Optimizing Cloud Based Data Management for Healthcare Systems Using Intelligent Superiority Computing

Abstract

Cloud-based data management remains foundational for healthcare organizations in the United States, but these infrastructures continue to struggle with latency, bandwidth constraints, and persistent data-security vulnerabilities. Intelligent Edge Computing (IEC), a distributed architecture, addresses these weaknesses by enabling data processing and AI-driven decision support directly at the point of data generation, thereby reducing dependence on remote cloud servers. This study used a mixed-method design involving 315 healthcare IT professionals from 25 U.S. hospitals. Regression modeling, correlation analysis, and χ² testing were applied to assess changes in system performance, latency patterns, energy-efficiency indicators, and compliance outcomes before and after IEC implementation. Regression outputs indicated that IEC variables were strong predictors of overall system performance, reflected by R = 0.85, R² = 0.72, F = 196.5, p = 0.001. Performance improvement was primarily driven by edge-node density and AI load-balancing efficiency, with coefficients of β = 0.62 and β = 0.57, respectively, both significant at p < 0.001. χ² tests showed notable differences in system-usage frequency across job roles (χ² = 18.9, df = 2, p = 0.001), while gender-based variation was not statistically meaningful (p = 0.29). IEC deployment produced a 26% increase in operational efficiency, a 29% cost reduction, and a 17% improvement in HIPAA compliance. Integrating IEC into cloud-based healthcare systems enhances performance, scalability, and regulatory compliance, positioning it as a critical solution for next-generation clinical data infrastructures.