The intersection of the Internet of Things (IoT) and wearable devices is transforming patient monitoring because they allow the provision of data-driven, uninterrupted, and remote healthcare services. The paper examines real-time health management and decision-making clinical and operational situations on how these technologies, combined with business analytics frameworks, can improve real-time health management and decision-making. Carrying out a synthesis of the current breakthroughs and large-scale deployments throughout the worldwide health system, the paper explores the operational synergy of smart medical devices and analytical platforms in care outcome optimization, response time decrease, and resource utilization. This study employs a data-driven observational study design to analyze high-frequency physiological measurements recorded by wearable sensors and connected medical devices in a range of chronic and acute care conditions. business analytics tools are applied to the collected data in order to isolate actionable business insights, spot anomalies, and enable predictive risk modelling. The study methodology focuses on real-time data capture, patient stratification and cross-sectional system performance measures assessment. The results indicate a considerable positive shift in early intervention abilities, patient adherence, and operational effectiveness, proving that real-time analytics based on IoT-connected wearables can decrease the number of hospitalizations and fine-tune treatment plans. Another influential obstacle pointed out in the study is data privacy, device interoperability, and the digital divide. The study has its contribution to the emerging domain of healthcare informatics, as it presented a scalable and replicable model of implementing IoT and analytics in patient monitoring. It meets the existing literature gaps by uniting the technological, clinical, and business standpoints and offering practical insights that can be used by health IT leaders, policymakers, and clinicians who want to transform the care delivery models using smart and data-driven solutions.
Saeed et al. (Mon,) studied this question.