AI and Big Data-Driven m-Health: Integrating Cloud Computing, Remote Patient Monitoring, Clinical Decision Support Systems, and Self-Supervised Learning with FHIR for Scalable Healthcare Systems

Abstract

Background The health sector is being transformed by AI, big data, and m-Health systems. Scalable, interoperable, and efficient healthcare solutions can be achieved through the integration of technologies like Remote Patient Monitoring (RPM), Clinical Decision Support Systems (CDSS), Self-Supervised Learning (SSL), and Fast Healthcare Interoperability Resources (FHIR). The proposed frameworks support real-time monitoring, personalized care, and improved clinical decision-making.
Methods This research utilizes cloud computing, IoT-enabled RPM, CDSS for evidence-based insights, and SSL for the analysis of unstructured data. FHIR ensures interoperability for the free exchange of data across platforms. The AI-driven architecture integrates these components to build a robust m-Health system that supports real-time monitoring, predictive analytics, and scalable healthcare management.
Objectives The key objectives include providing remote access to healthcare, improving clinical decision-making through AI, and using cloud computing to optimize scalability in healthcare, all while ensuring seamless interoperability with FHIR. This framework addresses healthcare challenges in underserved areas, reduces hospital visits, and promotes proactive care through real-time data-driven recommendations.
Results The proposed framework reaches superior performance, accuracy at 94%, scalability at 93%, and F1 score at 95%. Combining RPM, CDSS, SSL, and FHIR, it outperforms the traditional methods in terms of anomaly detection and efficiency to provide a scalable efficient solution for modern healthcare challenges.
Conclusion It integrates RPM, CDSS, SSL, and FHIR with an AI-driven framework to bring significantly improved healthcare delivery. It enhances efficiency, scalability, and decision-making, in contrast with traditional healthcare systems. This approach promotes proactive care support to underserved areas and establishes a robust foundation for scalable m-Health systems.