Scalable Cloud Systems for Automated Precision Medicine
DOI:
https://doi.org/10.5281/zenodo.21413134Keywords:
Data Ecosystems, Cloud Native, Healthcare Systems, Predictive Analytics, Decision Systems, Microservices, Service Mesh, Data Contracts, Precision Healthcare, Disease Prediction, Hybrid Cloud, Multi Cloud, Scalability, Resilience, Disaster Recovery, Data Protection, Access Control, Regulatory Compliance, Cloud Infrastructure, Service Architecture.Abstract
The paper discusses the critical importance of data ecosystems within cloud-native environments designed for predictive and automated decision-making systems in healthcare. The evolving requirements for healthcare service delivery necessitate an alternative architectural paradigm that goes beyond traditional monolithic systems and monolithic software applications hosted in dedicated private data centers or single public cloud infrastructures. Service-oriented deployments are best modeled using microservices and service meshes, data contracts, and independent deployments.
Precision healthcare addresses disease prevention, detection, forecasting, diagnosis, management, and outcome prediction for individuals or specific population sets. It leverages underlying compute, storage, and networking services designed for traditional enterprise workloads with strict recoverability and business continuity requirements. Frameworks for scalability, resilience, and disaster recovery translate elasticity features from the public cloud domain, while supporting cloud-native solutions in hybrid or multi-cloud environments. The success of such an approach is entirely dependent on effective data protection techniques, access controls, and the ability to demonstrate compliance with multiple regulations.
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