Shift nurses’ work quality and job satisfaction after implementing the Inha University hospital nursing AI scheduling system (IH-NASS)
Abstract 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 qual...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Nursing |
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| Online Access: | https://doi.org/10.1186/s12912-025-03470-6 |
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| author | Hye Won Kang Jiyoung Kim Kyoung Ja Kim Eun Kyoung Bae Heesuk Kang Jeong Hee Jang Whasuk Choe |
| author_facet | Hye Won Kang Jiyoung Kim Kyoung Ja Kim Eun Kyoung Bae Heesuk Kang Jeong Hee Jang Whasuk Choe |
| author_sort | Hye Won Kang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-4e233b347ba44565b38c9aa55ad5e87c |
| institution | Kabale University |
| issn | 1472-6955 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nursing |
| spelling | doaj-art-4e233b347ba44565b38c9aa55ad5e87c2025-08-20T03:45:24ZengBMCBMC Nursing1472-69552025-07-0124111010.1186/s12912-025-03470-6Shift nurses’ work quality and job satisfaction after implementing the Inha University hospital nursing AI scheduling system (IH-NASS)Hye Won Kang0Jiyoung Kim1Kyoung Ja Kim2Eun Kyoung Bae3Heesuk Kang4Jeong Hee Jang5Whasuk Choe6Department of Nursing, Inha University HospitalSchool of Nursing, Inha UniversitySchool of Nursing, Inha UniversityDepartment of Nursing, Inha University HospitalDepartment of Nursing, Inha University HospitalDepartment of Nursing, Inha University HospitalDepartment of Nursing, Inha University HospitalAbstract 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.https://doi.org/10.1186/s12912-025-03470-6Shift work scheduleNursePerceptionJob satisfactionArtificial intelligence |
| spellingShingle | Hye Won Kang Jiyoung Kim Kyoung Ja Kim Eun Kyoung Bae Heesuk Kang Jeong Hee Jang Whasuk Choe Shift nurses’ work quality and job satisfaction after implementing the Inha University hospital nursing AI scheduling system (IH-NASS) BMC Nursing Shift work schedule Nurse Perception Job satisfaction Artificial intelligence |
| title | Shift nurses’ work quality and job satisfaction after implementing the Inha University hospital nursing AI scheduling system (IH-NASS) |
| title_full | Shift nurses’ work quality and job satisfaction after implementing the Inha University hospital nursing AI scheduling system (IH-NASS) |
| title_fullStr | Shift nurses’ work quality and job satisfaction after implementing the Inha University hospital nursing AI scheduling system (IH-NASS) |
| title_full_unstemmed | Shift nurses’ work quality and job satisfaction after implementing the Inha University hospital nursing AI scheduling system (IH-NASS) |
| title_short | Shift nurses’ work quality and job satisfaction after implementing the Inha University hospital nursing AI scheduling system (IH-NASS) |
| title_sort | shift nurses work quality and job satisfaction after implementing the inha university hospital nursing ai scheduling system ih nass |
| topic | Shift work schedule Nurse Perception Job satisfaction Artificial intelligence |
| url | https://doi.org/10.1186/s12912-025-03470-6 |
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