Optimizing Cooperative Community Hospital Selection for Post-Discharge Care with NSGA-II Algorithm
<b>Background</b>: With the growing emphasis on full-process disease management, efficient post-discharge care has become increasingly critical. Although prior studies have examined follow-up services, resource allocation, and facility location in primary healthcare, model-based optimization of collaborative frameworks between comprehensive hospitals and primary care systems remains limited. <b>Methods</b>: We study a cooperative community hospital selection problem involving contractual cooperation, patient engagement, and follow-up resource allocation. A multi-objective mixed-integer programming model is developed to maximize patient accessibility and minimize total hospital costs, and an NSGA-II-based heuristic is proposed for solution generation. A real-world case study using data from a comprehensive hospital in Chengdu, China, is conducted. <b>Results</b>: The proposed approach produces a Pareto set that quantifies the accessibility-cost trade-off and reveals a knee region with diminishing returns: moderate expansion of cooperating providers substantially improves accessibility, whereas further expansion yields limited additional gains while increasing hospital cost. Sensitivity analyses indicate that cost-related parameters and follow-up frequencies are key drivers of the trade-off. <b>Conclusions</b>: The proposed optimization framework serves as an implementable decision aid for designing hospital-primary care collaboration for post-discharge follow-up: it supports partner selection and capacity planning and indicates levers to improve performance.
Authors
- Yunxuan Li
- Xin Lu (ORCID: https://orcid.org/0000-0001-9885-6031)
- Zhenli Wu
Institutions
- National University of Defense Technology (CN)
Publication Details
- Journal
- Healthcare
- Published
- 2026-02-02
- DOI
- https://doi.org/10.3390/healthcare14030372
- Primary Topic
- Healthcare Operations and Scheduling Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00