Optimizing Cloud Infrastructure for Large-Scale Electronic Health Record Systems

The exponential growth of electronic health record data, coupled with increasing demands for real-time clinical access and advanced analytics, has made infrastructure optimization a critical priority for healthcare organizations adopting cloud computing. This research addresses the challenge of optimizing cloud infrastructure specifically for large-scale electronic health record systems, focusing on the intersection of performance, cost efficiency, security, and compliance. The study employed a design science research methodology to develop and validate an optimization framework encompassing compute resource selection, storage tiering, auto-scaling strategies, and financial operations. The proposed framework integrates right-sizing methodologies using continuous monitoring and recommendation engines, performance-optimized compute instances including AWS Graviton processors demonstrating 40 percent better price-performance ratios compared to x86 architectures, and multi-tier storage strategies leveraging lifecycle policies to reduce costs by transitioning infrequently accessed data to lower-cost tiers. A hybrid approach combining reserved capacity for baseline workloads with on-demand and spot instances for variable demand achieved up to 72 percent cost savings compared to pure on-demand deployments. The framework incorporates auto-scaling policies tuned for healthcare workload patterns, including predictable diurnal variations and surge capacity for public health events. Disaster recovery optimization through isolated recovery environments reduced recovery time objectives while controlling standby costs. Case study validation with a multi-hospital system demonstrated 20 percent improvement in application performance, 19 percent acceleration in response times, and 18 percent reduction in compute resources through Graviton3 migration, with corresponding monthly savings of $3,600. A second case study of medical record processing achieved 86 percent cost reduction and 66 percent faster processing through prompt caching optimization. The research contributes a validated optimization framework with implementation guidance for healthcare organizations, addressing the unique constraints of electronic health record systems including licensing dependencies, minimum capacity requirements, and regulatory compliance. The findings demonstrate that systematic infrastructure optimization can simultaneously improve performance, reduce costs, and maintain the high availability and security essential for clinical environments.