AI-Driven Real-Time Tracking and Optimization of Healthcare Product Delivery Systems
Abstract
Efficient and transparent delivery of healthcare products is essential for ensuring timely patient care and operational effectiveness. This paper proposes an AI-driven real-time tracking and optimization system that leverages machine learning, GPS data, and predictive analytics to monitor and manage the movement of medical supplies. The framework provides dynamic route optimization, delay prediction, and automated alerts to stakeholders, enabling proactive decision-making. By integrating real-time data streams with intelligent algorithms, the system enhances delivery accuracy, reduces transit time, and minimizes operational costs. Experimental results demonstrate significant improvements in supply chain visibility, reliability, and responsiveness, making it a viable solution for modern healthcare logistics.
References
Davenport, T. H., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98.
Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731.
Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017). Disease prediction by machine learning over big data from healthcare communities. IEEE Access, 5, 8869–8879.
Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: A literature review. International Journal of Production Research, 57(15–16), 4719–4742.
Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: An analysis and recent literature. International Journal of Production Research, 56(1–2), 414–430.
Chawla, N., Kotla, P., Venna, S. R., & Patel, M. B. (2025, August). Comprehensive Analysis of Robotic Process Automation for Software Project Management. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-4). IEEE.
Yugandhar, M. B. D., Goli, A. K. R., Goli, S. R., & Chawla, N. (2025, August). Comprehensive Analysis of Challenges in Deploying AI Models in FinTech. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.
Chawla, N. (2025). AI-Driven Predictive Project Risk Management in Large-Scale Financial Software Programs. J. Electrical Systems, 21(1s), 618-626.
Chawla, N., & Dasnam, S. V. (2024). AI-Assisted Change Impact Analysis for Legacy-to-Cloud Migration in Banking Systems. Sch J Eng Tech, 12, 411-417.
Singh, D. (2025). Explainable Graph-Based AI for Real-Time Fraud Detection in Distributed Healthcare Claims Processing Systems. International Journal of Science, Technology and Convergence, 7(7). Retrieved from https://ijcdra.us/index.php/IJSTC/article/view/60
Patel, M. B., Singh, D., Yugandhar, M. B. D., & Konda, R. (2025, August). Comprehensive Analysis of Automl Techniques for Data-Driven Decision Making. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-5). IEEE.
Singh, D. (2022). Managing API Evolution in Large-scale Microservices: Versioning and Backward Compatibility. International Journal of Science, Technology and Convergence, 4(4). Retrieved from https://ijcdra.us/index.php/IJSTC/article/view/61
Singh, D., & Deshpande, G. (2025). PREDICTIVE AI FOR IDENTIFYING VULNERABILITIES BEFORE RANSOMWARE ATTACKS IN HOSPITALS. Phoenix: International Multidisciplinary Research Journal (Peer reviewed High Impact Journal), (4), 29.
Singh, D. (2023). Designing Resilient Event-Driven Microservices Using AWS SQS/SNS and Domain-Driven Design for Real-Time Systems. Australian Journal of Cross-Disciplinary Innovation , 5(5). Retrieved from https://journals.theusinsight.com/index.php/AJCDI/article/view/160
Singh, D. (2022). Optimizing Enterprise Search Performance Using EHCache-Backed Apache Lucene Indexing for Hybrid Caching Systems. Australian Journal of Cross-Disciplinary Innovation , 4(4). Retrieved from https://journals.theusinsight.com/index.php/AJCDI/article/view/161
Singh, D., Yugandhar, M. B. D., & Chawla, N. (2024). Design and Implementation Strategies for Scalable RESTful APIs in Enterprise Systems.
Singh, D. (2024). Enhancing Member Risk Profiling Using Data-Driven Architectures in Healthcare.
Deshpande, G., & Singh, D. (2025). AI-ASSISTED SECURITY ORCHESTRATION IN HEALTHCARE INCIDENT RESPONSE. Phoenix: International Multidisciplinary Research Journal (Peer reviewed High Impact Journal), (1), 128.
Singh, D.(2025) OCR-Driven Automation: A Case Study on Document Processing Using Tesseract and OpenCV.
Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358.
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657–2664.
Shah, S., & Patel, M. (2020). AI-driven healthcare supply chain optimization. International Journal of Logistics Management, 31(2), 345–362.
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904–2915.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.
Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22–28.
Agarwal, R., Gao, G., DesRoches, C., & Jha, A. K. (2010). The digital transformation of healthcare: Current status and the road ahead. Information Systems Research, 21(4), 796–809.
