Integrated Architecture for Hospital AI Systems

Summary & Methodology Analysis

The proposed architecture addresses the scaling failures inherent in siloed deployments by moving toward an agent-orchestrated framework. At its core, an Agent Orchestration Layer coordinates workflows across clinical, operational, and financial domains. This layer functions as the primary hub for multi-agent interaction, ensuring that separate AI tasks are unified rather than fragmented. By decoupling the logic from specific point solutions, the architecture provides a blueprint for scalable hospital systems that manage data flow across traditional departmental boundaries. To ensure regulatory adherence, the authors implement a dedicated Compliance and Policy Layer. This component centralizes policy-as-code, codifying the requirements of HIPAA, GDPR, the EU AI Act, the DISHA Act, India's DPDP Act, and various ISO/IEC standards into the system logic. This allows for automated governance rather than manual verification at each deployment node. Data privacy is handled by a Privacy-Preserving Data Fabric that integrates federated learning (a training approach where models are trained locally on data across multiple devices or servers without exchanging the raw data itself) and differential privacy (a mathematical framework that adds noise to datasets to ensure individual records cannot be identified) alongside secure enclaves within the existing Hospital Information Management System.