Lifeline Analytics: A Predictive Approach to Blood Demand Management
Prof. Sharvani V · International Journal for Research in Applied Science and Engineering Technology · 2025
Lifeline Analytics addresses the critical challenges in blood supply management by combining advanced data analysis with predictive modeling to optimize both demand forecasting and donor engagement [1],[3]. Drawing from historical utilization records, inventory trends, and behavioral indicators, the platform enables proactive decision-making that minimizes shortages and wastage[4]. Its intuitive, role-specific dashboard unites administrators, clinicians, and donor coordinators through real-time insights, interactive visualizations, and automated alerts [5]. Preliminary deployments have demonstrated measurable improvements in inventory stability and donor retention. Built on a modular, secure architecture, Lifeline Analytics integrates seamlessly with hospital systems while adhering to healthcare data standards. Scalable microservices support ongoing data expansion and rapid analytical updates. Embedded feedback loops ensure model refinement based on user interaction and regional variations. This robust, adaptive framework provides a forward-looking, resilient, and patient-centric approach to blood supply management.