Real-Time Healthcare Workforce Rescheduling using a Quantum Computer: A Novel Approach to Dynamic Staff Allocation in Hospital Settings

Real-time healthcare workforce scheduling represents a critical optimization challenge with direct implications for patient care quality and operational efficiency. This article introduces a quantum computing approach to dynamic hospital staff rescheduling that addresses the complex constraints of medical workforce allocation when facing unexpected absences. By formulating the problem as a quadratic unconstrained binary optimization (QUBO) model suitable for quantum processing, demonstrate how quantum annealing and gate-based quantum algorithms can rapidly identify optimal staff substitutions while balancing factors such as specialist qualifications, shift duration, fatigue management, and geographical proximity. The simulation results indicate that quantum-accelerated scheduling can significantly reduce the average time to fill critical absences compared to classical heuristic methods while maintaining high adherence to staffing quality metrics. This has substantial potential to mitigate the downstream clinical impacts of staffing disruptions, particularly in high-acuity hospital departments where timely specialist presence is essential for patient outcomes.