AI-Powered Nurse Rostering for Smarter Care and Better Outcomes

Year Submitted: 2025
Published Date: 26 December 2025

Tags: Care Process ＆ Redesign, Operational Management, Resource Allocation, Productivity, Cost Saving, Time Saving, Quality Improvement, Workflow Redesign, Technology, Digitalisation, Automation, Artificial Intelligence

About this Content

Aims

The project aimed to streamline the roster generation process by reducing manual effort and time required, while improving nurse satisfaction through an AI-enabled rostering system that supports timely, flexible, and transparent scheduling.

Background

Nurse Leaders faced substantial challenges with time-consuming manual roster creation, resulting in delays in shift communication, inefficiencies in accommodating staff preferences, and difficulties in meeting roster rule requirements. These issues negatively affected nurses' morale and work-life balance as well as care delivery.

Methods

An iterative design approach was employed to explore, test, and implement new system features. Close collaboration was undertaken with roster planners and ward leaders to co-develop and refine the AI model. Rule adherence and changes were continuously reviewed to identify areas for performance improvement. A phased deployment strategy was adopted to ensure scalability, adaptability, and user satisfaction

Results

NurseShift.ai reduced the time spent on roster management by 51%. Post-implementation surveys revealed improved nurse satisfaction, with users citing increased flexibility, timely roster availability, greater transparency, and a more positive scheduling experience for both roster planners and staff.
The solution provides healthcare sustainability.

Conclusion

The AI-powered rostering solution, NurseShift.ai, effectively streamlined the nurse scheduling process, improved nurse satisfaction, and ensured compliance with rostering rules, ultimately enhancing patient care delivery.

Lessons Learnt

Conduct thorough needs assessments before system design or selection, set realistic timelines to account for complexity in rostering requirements, engage stakeholders early to gather insights and secure buy-in, communicate clearly the purpose, benefits, and expectations to ease transitions, proactively address resistance through listening and support, and provide comprehensive training to build user confidence and ensure effective adoption.

Additional Information

This project has won National Healthcare Innovation and Productivity Awards 2025 under Best Practice (AI, IT and Robotics Innovation) category.

Keywords

Artificial Intelligence, Scheduling, Nurse, Productivity

Innovators' Details

Connect with this contributor!

Kavitha Dhanabal - kavitha.dhanabal@mountelizabeth.com.sg

Project Attachment

214_IHH_NHIP_2025_AI_Powered_Nurse_Rostering_for_Smarter_Care_and_Better_Outcomes.pdf