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Related Experiment Video

Updated: Feb 14, 2026

Improving IV Insulin Administration in a Community Hospital
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Optimizing Cooperative Community Hospital Selection for Post-Discharge Care with NSGA-II Algorithm.

Zhenli Wu1, Yunxuan Li1, Xin Lu1

  • 1College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

Healthcare (Basel, Switzerland)
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

Optimizing hospital-primary care collaboration improves post-discharge patient access while managing costs. A new model aids partner selection and resource planning for efficient healthcare delivery.

Keywords:
NSGA-II algorithmfollow-up servicemedical resource allocationmulti-objective mixed-integer programmingpost-discharge care

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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.