ENHANCING PATIENT SAFETY THROUGH LINEAR PROGRAMMING TECHNIQUE

Linear programming (LP) offers a robust mathematical framework for optimizing resource allocation in healthcare systems, with direct implications for improving patient outcomes and safety. By formulating healthcare challenges—such as staff scheduling, bed assignment, treatment prioritization, and supply chain logistics—as constrained optimization problems, LP models enable decision-makers to maximize efficiency while adhering to clinical and operational constraints. This approach facilitates evidence-based planning by minimizing wait times, reducing medical errors, and ensuring equitable access to care. For example, LP can optimize nurse-to-patient ratios, allocate ICU beds during surges, or streamline diagnostic workflows to reduce delays in treatment. Moreover, LP models can be integrated with real-time data and predictive analytics to support dynamic decision-making in emergency scenarios and chronic care management. Through sensitivity analysis and scenario testing, LP frameworks also help identify critical bottlenecks and evaluate the impact of policy changes on patient safety metrics. As healthcare systems face increasing complexity and demand, linear programming emerges as a vital tool for delivering high-quality, patient-centered care in a resource-constrained environment.