Avoiding Engineering Failures in Healthcare Cloud Migrations: Ten Lessons from Platform Modernization at Scale

Cloud migration in the healthcare industry involves high-stakes initiatives impacting not only technical infrastructure but crucially data governance, compliance, and patient care. While public cloud platforms offer significant scalability and flexibility, healthcare organizations frequently encounter systemic challenges during large-scale migrations, including regulatory misalignment, architectural inefficiencies, and operational gaps. Unlike previous literature that primarily focuses on general technical considerations, this paper provides novel insights drawn from hands-on leadership experience in large-scale healthcare data platform modernization efforts. Specifically, it identifies and analyzes ten recurring engineering pitfalls, emphasizing often-overlooked data management issues such as metadata cataloging, data governance, and compliance monitoring. Each identified mistake is supported by concrete, real-world examples, recent academic findings, and explicit practical mitigation strategies utilizing advanced data management tools (e.g., AWS Glue, Lake Formation, and Google Data Catalog). By highlighting these critical data-centric considerations, this work aims to guide engineering leaders, architects, and compliance officers toward building healthcare cloud-native systems that are compliant, cost-effective, resilient, and fully optimized for data reuse, transparency, and adaptability within evolving digital health ecosystems.