Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The exponential growth in healthcare data, coupled with the increasing demand for real-time analytics, presents significant challenges in ensuring timely and efficient data access. Traditional data access mechanisms often struggle with latency and data inconsistency, particularly in large-scale healthcare systems. Intelligent caching mechanisms, driven by machine learning and predictive analytics, offer a promising solution to bridge this gap. This paper presents an in-depth study and implementation of intelligent caching strategies tailored for healthcare analytics platforms. We explore context-aware, adaptive, and predictive caching models that leverage historical data usage patterns, semantic understanding, and patient-centric data clustering to enhance patient care. The proposed framework incorporates a multi-layered caching architecture optimised for Electronic Health Records (EHRs), medical imaging, and real-time monitoring systems. Experimental results demonstrate significant improvements in query response time, cache hit rate, and overall system throughput. We also address critical concerns related to data privacy, consistency, and compliance with healthcare regulations such as HIPAA. By integrating advanced caching techniques with healthcare analytics, this study aims to enhance decision-making efficiency, reduce system load, and ultimately improve patient care outcomes.