LifePing: A GPS-Assisted Real-Time Emergency Blood Donor Coordination Framework
Gaurav Naithani, Manisha Negi, Shivang Kothari, Arushi, Anshul Namdev
Abstract
Timely access to compatible blood remains a critical challenge in emergency healthcare systems, particularly in trauma care, surgical emergencies, maternal complications, and accident response scenarios where treatment delays may directly affect patient survival. Despite advancements in healthcare digitization, existing blood donor coordination mechanisms in many regions continue to rely on fragmented databases, manual communication workflows, non-updated donor records, and institution-centric coordination practices. These limitations reduce operational efficiency during emergencies and create a significant gap between urgent blood demand and the practical availability of nearby eligible donors. The absence of intelligent location-aware matching and real-time donor prioritization further increases coordination latency during high-pressure medical situations. This paper proposes LifePing, a GPS-assisted real-time emergency blood donor coordination framework designed to enhance donor discovery, emergency communication, and healthcare response efficiency through an integrated digital coordination architecture. The proposed framework combines centralized donor management, geospatial filtering, automated emergency alerting, and compatibility-driven donor ranking within a scalable client-server environment. The system is designed to support hospitals, blood banks, emergency coordinators, and recipients by enabling rapid identification of donors based on blood group compatibility, geographic proximity, donor availability, and verification status. The proposed architecture is implemented using the Django web framework for backend orchestration, MongoDB for flexible and scalable data persistence, and responsive web technologies for user interaction and request handling. To improve emergency response performance, the framework introduces a weighted donor prioritization model that integrates compatibility score, spatial distance, donor activity status, and verification reliability into a unified ranking mechanism. Geographic proximity estimation is performed using geolocation-based distance computation methods to ensure rapid nearby donor identification under emergency constraints. The modular architecture separates authentication, donor management, emergency processing, matching, and notification services into independent functional layers, thereby improving extensibility, maintainability, and deployment, scalability Experimental prototype evaluation and simulated emergency-response analysis demonstrate that the proposed framework significantly reduces donor search time and improves coordination efficiency compared with conventional manual donor lookup procedures. Performance analysis indicates improvements in search responsiveness, donor accessibility, and communication reliability under repeated emergency request scenarios. The results further suggest that integrating location intelligence with automated healthcare coordination mechanisms can substantially strengthen emergency blood management infrastructure. The study contributes a scalable and research-oriented framework for intelligent emergency blood donor coordination by combining healthcare informatics, geospatial computation, and real-time workflow automation. The proposed system establishes a foundation for future extensions involving AI-driven donor prediction, mobile health integration, cloud-native healthcare deployment, predictive emergency analytics, and smart hospital interoperability within next-generation digital healthcare ecosystems.
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