Healthcare providers face significant challenges in efficiently scheduling nurses, balancing factors like client preferences, nurse availability, and regulatory shift limits. The growing demand for nursing services adds complexity, especially when softer factors like personality traits and continuity of care are critical to client satisfaction. Existing methods struggle to incorporate these variables, leading to staffing inefficiencies, increased overtime, and inconsistent service quality.
Our solution uses Operations Research (OR) tools to optimize nurse scheduling by integrating client preferences, nurse availability, and legal work-hour constraints.
The OR-based approach effectively handles complex variables, enabling scalable and efficient nurse assignments.
Enhanced scheduling that prioritizes preferred nurses for recurring clients, improving care continuity and satisfaction.
Efficiently matching nurse availability with demand, reducing excess labour costs and preventing burnout.
Ensures adherence to legal shift regulations, improving both staff welfare and service quality.
Introduce machine learning to better match nurses and clients based on traits, preferences, and care history.
Use AI-driven models to forecast nurse demand and optimize schedules proactively.
Develop a dynamic system for real-time schedule changes based on unexpected shifts in availability or client needs.
