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

Journal: BMC nursing
Published Date: Jul 1, 2025

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.

Authors

  • Hye Won Kang
    Department of Nursing, Inha University Hospital, Incheon, Republic of Korea.
  • Jiyoung Kim
    Department of Material Sciences and Engineering, University of Texas at Dallas, Richardson, TX, 75080, USA.
  • Kyoung Ja Kim
    School of Nursing, Inha University, 100 Inha-ro, Michuhol-gu, Incheon, 22212, Republic of Korea.
  • Eun Kyoung Bae
    Department of Nursing, Inha University Hospital, Incheon, Republic of Korea.
  • Heesuk Kang
    Department of Nursing, Inha University Hospital, Incheon, Republic of Korea.
  • Jeong Hee Jang
    Department of Nursing, Inha University Hospital, Incheon, Republic of Korea.
  • Whasuk Choe
    Department of Nursing, Inha University Hospital, Incheon, Republic of Korea.

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