Optimal Allocation of Resources and Hospital Capacity Planning for Critical Diseases using AI and Data Mining

Critical illness management relies heavily on the strategic allocation of resources and the well-planned construction of hospital infrastructure. In this study, we propose to adopt the “AI-OptiHealth” technique, which employs artificial intelligence (AI) and data mining technologies to revolutionize resource allocation and capacity planning during severe disease epidemics. We evaluate the results of this approach in depth and compare them to those of more traditional methods. Our proposed AI-OptiHealth system combines the Patient Risk Prediction (PRP) algorithm with the Resource Demand Forecasting (RDF) algorithm and the Resource Allocation Optimization (RAO) algorithm. Three distinct modules, the Resource Demand Forecaster (RDF), the Resource Allocation Optimizer (RAO), and the Patient Risk Profiler (PRP), work together to improve resource allocation in response to dynamic situations. The results show that the AI-OptiHealth system outperforms the status quo in a number of key areas. Better patient outcomes can be achieved by accurate risk assessment and strategic use of resources. In addition to being very flexible, the method also takes ethical considerations into account when allocating resources. The AI-OptiHealth method represents a major advancement in recent years for allocating healthcare resources and planning capacity for critical diseases. Its use of AI and data mining yields resource allocation techniques that are highly efficient, malleable, and ethical, eventually leading to better patient care during pandemics. This research emphasizes the need for novel approaches to healthcare management in order to address the enduring challenges of the sector.