Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study.

IF 2 Q3 HEALTH CARE SCIENCES & SERVICES
Fabienne Josefine Renggli, Maisa Gerlach, Jannic Stefan Bieri, Christoph Golz, Murat Sariyar
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引用次数: 0

Abstract

Background: Nurse scheduling is a complex challenge in health care, impacting both patient care quality and nurse well-being. Traditional scheduling methods often fail to consider individual preferences, leading to dissatisfaction, burnout, and high turnover. Inadequate scheduling practices, including restricted autonomy and lack of transparency, can further reduce nurse morale and negatively affect patient outcomes. Research suggests that participative scheduling approaches incorporating nurse preferences can improve job satisfaction. Artificial intelligence (AI) and mathematical optimization methods, such as mixed-integer programming (MIP), constraint programming (CP), genetic programming (GP), and reinforcement learning (RL), offer potential solutions to optimize scheduling and address these challenges.

Objective: This study aims to develop a framework for integrating nurses' preferences into AI-supported scheduling methods by gathering qualitative insights from nurses and supervisors and mapping these to mathematical and AI-based scheduling techniques.

Methods: Focus group interviews were conducted with 21 participants (nurses, supervisors, and temporary staff) from Swiss health care institutions to understand experiences and preferences related to staff scheduling. Qualitative data were analyzed using open and axial coding to extract key themes. These themes were then mapped to AI methodologies, including MIP, CP, GP, and RL, based on their suitability to address identified scheduling challenges.

Results: The study revealed key priorities in nurse scheduling. Fairness and participation were highlighted by 85% (18/21) of interview participants, emphasizing the need for transparent and inclusive scheduling. Flexibility and autonomy were preferred by 76% (16/21), favoring shift swaps and self-scheduling. AI expectations were mixed: 62% (13/21) saw potential for improved efficiency and fairness, while 38% (8/21) expressed concerns over reliability and loss of human oversight. Mapping to AI methods showed MIP as effective for fair shift allocation, CP for complex rule-based conditions, GP for handling unforeseen absences, and RL for dynamic schedule adaptation in hospital environments. A preliminary AI implementation of MIP in a training hospital unit (35 staff members) showed how to design a system from a mathematical perspective.

Conclusions: AI-supported scheduling systems can significantly enhance fairness, transparency, and efficiency in nurse scheduling. However, concerns regarding AI reliability, adaptability to individual needs, and human oversight must be addressed. A hybrid approach integrating AI recommendations with human decision-making may be optimal. Future research should explore the broader implementation of AI-driven scheduling models and assess their impact on nurse satisfaction and patient outcomes over time.

将护士偏好整合到基于人工智能的调度系统:定性研究。
背景:护士调度是医疗保健中的一项复杂挑战,影响患者护理质量和护士福祉。传统的日程安排方法往往没有考虑到个人的喜好,导致不满意、倦怠和高流动率。不充分的调度实践,包括有限的自主权和缺乏透明度,会进一步降低护士的士气,并对患者的治疗结果产生负面影响。研究表明,结合护士偏好的参与式调度方法可以提高工作满意度。人工智能(AI)和数学优化方法,如混合整数规划(MIP)、约束规划(CP)、遗传规划(GP)和强化学习(RL),为优化调度和应对这些挑战提供了潜在的解决方案。目的:本研究旨在通过收集护士和主管的定性见解,并将其映射到数学和基于人工智能的调度技术,开发一个框架,将护士的偏好整合到人工智能支持的调度方法中。方法:对来自瑞士医疗机构的21名参与者(护士、主管和临时工)进行焦点小组访谈,了解与员工调度相关的经验和偏好。定性数据分析使用开放和轴向编码提取关键主题。然后将这些主题映射到人工智能方法,包括MIP、CP、GP和RL,基于它们是否适合解决已确定的调度挑战。结果:本研究揭示了护士调度的重点。85%(18/21)的受访者强调公平和参与,强调需要透明和包容的日程安排。76%(16/21)的人更喜欢灵活性和自主性,喜欢轮班交换和自我调度。对人工智能的期望好坏参半:62%(13/21)的人看到了提高效率和公平的潜力,38%(8/21)的人对可靠性和失去人为监督表示担忧。与人工智能方法的映射显示,MIP在公平轮班分配方面是有效的,CP在复杂的基于规则的条件下是有效的,GP在处理不可预见的缺勤方面是有效的,RL在医院环境中是有效的。在一个培训医院单元(35名工作人员)中,MIP的初步人工智能实施展示了如何从数学角度设计系统。结论:人工智能支持的调度系统可显著提高护士调度的公平性、透明度和效率。然而,关于人工智能的可靠性、对个人需求的适应性和人类监督的担忧必须得到解决。将人工智能建议与人类决策相结合的混合方法可能是最佳的。未来的研究应该探索人工智能驱动的调度模型的更广泛实施,并评估它们对护士满意度和患者治疗结果的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR Formative Research
JMIR Formative Research Medicine-Medicine (miscellaneous)
CiteScore
2.70
自引率
9.10%
发文量
579
审稿时长
12 weeks
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