Gerlach Maisa, Renggli Fabienne Josefine, Bieri Jannic Stefan, Sariyar Murat, Golz Christoph
School of Health Professions, Bern University of Applied Sciences, Murtenstrasse 10, Bern, 3008, Switzerland.
School of Engineering and Computer Science, Bern University of Applied Sciences, Biel, Switzerland.
BMC Nurs. 2025 Sep 2;24(1):1161. doi: 10.1186/s12912-025-03808-0.
Work-life balance (WLB) is critical to nurse retention and job satisfaction in healthcare. Traditional shift scheduling, characterised by inflexible hours and limited employee control, often leads to stress and perceptions of unfairness, contributing to high turnover rates. AI-based scheduling systems are promoted as a promising solution by enabling fairer and more transparent shift distribution. This study explored the perspectives of nurse leaders, permanent nurses, and temporary nurses on the perceived fairness, transparency, and impact on WLB of AI-based shift scheduling systems, which they had not yet used.
A qualitative study design was used, with focus group (FG) interviews conducted between May and June 2024. FG interviews were conducted with 21 participants from acute hospitals, home care services, and nursing homes between May and June 2024. The interviews were analyzed using the knowledge mapping method, which allowed for a visual representation of key discussion points and highlighted consensus among participants. The discussions centered on five main themes: (1) experiences with current scheduling systems, (2) requirements for work scheduling, (3) fair and participatory work scheduling, (4) requirements for AI in work scheduling, and (5) perceived advantages and disadvantages of AI-based work scheduling.
Participants reported that current scheduling practices often lacked fairness and transparency, leading to dissatisfaction, particularly among permanent nurses. While temporary staff appreciated the flexibility in their schedules, permanent nurses expressed a desire for more autonomy and fairness in shift allocation. AI-based scheduling has the potential to improve shift equity by objectively managing shifts based on pre-defined criteria, thereby reducing bias and administrative burden. However, participants raised concerns about the depersonalisation of scheduling, emphasising the need for human oversight to consider the emotional and contextual factors that AI systems may overlook.
AI-based scheduling systems were perceived as having the potential to be beneficial in improving fairness, transparency and WLB for nurses. However, the integration of these systems must be accompanied by careful consideration of the human element and ongoing collaboration with healthcare professionals to ensure that the technology is aligned with organisational needs. By striking a balance between AI-driven efficiency and human judgement, healthcare organisations can improve nurse satisfaction and retention, ultimately benefiting patient care and organisational efficiency.
工作与生活的平衡（WLB）对于医疗保健领域护士的留用和工作满意度至关重要。传统的轮班排班方式，其特点是工作时间不灵活且员工控制权有限，常常导致压力和不公平感，进而造成高离职率。基于人工智能的排班系统被视为一种有前景的解决方案，因为它能实现更公平、更透明的轮班分配。本研究探讨了护士领导者、长期护士和临时护士对尚未使用的基于人工智能的轮班排班系统在感知公平性、透明度以及对工作与生活平衡的影响方面的看法。
采用定性研究设计，于2024年5月至6月进行焦点小组（FG）访谈。2024年5月至6月期间，对来自急症医院、家庭护理服务机构和养老院的21名参与者进行了焦点小组访谈。访谈采用知识图谱法进行分析，该方法能够直观呈现关键讨论点，并突出参与者之间的共识。讨论集中在五个主要主题上：（1）当前排班系统的经验；（2）工作排班的要求；（3）公平且参与性强的工作排班；（4）工作排班中对人工智能的要求；（5）基于人工智能的工作排班的感知优缺点。
参与者报告称，当前的排班做法往往缺乏公平性和透明度，导致不满情绪，尤其是长期护士。虽然临时员工欣赏其排班的灵活性，但长期护士表示希望在轮班分配上有更多自主权和公平性。基于人工智能的排班有潜力通过根据预定义标准客观管理轮班来提高轮班公平性，从而减少偏见和行政负担。然而，参与者对排班的非人性化表示担忧，强调需要人为监督，以考虑人工智能系统可能忽略的情感和背景因素。
基于人工智能的排班系统被认为有可能在提高护士的公平性、透明度和工作与生活平衡方面带来益处。然而，在整合这些系统时，必须仔细考虑人为因素，并与医疗保健专业人员持续合作，以确保技术与组织需求相契合。通过在人工智能驱动的效率和人为判断之间取得平衡，医疗保健组织可以提高护士满意度和留用率，最终使患者护理和组织效率受益。
