Replication-Aware Caching for Low-Latency Clinical Knowledge Retrieval
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706033Keywords:
Replication-Aware Caching, Clinical Knowledge Retrieval, Electronic Health Records, Adaptive Cache Management, Healthcare Data Systems, Low-Latency Computing, Distributed Clinical IntelligenceAbstract
The rapid evolution of the healthcare systems of the online services, electronic health records (EHRs) and clinical decision support systems increased the necessity to ensure the rapid and efficient access to the medical knowledge repositories. The traditional caching solutions might not be efficient because of dynamic clinical loads, variance in access patterns of data and consistency constraints of distributed healthcare environments. In this paper, the hypothesis of a Replication-Aware Caching Framework (RACF) will be put to support the retrieval of the clinical knowledge with low latency in reply to smart data replication, dynamic regulation of the cache and priority of context based retrieval. The framework, which has been proposed includes a Clinical Data Ingestion Layer, Knowledge Representation Layer, Replication Intelligence Engine, Adaptive Caching Layer, Consistency Management Module, and Low-Latency Retrieval Interface. The Replication Intelligence Engine relates frequency of query, clinical relevance, frequency frequency of temporal access patterns and resource availability to be useful strategies to optimal replication. The adaptive caching mechanism presents the changes and attains the synchronisation of the distributed nodes according to the dynamic priorities of the clinical concepts, diagnostic guidelines and drug information applied in the past and the patient-specific knowledge. Another policy included in the framework is the adoption of consistency-sensitive replication policies in order to minimize the number of stale information in vital healthcare applications. Based on experimental work on simulated clinical workload, evaluation on the system shows that the system offers more performance in terms of response times in retrieving a system, cache hits and network overheads of the solution when compared to the traditional caching solutions. The suggested solution offers an effective basis of scalable, intelligent and reliable clinical knowledge management systems.
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