Optimizing Cooperative Community Hospital Selection for Post-Discharge Care with NSGA-II Algorithm

Area of Science:

• Healthcare Management
• Operations Research
• Public Health

Background:

• Efficient post-discharge care is crucial for comprehensive disease management.
• Limited research exists on optimizing collaborative frameworks between hospitals and primary care systems.
• Prior studies focused on specific aspects like follow-up services or facility location.

Purpose of the Study:

• To develop a model-based optimization framework for hospital-primary care collaboration.
• To address the selection of community hospitals, patient engagement, and follow-up resource allocation.
• To maximize patient accessibility and minimize total hospital costs in post-discharge care.

Main Methods:

• A multi-objective mixed-integer programming model was developed.
• An optimization approach was proposed using a Non-dominated Sorting Genetic Algorithm II (NSGA-II).
• A case study was conducted using data from a hospital in Chengdu, China.

Main Results:

• The model generated a Pareto set illustrating the accessibility-cost trade-off.
• A 'knee region' was identified, showing diminishing returns from expanding cooperating providers.
• Sensitivity analyses highlighted cost parameters and follow-up frequency as key trade-off drivers.

Conclusions:

• The optimization framework provides a decision-aid for designing hospital-primary care collaborations.
• It supports strategic decisions in partner selection and capacity planning.
• The study identifies key factors for improving post-discharge follow-up performance.