Lived Experience of Nurses and Nursing Managers on the Application of Artificial Intelligence in Shift Scheduling Optimization

Keshavarz, Sirous; Taboli, Hamid; Nazari, Amaneh

doi:10.22034/imhr.2026.581441.1090

Lived Experience of Nurses and Nursing Managers on the Application of Artificial Intelligence in Shift Scheduling Optimization

Document Type : Original Article

Authors

sirous keshavarz ¹

hamid taboli ²

amaneh nazari ³

¹ Center for Management Studies, Tarbiat Modares University, Tehran, Iran

² Department of Management, Payame Noor University, Tehran, Iran

³ M.Sc. Graduate in Industrial Engineering, Faculty of Engineering, Islamic Azad University, Roudehen Branch, Tehran, Iran

10.22034/imhr.2026.581441.1090

Abstract

Background and Objective: Artificial intelligence-based work measurement and time-tracking systems hold significant potential for optimizing nurse shift scheduling and more efficiently managing their workload.
Methodology: This research aimed to investigate the experiences of nurses and managers regarding the implementation and utilization of such a system within a hospital setting, seeking to identify its challenges, opportunities, and impacts on operational and managerial processes. A qualitative approach using semi-structured interviews was employed. Participants included nurses and managers (with expertise in human resources management, operations, planning, and AI development) at various levels within a hospital. Data collected from the interviews were analyzed using the thematic analysis method and Maxqda software to identify the main and overarching themes related to users’ experiences with the AI system.
Findings: The data analysis revealed eight primary overarching themes: 1) Enhancing organizational efficiency through intelligent work measurement, 2) Increasing perceived fairness and job satisfaction, 3) Organizational agility and dynamic decision-making, 4) Gradual trust in AI systems, 5) Financial optimization and cost-effectiveness, 6) Managerial agility and empowerment with AI, 7) Balancing machine logic with human considerations, and 8) Intelligent shift optimization with a human-centered approach.
Conclusion: Therefore, although AI systems for work measurement and time measurement of nurses increase efficiency, they also face challenges related to adoption, training, and maintaining a balance between technical and human aspects; thus, their success requires a comprehensive approach and active staff participation.

Keywords

Artificial Intelligence

Work Measurement

Time Tracking

Shift Scheduling

Nursing

Subjects