Architecting Cloud-Native, Observability-Driven Healthcare Platforms: Integrating DevOps, DataOps, and Machine Learning for Scalable Cardiovascular Prediction Systems

• Dr. Helena Sørensen

  Department of Digital Systems and Innovation University of Copenhagen, Denmark

  Author

The accelerating convergence of cloud-native architectures, enterprise integration platforms, and machine learning-driven healthcare analytics has redefined the technological landscape of modern clinical systems. Cardiovascular diseases continue to represent a leading cause of global mortality, demanding predictive and scalable digital infrastructures capable of integrating clinical intelligence with enterprise-grade cloud environments. While prior scholarship has examined individual domains—such as scalable Heroku-Salesforce integrations, observability in cloud-native systems, cloud data service architectures, and supervised machine learning for heart disease prediction—a comprehensive synthesis bridging cloud-native engineering, enterprise integration, and intelligent healthcare analytics remains insufficiently explored.

This study develops a theoretically grounded and publication-ready framework for designing cloud-native, observability-driven healthcare platforms capable of supporting intelligent heart disease prediction systems at scale. Drawing strictly from the provided scholarly corpus, the research synthesizes insights from scalable application engineering, multitenant cloud data architectures, digital transformation theory, DevOps-DataOps-MLOps convergence, and intelligent cloud-based cardiovascular prediction models. The methodological approach employs conceptual architectural synthesis, mapping theoretical constructs from cloud transformation literature to healthcare machine learning deployment requirements.

The results propose a layered architecture integrating cloud-native transformation patterns, infrastructure observability, enterprise integration via iPaaS, multitenant data services, and supervised learning pipelines for cardiovascular risk prediction. Emphasis is placed on scalability, compliance, operational transparency, and digital maturity. The discussion examines governance challenges, compliance implications in healthcare, operational resilience, and the strategic role of deliberate digital transformation in sustaining cloud-native health ecosystems.

This research contributes an integrated theoretical model for designing scalable, compliant, and observability-enabled cardiovascular prediction platforms within modern enterprise cloud environments, offering both academic insight and architectural guidance for future intelligent healthcare systems.

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