Big Data Infrastructure and Analytics Framework for Next-Generation Smart Healthcare Systems

The exponential growth of healthcare data necessitates sophisticated big data infrastructures to enable intelligent and timely clinical decision-making. In this study, we proposed a multi-layered framework that seamlessly integrates edge computing, distributed storage, and advanced analytics to improve clinical outcomes and operational efficiency, while simultaneously addressing critical challenges, including data privacy, interoperability, and scalability. The framework supports high-volume batch and streaming data processing, low-latency query execution, and real-time machine learning inference, scaling efficiently to thousands of concurrent users with robust fault tolerance. Our evaluations demonstrate the proposed framework's effectiveness, yielding a 23 % increase in sepsis detection accuracy, 94.3 % dermatology concordance, a 22-minute reduction in clinician documentation, a 28 % decrease in medication errors, and a 31 % improvement in diabetic complication risk identification. These findings underscore the framework’s potential to transform real-time healthcare analytics into tangible improvements in care quality and clinical decision support. © 2025 IEEE.