Enhancing Telemedicine and Digital Health Product Delivery Using Artificial Intelligence

Authors

  • Prof. Kenji Rattan

Abstract

The rapid growth of telemedicine has transformed healthcare accessibility, yet challenges remain in integrating digital consultations with physical product delivery. This paper proposes an AI-based integrated platform that connects telemedicine services with automated prescription processing and medicine delivery systems. Natural Language Processing (NLP) is used to analyze doctor-patient interactions, while AI-driven recommendation systems optimize medication fulfillment and delivery schedules. The framework ensures seamless coordination between digital healthcare services and physical product distribution. Results indicate improved patient experience, reduced delivery times, and increased operational efficiency.

References

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

Chawla, N. (2021). DESIGNING RESILIENT FINANCIAL APIS USING ZERO-TRUST AND ADAPTIVE SECURITY MODELS. Phoenix: International Multidisciplinary Research Journal (Peer reviewed High Impact Journal), (1), 21.

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.

Published

2023-12-29

How to Cite

Rattan, P. K. (2023). Enhancing Telemedicine and Digital Health Product Delivery Using Artificial Intelligence. Indonasian Journal of Multidisciplinary Innovations , 5(5). Retrieved from https://scholarlyarticle.vncinstitute.com/index.php/IJMI/article/view/65

Issue

Section

Articles