The Internet of Things (IoT) is transforming the healthcare industry by enabling real-time patient monitoring, predictive analytics and smart decision making across interconnected medical environments. It's still hard to do things like timely response in an emergency, energy efficient scheduling, secure data collection, and accurate anomaly detection, particularly in large hospital networks. This paper proposes an intelligent IoT-driven healthcare framework that incorporates three novel algorithms to fill these gaps: (i) S3AD (Smart Sensor Data Acquisition and Anomaly Detection) for accurate and privacy-preserving physiological sensing; (ii) HEPS (Healthcare Event Prediction and Patient Scheduling) for proactive event forecasting and priority-based, energy-aware task scheduling; and (iii) CARES (Context-Aware Response and Emergency Strategy) for adaptive, real-time emergency management. Python-based simulations were used to develop a modular, layered architecture that combined cloud, edge, and device-level processing with 2,000 simulated IoT devices that transmitted SpO2, heart rate, and temperature data. The experimental results outperform six state-of-the-art algorithms in terms of scalability and efficiency, with a minimum response time of 3.1 s, a maximum anomaly detection accuracy of 97.5%, and an optimal energy usage of 300,000 mWh. Additional machine-learning models and Long Short-Term Memory (LSTM) networks improve diagnostic reliability and throughput. For next-generation healthcare IoT systems, the suggested framework creates a safe, long-lasting, and compatible base. Future research will examine explainable AI-driven decision assistance, blockchain-based data integrity, and robust industrial IoT integration for extensive smart medical infrastructures.
Bhatia et al. (2026) studied this question.