MUMBAI, India, Feb. 27 -- Intellectual Property India has published a patent application (202631017963 A) filed by C. V. Raman Global University, Bhubaneswar, Orissa, on Feb. 18, for 'predictive ai model for early prediction of resource shortages and chronic health outbreaks.'

Inventor(s) include Mohammad Awez Haider; Soma Kar; Aritra Nandi; Gaurav Shaw; Sachin Mahato; Rochita Bhowmik; and Dr. Rakesh Ranjan Kumar.

The application for the patent was published on Feb. 27, under issue no. 09/2026.

According to the abstract released by the Intellectual Property India: "This invention relates to a comprehensive AI-powered predictive system designed to forecast resource shortages and chronic health outbreaks in healthcare facilities, particularly in rural and underserved regions. The system integrates advanced machine learning algorithms with digitalized medical records and real-time data analytics to predict potential disease outbreaks before they occur. The invention leverages natural language processing and pattern recognition to identify early warning signs from medical documentation, enabling proactive resource allocation. The predictive model is trained on historical medical data patterns, seasonal trends, and demographic factors to accurately forecast resource requirements. An interconnected hospital network architecture enables seamless resource sharing between healthcare facilities during shortage periods. The system incorporates a donor management module that streamlines donation requests and fulfillment processes. The technology provides real-time shortage alerts, predictive dashboards, and automated resource matching algorithms. Additionally, the system maintains comprehensive digital records while ensuring data security and HIPAA compliance. The invention significantly improves healthcare service delivery in resource-constrained environments by enabling evidence-based resource planning and rapid response mechanisms. The practical implementation demonstrates improved patient outcomes, reduced supply chain disruptions, and enhanced inter-hospital collaboration across regional healthcare networks."

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