Exploring Nurse Perspectives on AI-Based Shift Scheduling for Fairness, Transparency, and Work-Life Balance

Abstract Introduction 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 offer a promising solution by enabling fairer and more transparent shift distribution. This study explored the perspectives of nurse managers, permanent nurses, and temporary nurses on the perceived fairness, transparency, and impact on work-life balance of AI-based shift scheduling systems. Methods A qualitative study design was used, with focus group (FG) interviews conducted between May and June 2024. The sample consisted of 21 participants from different healthcare settings, including acute hospitals, home care services and nursing homes in German-speaking Switzerland. The interviews were analysed using the knowledge mapping method, which allowed for a visual representation of key discussion points, highlighting consensus among participants. The focus group discussions revolved around five main themes, such as experiences with current scheduling systems, expectations of AI-based scheduling, and its potential advantages and disadvantages. Results 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. Conclusion AI-based scheduling systems could offer significant benefits in improving fairness, transparency and work-life balance for caregivers. 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.