Multi-objective admission planning problem: a two-stage stochastic approach.

Area of Science:

• Operations Research
• Healthcare Management
• Stochastic Optimization

Background:

• Effective hospital admission planning is crucial for improving patient throughput and reducing wait times.
• Uncertainty in patient arrivals and resource availability complicates patient allocation and hospital management.
• Hospitals face conflicting objectives of optimizing resource utilization and minimizing service costs.

Purpose of the Study:

• To develop a bi-objective stochastic optimization model for hospital admission planning.
• To analyze the trade-off between resource utilization and service costs under demand and capacity uncertainties.
• To evaluate the model's performance using real data from a Chilean public hospital.

Main Methods:

• A bi-objective stochastic optimization model was formulated.
• The model incorporates uncertainties in patient demand and resource capacity.
• Real-world data from surgery and medical departments of a Chilean public hospital was utilized for validation.

Main Results:

• The proposed optimization model demonstrates a superior approach to managing hospital admissions.
• Solutions derived from the model show significant improvements over the hospital's existing admission practices.
• The study quantifies the trade-offs between resource utilization and service costs.

Conclusions:

• The bi-objective stochastic optimization model offers an effective strategy for enhancing hospital admission planning.
• Implementing this model can lead to improved operational efficiency and cost reduction in healthcare settings.
• The approach provides valuable insights for hospitals seeking to balance competing objectives in patient flow management.