Research Article
AI-Driven, Full-Stack Blood Donation Management System for Self-Sustaining, Efficient, and Scalable Healthcare in India
@INPROCEEDINGS{10.4108/eai.28-4-2025.2357768, author={Arekallu Aravind and Beemireddy Omprakash Reddy and Cheruvu Belagal Chenna Keshava Reddy and Daram Guru Mahesh and Malangiri Govardhan Lakshman}, title={AI-Driven, Full-Stack Blood Donation Management System for Self-Sustaining, Efficient, and Scalable Healthcare in India}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part I}, publisher={EAI}, proceedings_a={ICITSM PART I}, year={2025}, month={10}, keywords={ai in healthcare; blood donation systems; federated learning; predictive analytics; healthcare logistics}, doi={10.4108/eai.28-4-2025.2357768} }- Arekallu Aravind
Beemireddy Omprakash Reddy
Cheruvu Belagal Chenna Keshava Reddy
Daram Guru Mahesh
Malangiri Govardhan Lakshman
Year: 2025
AI-Driven, Full-Stack Blood Donation Management System for Self-Sustaining, Efficient, and Scalable Healthcare in India
ICITSM PART I
EAI
DOI: 10.4108/eai.28-4-2025.2357768
Abstract
Despite the importance, blood donation and its management still confound India’s health care ecosystem in a number of ways: Fragmented infrastructure, low donor retention resulting in low stock availability and reactive logistics. In this paper, we present a novel AI driven full stack blood donation management system intended to make the healthcare operations self-sustained, efficient and scalable. The system, which encompasses privacy preserving predictive analytics via federated learning, AI powered demand forecasting and smart emergency routing enhances the blood supply chain optimization. In order to enhance donor engagement and also include urban and rural populations the gamified user interface and multilingual support were added. The experimental results indicate that we can achieve a 37% decrease in the fulfilment time, 58% reduction in the inventory shortages, as well as a better forecast for the demand. This architecture shows high potential of national scalability and becomes a transformative approach to digital health logistics in emerging economies.
