Predictive Healthcare Analytics Using AI on Modernized Big Data Platforms: Transforming Clinical Outcomes and Operational Excellence - Srimanth Maddipatla (International Journal of Computational and Experimental Science and Engineering, 2026)
Recent transformations of healthcare analytics with artificial intelligence, machine learning, and modern big data have helped in guiding clinical decision-making, allocating resources, and improving clinical outcomes. Healthcare organizations are challenged with managing the rapid inflow of electronic health records, medical imaging, genomic sequencing, wearable technologies, and real-time patient monitoring devices, which require analytics infrastructures beyond what traditional systems can handle. Cloud-native architectures, distributed computing models, and scalable data stores enable the new generation of predictive analytics for anticipatory care models, which leverage cutting-edge artificial intelligence algorithms such as deep learning, natural language processing, and time-series analysis to extract insights from multi-dimensional and heterogeneous healthcare data and generate predictions of clinical deterioration, readmissions, and operational bottlenecks. Health systems show real-world implementations can reduce mortality, enhance intensive care unit capability and flow from the emergency department, and increase operating room capacity. Organizations with more advanced analytics capabilities and experience can achieve greater clinical impact, operational efficiencies, and cost reductions while remaining regulatory compliant and acting ethically. The ultimate vision for AI-enabled transformation in healthcare is a learning health system, in which clinical data continuously collected from the real world feed into predictive models to inform clinical decision-making across the individual patient population. Achieving this vision requires active cultural, cross-domain (clinical/technical/regulatory/ethical), and technological advancement.