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Background <p>Shift 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.</p> Methods <p>A 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.</p> Results <p>Compared to traditional manual schedules, IH-NASS-generated schedules significantly reduced the number of nurses with &lt; 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.</p> Conclusions <p>This 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.</p> Trial registration <p>Not applicable. This was not a clinical trial.</p>

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Shift nurses’ work quality and job satisfaction after implementing the Inha University hospital nursing AI scheduling system (IH-NASS)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Hye Won Kang,
  • Jiyoung Kim,
  • Kyoung Ja Kim,
  • Eun Kyoung Bae,
  • Heesuk Kang,
  • Jeong Hee Jang,
  • Whasuk Choe

摘要

Background

Shift 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.

Methods

A 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.

Results

Compared 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.

Conclusions

This 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 registration

Not applicable. This was not a clinical trial.