Intelligent Decision Support System for Health Care Resource Management

Abstract

The proposed paper suggests an Intelligent Decision Support System (IDSS) to support the management of healthcare resources with the help of a Hybrid AI Architecture based on the fusion of Machine Learning (ML) and Knowledge Graphs. The system also seeks to streamline resource allocation i.e. bed allocation, staff scheduling and equipment allocation through predictive analytics and real time decisions. The key tools that we use in this approach are Azure AI and Azure Knowledge Graph. The machine learning of Azure AI is employed to create models that predict patient demand and resource needs, and the Azure Knowledge Graph organizes and combines heterogeneous healthcare data, i.e., patient history, diagnosis and resource availability. Moreover, Retrieval-Augmented Generation (RAG) is being introduced to offer live data based and predictive model recommendations. This integrated solution guarantees scalable, adaptive, and transparent decision-making and eventually, the efficiency of healthcare, lowers cost, and improves the quality of care using smart and data-driven resource management.

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