Renggli Fabienne Josefine, Gerlach Maisa, Bieri Jannic Stefan, Golz Christoph, Sariyar Murat
School of Health Professions, Bern University of Applied Sciences, Bern, Switzerland.
School of Engineering and Computer Science, Bern University of Applied Sciences, Höheweg 80, Biel, 2502, Switzerland, 41 32 321 64 37.
JMIR Form Res. 2025 Jun 4;9:e67747. doi: 10.2196/67747.
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.
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.
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.
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.
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.
护士排班是医疗保健领域一项复杂的挑战，会影响患者护理质量和护士的幸福感。传统的排班方法往往未能考虑个人偏好，导致不满、倦怠和高离职率。排班做法不当，包括自主权受限和缺乏透明度，会进一步降低护士士气，并对患者护理结果产生负面影响。研究表明，纳入护士偏好的参与式排班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），为优化排班和应对这些挑战提供了潜在的解决方案。
本研究旨在通过收集护士和主管的定性见解，并将这些见解映射到基于数学和人工智能的排班技术，来开发一个将护士偏好整合到人工智能支持的排班方法中的框架。
对来自瑞士医疗机构的21名参与者（护士、主管和临时工作人员）进行了焦点小组访谈，以了解与员工排班相关的经验和偏好。使用开放式和轴心式编码对定性数据进行分析，以提取关键主题。然后根据这些主题对解决已识别排班挑战的适用性，将其映射到人工智能方法，包括MIP、CP、GP和RL。
该研究揭示了护士排班中的关键优先事项。85%（18/21）的访谈参与者强调了公平性和参与度，强调需要透明和包容性的排班。76%（16/21）的人更喜欢灵活性和自主权，倾向于轮班交换和自我排班。对人工智能的期望不一：62%（13/21）的人认为人工智能有提高效率和公平性的潜力，而38%（8/21）的人对可靠性和失去人为监督表示担忧。映射到人工智能方法显示，MIP对于公平的轮班分配有效，CP对于基于复杂规则的条件有效，GP对于处理意外缺勤有效，RL对于医院环境中的动态排班调整有效。在一家培训医院科室（35名工作人员）对MIP进行的初步人工智能实施展示了如何从数学角度设计一个系统。
人工智能支持的排班系统可以显著提高护士排班的公平性、透明度和效率。然而，必须解决对人工智能可靠性、对个人需求的适应性以及人为监督的担忧。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选择。未来的研究应该探索人工智能驱动的排班模型的更广泛实施，并评估它们随着时间的推移对护士满意度和患者护理结果的影响。
