Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach.

Area of Science:

• Operations Research
• Healthcare Management
• Computer Science

Background:

• Outpatient clinics face inefficiencies due to resource limitations and varied patient needs.
• Coordinated care and minimized patient waiting times are critical for efficient healthcare delivery.

Purpose of the Study:

• To develop an optimized model for allocating and sequencing medical resources to minimize total patient waiting time.
• To address inefficiencies in outpatient care delivery caused by resource shortages and demand heterogeneity.

Main Methods:

• Utilized Real-Time Location System (RTLS) data to identify patient care pathways.
• Developed a two-stage Stochastic Mixed Integer Linear Programming (SMILP) model.
• Incorporated uncertainty in care activity duration using Sample Average Approximation (SAA) and Monte Carlo Optimization.

Main Results:

• The proposed SMILP model reduced average patient waiting times by 60% compared to deterministic models.
• Achieved significant waiting time reduction with acceptable computational resource usage and time complexity.
• Demonstrated the model's effectiveness in dynamic outpatient environments with complex coordination needs.

Conclusions:

• The study presents a computationally efficient formulation for optimizing multi-resource allocation and care sequencing under uncertainty.
• The model enables data-driven, real-time adjustments for dynamic outpatient scheduling.
• The findings offer a practical solution for improving efficiency and patient experience in clinical settings.