Optimization of Patient and Nurse Management in Health Care: A Case Study

Abstract

Effective patient selection and nurse allocation are crucial for optimizing hospital care, especially in resource-constrained environments. This study introduces a two-stage mixed integer linear programming (MILP) model designed to prioritize patients based on care urgency and match them with appropriately skilled nurses. Following consultations with healthcare professionals, patients were categorized into three priority levels: high (critical care), normal (basic care), and low (expert consultations). Applying the model to the surgery department of Pabna Medical College Hospital, Bangladesh, 196 out of 950 patients were selected for care: 117 requiring critical care, 59 needing basic care, and 20 for consultative care, necessitating the allocation of 41 nurses. The model demonstrated significant improvements in patient selection and nurse workload management, ensuring maximum patient service within capacity constraints and achieving a balanced distribution of nurse workloads. This approach enhances hospital efficiency and patient care quality, allowing non-chosen patients to seek alternative care options. The model’s adaptability across different departments underscores its potential for widespread application in healthcare management.

Journal of Engineering Science 15(1), 2024, 83-94