Othman Mutaz I, Nashwan Abdulqadir J, Abujaber Ahmad A
Nursing Department, Hamad Medical Corporation, Doha, Qatar.
Nurs Open. 2025 Apr;12(4):e70195. doi: 10.1002/nop2.70195.
Machine learning (ML) models can enhance patient-nurse assignments in healthcare organisations by learning from real data and identifying key capabilities. Nurses must develop innovative ideas for adapting to the dynamic environment, managing staffing and establishing flexible workforce solutions.
This discursive paper discusses the application of ML in optimising patient-nurse assignments within healthcare settings, considering various factors such as staff skill mix, patient acuity, cultural competencies and language considerations.
A discursive approach was used to optimise nurse-patient assignments and the impact of ML models. Through a review of traditional and emerging perspectives, factors such as staff skill mix, patient acuity, cultural competencies and language-related challenges were emphasised.
Machine learning models can potentially enhance healthcare patient-nurse assignments by considering skill integration, acuity level assessment and cultural and language barrier awareness. Thus, models have the potential to optimise patient care through dynamic adjustments.
The application of ML models in optimising patient-nurse assignments presents significant opportunities for improving healthcare delivery. Future research should focus on refining algorithms, ensuring real-time adaptability, addressing ethical considerations, evaluating long-term patient outcomes, fostering cooperative systems, and integrating relevant data and policies within the healthcare framework. No patient or public contribution.
机器学习（ML）模型可以通过从真实数据中学习并识别关键能力，来改善医疗保健机构中的患者与护士分配。护士必须想出创新的办法来适应动态环境、管理人员配置并建立灵活的劳动力解决方案。
这篇论述性论文探讨了ML在优化医疗环境中患者与护士分配方面的应用，考虑了诸如员工技能组合、患者 acuity、文化能力和语言因素等各种因素。
采用论述方法来优化护士与患者的分配以及ML模型的影响。通过回顾传统和新兴观点，强调了员工技能组合、患者 acuity、文化能力和与语言相关的挑战等因素。
机器学习模型通过考虑技能整合、 acuity水平评估以及文化和语言障碍意识，有可能改善医疗保健中的患者与护士分配。因此，模型有潜力通过动态调整来优化患者护理。
ML模型在优化患者与护士分配方面的应用为改善医疗服务提供了重大机遇。未来的研究应专注于完善算法、确保实时适应性、解决伦理问题、评估长期患者结果、促进合作系统以及在医疗保健框架内整合相关数据和政策。无患者或公众参与。
