ArticleBMC nursing2025
Exploring nurse perspectives on AI-based shift scheduling for fairness, transparency, and work-life balance.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Management strategies to mitigate burnout and turnover intention while enhancing patient safety in neonatal intensive care: an integrative review.Frontiers in public health · 2026Pooled it
- Informal Digital Coordination of Nurse Shift Swaps in a Saudi Tertiary Hospital: A Qualitative Descriptive Study.Healthcare (Basel, Switzerland) · 2026Article
- Explainable AI for Equitable Nurse Scheduling: Pragmatic Pre-Post Implementation Study.JMIR nursing · 2026Article
- Enhancing hospital workforce planning, scheduling, and performance evaluation through an AI-driven human resource management system.Scientific reports · 2026Article
- A qualitative study on the mismatch between health literacy and nursing needs in the postoperative recovery of breast cancer: nurse and survivor perspectives.BMC nursing · 2026Article
- Physicians' perceptions and attitudes on artificial intelligence in hospital management: a qualitative study.BMC health services research · 2025Article
- Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study.JMIR formative research · 2025Article
- Artificial Intelligence in Nursing Governance and Regulation: An Umbrella Review of Ethical and Policy Dimensions.SAGE open nursingReview
- Automating Rule-Compliant and Equitable Call Schedules for Orthopedic Surgery Residents With Artificial Intelligence and Large Language Models: A Simulation-Based Validation Study.Journal of medical education and curricular developmentArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionWork-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.
methodsA 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.
resultsParticipants 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.
conclusionAI-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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.