ArticleBMC nursing2025
Shift nurses' work quality and job satisfaction after implementing the Inha University hospital nursing AI scheduling system (IH-NASS).
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 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Stakeholder experience with artificial intelligence in healthcare: a bibliometric study of satisfaction, trust, acceptance, and patient engagement.Frontiers in digital health · 2026Pooled it
- Evaluation of an AI-Based Constraint-Optimization Scheduler to Optimize On-Call Schedule Equity and Reduce Administrative Burden in a Pediatric Residency: Retrospective Comparative Study.Journal of medical Internet research · 2026Article
- Explainable AI for Equitable Nurse Scheduling: Pragmatic Pre-Post Implementation Study.JMIR nursing · 2026Article
- The impact of reality shock on professional identity among junior nurses: a latent profile analysis.Frontiers in medicine · 2026Article
- Latent class analysis of sleep-related worry and its influencing factors among shift work nurses: a cross-sectional study.Frontiers in public health · 2026Article
- Digital Innovations in Nursing Workforce Management: A Scoping Review of Advanced Applications and Implementation Challenges.Journal of nursing management · 2026Article
- Clinical nurses as digital guardians: unlocking the key determinants of digital resilience in the AI era.Frontiers in public health · 2026Article
- Physicians' perceptions and attitudes on artificial intelligence in hospital management: a qualitative study.BMC health services research · 2025Article
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7 authors.
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Abstract
backgroundShift work is essential for nurses and is the backbone of the healthcare workforce. Addressing the challenges associated with time-consuming scheduling is crucial for ensuring nurses' work quality, optimal staffing levels, and increased job satisfaction. We compared the work quality from both organizational and individual perspectives after the implementation of the Inha University Hospital Nursing Artificial Intelligence (AI) Scheduling System (IH-NASS), and analyzed the factors influencing nurses' job satisfaction, focusing on their perceptions of IH-NASS and work quality.
methodsA total of 253 shift nurses from 14 wards where the IH-NASS was implemented at a tertiary university hospital in Korea were selected. Data from the traditional manual (December 2022, retrospective study) and the IH-NASS-generated schedules (December 2023, prospective study) were compared. Nurses' general characteristics, IH-NASS perceptions (convenience, satisfaction, and fairness), and job satisfaction were surveyed and analyzed.
resultsCompared to traditional manual schedules, IH-NASS-generated schedules significantly reduced the number of nurses with < 1 year of experience in day shifts. From an individual perspective, the number of night-off-evening (NOE) shifts was significantly lower. Additionally, IH-NASS-generated schedules had more consecutive off days (≥ 2), off days (≥ 2) following two or more consecutive night shifts, Saturday-Sunday off days, and Sunday off days, whereas weekday shifts with unsocial hours were fewer. Factors influencing job satisfaction among shift nurses included satisfaction with the IH-NASS, perceived convenience of the IH-NASS, and the number of NOE shifts under unhealthy work scheduling, which together accounted for approximately 27% of the variance in job satisfaction.
conclusionsThis study provides empirical evidence supporting the use of AI systems in nurse scheduling. Specifically, AI-based scheduling can optimize workforce allocation while maintaining work quality, enhancing nurses' positive perceptions, and improving job satisfaction.
trial registrationNot applicable. This was not a clinical trial.
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