Exploring nurse perspectives on AI-based shift scheduling for fairness, transparency, and work-life balance

Area of Science:

• Healthcare Management
• Nursing Informatics
• Artificial Intelligence in Healthcare

Background:

• Work-life balance (WLB) is crucial for nurse retention and job satisfaction.
• Traditional scheduling often leads to stress, unfairness, and high turnover rates.
• AI-based scheduling offers potential for fairer, more transparent shift distribution.

Purpose of the Study:

• To explore nurse leaders', permanent nurses', and temporary nurses' perspectives on AI-based shift scheduling.
• To assess perceived fairness, transparency, and impact on WLB of AI scheduling systems.
• To identify requirements and concerns regarding AI in work scheduling.

Main Methods:

• Qualitative study design utilizing focus group (FG) interviews.
• 21 participants from acute hospitals, home care, and nursing homes.
• Knowledge mapping method for analysis, focusing on five key themes.

Main Results:

• Current scheduling lacks fairness and transparency, causing dissatisfaction, especially for permanent nurses.
• AI scheduling can improve equity and reduce bias but risks depersonalization.
• Need for human oversight to address emotional and contextual factors.

Conclusions:

• AI scheduling shows potential for improving nurse fairness, transparency, and WLB.
• Successful integration requires balancing AI efficiency with human judgment and collaboration.
• Addressing human elements ensures technology aligns with organizational needs and improves nurse satisfaction and retention.