COVID-19 pandemic highlighted the need for flexible and scalable staffing models to manage surges in patient volume and acuity.
A descriptive study was conducted in 39 inpatient wards across a university hospital and three state hospitals, involving 117 ward-level observations. Data were collected using the Rush Medicus Patient Classification Scale and analysed using k-Nearest Neighbour, Support Vector Machine, Random Forest, and Logistic Regression algorithms. Effectiveness was measured by the accuracy of machine learning predictions regarding nurse staffing adequacy, while suitability was determined by the congruence between observed nursing care models and patient needs.
STROBE checklist.
The Random Forest algorithm demonstrated the highest accuracy in predicting both nurse staffing adequacy and the appropriateness of nursing care delivery systems. The study found that 68.4% of wards had sufficient nurse staffing and 26.5% of wards used appropriate care delivery models, with functional nursing and total patient care models being the most commonly used.
The study highlights functional nursing and total patient care models, emphasising the need to consider nurse qualifications and patient needs in selecting care systems. Machine learning, particularly the Random Forest algorithm, proved effective in aligning staffing with patient requirements.
Machine learning, particularly the Random Forest algorithm, proves effective in optimising nursing care delivery models, suggesting significant potential for enhancing patient care and nurse satisfaction.
The research underscores machine learning's role in improving nursing care delivery, aligning nurse staffing with patient needs, and advancing healthcare outcomes.
The findings advocate for integrating machine learning in the planning of nursing care delivery models. This study sets a precedent for using data-driven approaches to improve nurse staffing and care delivery, potentially enhancing global clinical outcomes and operational efficiencies. The global clinical community can learn from this study the value of employing machine learning techniques to make informed, evidence-based decisions in healthcare management.
While the study lacked direct patient involvement, its goal was to enhance patient care and healthcare efficiency. Future research will aim to incorporate patient and public insights more directly.
本研究旨在评估机器学习（ML）技术在优化护士人员配置以及评估医院病房护理服务模式适宜性方面的表现。主要结局指标包括护士人员配置的充足性以及护理服务系统的适宜性。
历史和当前的医疗保健挑战，如护士短缺和患者病情严重程度增加，需要创新的护理服务提供方式。例如，新冠疫情凸显了需要灵活且可扩展的人员配置模式来应对患者数量和病情严重程度的激增。
在一所大学医院和三家州立医院的39个住院病房进行了一项描述性研究，涉及117次病房层面的观察。使用拉什医学患者分类量表收集数据，并使用k近邻、支持向量机、随机森林和逻辑回归算法进行分析。有效性通过机器学习对护士人员配置充足性预测的准确性来衡量，而适宜性则由观察到的护理模式与患者需求之间的一致性来确定。
采用STROBE清单。
随机森林算法在预测护士人员配置充足性和护理服务系统适宜性方面表现出最高的准确性。研究发现，68.4%的病房护士人员配置充足，26.5%的病房使用了适宜的护理服务模式，功能制护理和整体护理模式是最常用的。
该研究突出了功能制护理和整体护理模式，强调在选择护理系统时需要考虑护士资质和患者需求。机器学习，尤其是随机森林算法，在使人员配置与患者需求相匹配方面被证明是有效的。
机器学习，尤其是随机森林算法，在优化护理服务模式方面被证明是有效的，这表明在提高患者护理质量和护士满意度方面具有巨大潜力。
该研究强调了机器学习在改善护理服务、使护士人员配置与患者需求相匹配以及推进医疗保健成果方面的作用。
研究结果倡导在护理服务模式规划中整合机器学习。本研究为使用数据驱动方法改善护士人员配置和护理服务树立了先例，有可能提高全球临床疗效和运营效率。全球临床界可以从本研究中学到在医疗管理中运用机器学习技术做出明智的、基于证据的决策的价值。
虽然该研究缺乏患者的直接参与，但其目标是提高患者护理质量和医疗效率。未来的研究将旨在更直接地纳入患者和公众的见解。
