Automated Blood Bank Management System: Integrating RFID and AI for Real-Time Inventory Tracking

Abstract

Traditional blood bank systems face critical inefficiencies due to manual processes, leading to delays, errors, and significant blood wastage. This paper proposes an Automated Blood Bank Management System leveraging RFID technology and AI-driven analytics to optimize real-time inventory tracking, reduce wastage, and enhance emergency response. The system integrates RFID tags for unit-level monitoring, enabling precise tracking of blood stock levels, expiry dates, and storage conditions. AI algorithms predict demand patterns and prioritize emergency requests through dynamic priority queuing, reducing response times by 40% compared to manual systems. A hybrid cloud-edge architecture ensures scalability, while compliance with WHO and FDA standards is maintained through AES-256 encryption and role-based access control. Through automated SMS/IVR reminders, a mid-sized hospital\'s pilot study showed a 25% increase in donor retention and a 30% decrease in blood waste. The IoT-enabled dashboard of the system offers real-time information into compliance audits, donor involvement, and inventory trends. The findings demonstrate how the framework\'s inclusive design—which includes multilingual voice interfaces for non-tech users—can bridge healthcare gaps between urban and rural areas. By fusing AI\'s predictive capabilities with RFID\'s granular tracking, our work enhances blood bank automation and provides a scalable solution for international healthcare systems. AI-powered mobile apps for tailored donor interaction and blockchain integration for impenetrable audits are examples of future additions.

Introduction

Blood banks are critical for healthcare, but global inefficiencies cause 20% of blood wastage and delays in 30% of emergency transfusions. Traditional manual or fragmented systems lead to human errors, wasted expired blood, slow emergency responses, and poor donor engagement—especially in rural areas where access and technology are limited.

The paper proposes an Automated Blood Bank Management System that integrates AI and RFID technology to tackle these issues. AI forecasts demand using historical and environmental data, prioritizes emergency requests, and supports dynamic donor-recipient crossmatching. RFID tags track blood units in real-time, monitoring location, storage conditions, and expiry. The system uses a hybrid cloud-edge architecture for scalability and complies with FDA and WHO safety standards via encryption and audit trails.

Pilot implementations showed a 30% reduction in blood wastage, 75% faster emergency response times, and a 25% increase in donor retention, helped by gamification and multilingual IVR for rural users. The system advances beyond earlier models by combining AI demand forecasting with IoT transparency and rural adaptability.

The study involved surveys, case studies, and field visits, and was developed using Django, React, MySQL, and Raspberry Pi-based IoT components. Testing showed significant improvements: reducing manual errors by 83%, boosting forecast accuracy to 99.2%, and cutting response times from 2 hours to 30 minutes. Staff found the system intuitive, and donor feedback was positive, though initial RFID training posed some challenges.

The work sets the foundation for future enhancements such as blockchain integration for secure audits and aims to improve equity in blood access and safety globally.

Conclusion

An innovative solution to long-standing inefficiencies in donor engagement, emergency response, and blood inventory management is the Automated Blood Bank Management System. This solution addresses important issues including blood waste, delayed emergency responses, and donor retention by combining RFID technology with AI-driven data. It also encourages inclusivity in rural healthcare access.

References

[1] A. Ghiasi, M. R. Abdar, and M. Dehghan, \"Design and Implementation of a Web-Based Blood Bank Management System,\" Procedia Computer Science, vol. 62, pp. 500–507, 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877042815036940 [2] S. S. Chawla and M. G. Meena, \"Blood Bank Management and Inventory Control Database,\" Procedia Computer Science, vol. 167, pp. 1560–1569, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S187705092102500X [3] M. Ayatollahi, H. Mirhosseini, and S. H. Haghani, \"An RFID-Based Blood Tracking System in Hospitals,\" Journal of Medical Systems, vol. 35, no. 5, pp. 1045–1050, 2011. [Online]. Available: https://link.springer.com/article/10.1007/s10916-009-9426-1 [4] J. Yao, J. Chu, and Z. Li, \"The Application of Artificial Intelligence in Blood Supply Chain Management,\" arXiv preprint, 2020. [Online]. Available: https://arxiv.org/abs/2008.07486 [5] M. Taghavi, et al., \"A Review of Artificial Intelligence Techniques in Blood Donation and Transfusion,\" Artificial Intelligence in Medicine, vol. 111, 101983, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0933365720302020 [6] N. Sharma, P. Bhatia, and R. K. Sharma, \"A Secure Blood Bank Management System Using RFID and IoT,\" International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 6, pp. 1226–1230, 2019. [Online]. Available: https://www.ijitee.org/wp-content/uploads/papers/v8i6/F3930048619.pdf [7] P. B. Aysu, Y. S. Safak, and G. S. Turgut, \"RFID Based Blood Bank Management System,\" International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9214170 [8] A. E. Obiniyi and A. A. Salihu, \"Design and Implementation of Blood Bank Management System Using Machine Learning,\" International Journal of Advanced Computer Science and Applications (IJACSA), vol. 12, no. 6, 2021. [Online]. Available: https://thesai.org/Downloads/Volume12No6/Paper_60-Design_and_Implementation_of_Blood_Bank.pdf [9] Technavio, \"RFID Blood Refrigerator and Freezer Market Size is Set to Grow by USD 498.3 Million from 2024-2028,\" PR Newswire, 2024. [Online]. Available: https://www.prnewswire.com/news-releases/rfid-blood-refrigerator-and-freezer-market-size-is-set-to-grow-by-usd-498-3-million-from-2024-2028--high-demand-for-blood-and-blood-components-boost-the-market-technavio-302189613.html [10] Grand View Research, \"RFID Blood Monitoring Systems Market Size & Share Report, 2030,\" GVR, 2024. [Online]. Available: https://www.grandviewresearch.com/industry-analysis/rfid-blood-monitoring-systems-market [11] N. Azman, S. K. Subramaniam, and M. Esro, \"Investigation and Development of a Data Acquisition System for Blood Bank,\" International Journal of Artificial Intelligence, vol. 10, no. 1, pp. 21–38, 2023. [Online]. Available: https://www.lamintang.org/journal/index.php/ijai/article/download/488/368 [12] J. Yao, J. Chu, and Z. Li, \"The Application of Artificial Intelligence in Blood Supply Chain Management,\" arXiv preprint, 2020. [Online]. Available: https://arxiv.org/abs/2008.07486

Copyright

Copyright © 2025 Mr. Lachigoudugari Pavan Kumar, Mr. P Charan Deep, Mr. N Ranjith Kumar, Mr. K Dileep Yadav, Dr. R. Karunia Krishnapriya, Mr. Pandreti Praveen, Mr. N. Vijaya Kumar, Mr. V. Shaik Mohammad Shahil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.