Doctor in Nursing Science, Faculty of Nursing and Rehabilitation, Universidad de La Sabana, Chía, Cundinamarca, Colombia.
Doctor in Engineering, Faculty of Engineering, University of La Sabana, Chía, Cundinamarca, Colombia.
J Nurs Scholarsh. 2021 Nov;53(6):803-814. doi: 10.1111/jnu.12711. Epub 2021 Oct 19.
Prescriptive and predictive analytics and artificial intelligence (AI) provide tools to analyze data with objectivity. In this paper, we provide an overview of how these techniques can improve nursing care, and we detail a quantitative model to afford managerial insights about care management in a Hospital in Colombia. Our main purpose is to provide tools to improve key performance indicators for the care management of inpatients which includes the nurse workload.
The optimal nurse-to-patient assignment problem is addressed using analytics, lean health care, and AI. Also, we propose a new mathematical model to optimize the nurse-to-patient assignment decisions considering several variables about the patient state such as the Barthel index, their risks, the complexity of the care, and the mental state.
Our results show that there are several processes inherent to compassionate nursing care that can be improved using technology. By using data analytics, we can also provide insights about the high variability of the care requirements and, by using models, find nurse-to-patient assignments that are nearly perfectly balanced.
We illustrated this improvement with a pilot test that makes the equitable distribution of nursing workload the functionality of this strategy. The findings can be useful in highly complex hospitals in Latin America.
The proposed model presents an opportunity to make near perfectly balanced nurse-to-patient assignments according to the number of patients and their health conditions using technology.
规定性和预测性分析以及人工智能 (AI) 提供了客观分析数据的工具。在本文中，我们概述了这些技术如何能够改善护理服务，并详细介绍了一个定量模型，为哥伦比亚一家医院的护理管理提供管理见解。我们的主要目的是提供工具，以改善住院患者护理管理的关键绩效指标，其中包括护士工作量。
使用分析、精益医疗保健和人工智能来解决最佳护士与患者分配问题。此外，我们还提出了一个新的数学模型，以优化护士与患者的分配决策，考虑患者状态的多个变量，如巴氏指数、风险、护理的复杂性和精神状态。
我们的结果表明，富有同情心的护理中有几个固有过程可以通过技术加以改进。通过使用数据分析，我们还可以提供有关护理需求高度可变性的见解，并通过使用模型找到几乎完全平衡的护士与患者分配。
我们通过一个试点测试说明了这种改进，该测试使公平分配护理工作量成为该策略的功能。该发现对于拉丁美洲的高度复杂医院可能具有重要意义。
所提出的模型提供了一个机会，通过使用技术根据患者数量和健康状况进行近乎完美的平衡护士与患者的分配。
