The accurate forecasting of healthcare demand is critical for ensuring optimal resource allocation, minimizing costs, and improving patient outcomes. This research aims to develop a robust demand forecasting model tailored to healthcare systems, using time-series modelling. By analyzing historical patient data, seasonal trends, demographic variables, and disease patterns, the model will predict future healthcare demands, including hospital admissions, outpatient visits, and medication needs, for instance. The proposed model will provide healthcare administrators with actionable insights to enhance decision-making, reduce bottlenecks, and improve service delivery, particularly during periods of high demand, such as pandemics or flu seasons. Ultimately, this research seeks to create a more resilient and sustainable healthcare system that is proactive rather than reactive to patient needs.
School of Computer Science and Statistics