A Cloud-Integrated Internet of Things Framework for Optimizing Operations in Healthcare Industries
Year: 2025
Type: article
Abstract: The healthcare industry faces critical challenges such as equitable access to quality care, efficient resource utilization, patient engagement, and data security. To address these issues, this paper proposes an innovative Internet of Things (IoT)-based system that integrates advanced technologies into healthcare operations. The system leverages IoT sensors and cloud platforms for real-time monitoring of vital signs (heart rate, blood pressure, oxygen levels) and environmental conditions (temperature, humidity, air quality), while also tracking medical equipment and supply usage for optimized resource allocation. Unlike existing healthcare IoT frameworks that mainly emphasize monitoring, the proposed system uniquely integrates real-time anomaly detection, energy-efficient resource optimization, and regulatory-compliant data security into a single, scalable platform, validated through experimental results. A user-friendly interface benefits administrators, professionals, and patients - administrator optimize workflows using analytics tools, medical professionals gain real-time access to patient data for improved decision-making, and patients receive personalized health insights with enhanced communication. Experimental validation demonstrates 95.2% anomaly detection accuracy, a 30% reduction in response time, a 25% improvement in energy efficiency, and 99.1% data transmission reliability. These results confirm the system’s ability to enhance patient outcomes, reduce operational costs, and ensure robust security through compliance with Health Insurance Portability and Accountability Act and General Data Protection Regulation standards. Overall, this scalable and cost-effective solution modernizes healthcare delivery, fostering a connected and patient-centered ecosystem.
Authors: Satyajeet Shinge, Urmila Shrawankar
Institution: Raisoni Group of Institutions
Language: English
Topic: Internet of Things and AI
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid:
OpenAlex ID: W4414915870