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Healthcare Resource Allocation

  • Unique Paper ID: 177294
  • Volume: 11
  • Issue: 12
  • PageNo: 391-396
  • Abstract:
  • Hospitals and healthcare systems around the world face a significant challenge: the efficient allocation of healthcare resources. Optimized resource management is more important than ever because of the growing number of patients, the shortage of beds, medical personnel, and supplies, as well as the growing expenses of running a hospital. Reducing total healthcare expenditures, improving patient care quality, and cutting down on patient waiting times are the main goals of healthcare resource allocation. These goals, however, frequently clash; for example, cutting waiting times would necessitate hiring more personnel and allocating more funds, which would raise expenses; concentrating just on cutting costs, on the other hand, might result in lower-quality care or longer treatment delays. This study examines optimization techniques that can reconcile these conflicting objectives and discusses the intricate trade-offs associated with the distribution of healthcare resources. We examine existing procedures, spot inefficiencies, and suggest a multi-objective optimization approach that combines decision-support tools, real-time data, and predictive modelling. To make well-informed allocation decisions, the method takes into account dynamic scheduling, resource availability, and patient prioritizing. Using data-driven methods like operations research and machine learning, the suggested strategy seeks to increase operational effectiveness without sacrificing care quality. Predictive analytics-based strategic allocation can greatly improve healthcare delivery, as shown by case studies and simulation findings. This study emphasizes how crucial it is to implement intelligent, scalable, and adaptable systems for allocating resources in order to satisfy the expanding needs of contemporary healthcare. The ultimate objective is to provide a sustainable healthcare setting where cost-effectiveness, service efficacy, and patient outcomes are all maximized
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 391-396

Healthcare Resource Allocation

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