International Journal of Science, Engineering and TechnologyAuthors: Anmol Tyagi, Abhishek Tyagi, Vansh Sharma, Anas Malik, Shivam Tyagi, Dr. Iqbal
Abstract: The rapid advancement of Artificial Intelligence (AI) and cloud-based healthcare technologies has transformed the way medical organizations manage patient communication and service delivery. This research presents an AI-powered healthcare contact center system designed to improve patient engagement, real-time monitoring, and dynamic call prioritization through intelligent computational techniques. The proposed system integrates cloud-native architecture, machine learning models, sentiment analysis, and healthcare interoperability standards to create a smart and responsive communication framework for modern healthcare environments. The system focuses on real-time patient journey mapping, allowing healthcare providers to monitor patient interactions, treatment stages, and emergency conditions more efficiently. By utilizing AI-driven prioritization mechanisms, the framework automatically classifies patient requests based on urgency, emotional state, and clinical context, ensuring that critical cases receive immediate attention. The proposed model also incorporates speech and sentiment analysis to detect stress, discomfort, or emotional instability during patient conversations, thereby enabling proactive medical intervention.To ensure scalability and flexibility, the architecture follows a distributed microservices approach integrated with standardized healthcare data exchange protocols. The framework supports seamless communication between healthcare databases, patient management systems, and intelligent analytical modules. Experimental analysis demonstrates improvements in operational efficiency, response time, patient satisfaction, and healthcare resource utilization when compared with conventional healthcare communication systems. The research highlights the significance of intelligent healthcare communication platforms in reducing delays, improving care continuity, and supporting personalized patient experiences. Furthermore, the study discusses implementation challenges related to data privacy, ethical AI usage, interoperability, and regulatory compliance. The proposed framework offers a future-ready solution for smart healthcare ecosystems and demonstrates the potential of AI-driven communication systems in enhancing the quality and accessibility of healthcare services.
