Optimizing maternal and child health services with operations research techniques approach
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Abstract
Operations research is used to optimize mother and child health services through appointment scheduling and resource allocation. Public health is reflected in maternal and child health. Maternal and infant death rates remain a global issue despite medical advances. These issues stem from mother and child health service inefficiencies and poor care. This study uses operations research to improve healthcare delivery and patient outcomes.The study begins by identifying maternal and child health service issues such high wait times, insufficient resource allocation, and poor appointment scheduling. It then creates a mathematical formulation model that encompasses healthcare system intricacies including patient flow, resource use, and appointment scheduling. Linear programming, simulation, queuing theory, and data analytics enhance patient scheduling for varying medical urgency levels and time needs. A numerical illustration illustrates the mathematical formulation model. Patient wait times, resource allocation, and service efficiency improved significantly. Early time slots favor patients with higher medical urgency, ensuring timely healthcare treatments. Optimized resource use prevents overcrowding and ensures appointment equity. Stakeholder engagement and collaboration with healthcare practitioners, administrators, policymakers, and others are stressed throughout the study process. Key stakeholders can adjust proposed solutions to mother and child health service requirements and obstacles, improving acceptance and feasibility. This research advances operations research-based mother and child health service optimization. Data-driven decision-making and creative approaches aim to improve mother and child health service delivery, resource usage, and patient outcomes. Global mother and child health initiatives and sustainable development goals might benefit from evidence-based policy decisions and healthcare management solutions.
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