Real-Time Genomic Analytics in Clinical Practice: A Framework for High-Throughput Data Processing and Decision Support

Abstract

Recent advances in genomic sequencing technologies have generated unprecedented volumes of clinical genomic data, necessitating robust real-time analytics solutions for immediate clinical decision support. This article presents a comprehensive framework for implementing real-time genomic data processing in clinical settings, addressing the challenges of high-throughput data management while maintaining patient privacy and data security. The article examines the integration of distributed computing frameworks and stream processing technologies to facilitate rapid analysis of genomic data alongside clinical phenotypic information. The article reveals that modern healthcare informatics platforms can effectively manage multi-modal datasets through optimized data pipelines, enabling faster diagnostic processes and improved patient outcomes. The article demonstrates how real-time analytics can enhance clinical decision-making through immediate variant calling and interpretation while supporting larger population-scale genomic studies. The article discusses solutions to critical challenges in data quality management, privacy preservation, and computational resource optimization. The findings suggest that real-time genomic analytics can significantly improve diagnostic speed and accuracy while advancing preventative healthcare strategies through better identification of genetic risk factors. This article contributes to the growing field of precision medicine by providing a scalable approach to managing and analyzing genomic data in time-critical clinical environments.