MIGRATING LEGACY HEALTHCARE SYSTEMS TO CLOUD-NATIVE MICROSERVICES WITH AI: BEST PRACTICES AND PITFALLS

Abstract

This paper discusses the challenge and opportunity of migrating legacy health care information systems to cloud-native microservices designs with additional artificial intelligence capabilities. Healthcare organizations have unique constraints including regulatory compliance requirements, data sensitivity concerns, and the high-value nature of the requirement for continuous availability of services. Through case study analysis and best practices from the industry, we identify top implementation strategies, pitfalls, and a road map to successful migration that achieves the optimal balance between innovation, patient safety, and data protection. Our findings are that a phased risk-managed implementation with suitable governance models and specialized AI modules can bring significant improvement in system scalability, interoperability, and clinical decision support with minimal disruption to care delivery.