From Siloed Algorithms to Compliance‑First Agentic Platforms: A Multi‑Layered Architecture for Hospital AI Systems

Authors:: Manideep Dhar, Ritwik Singh, Sharat Chandra Kumar Manikonda
Lab:: Instil-it AI Research Hub

Abstract

Hospitals are rapidly adopting artificial intelligence for triage, imaging, scheduling etc., yet most deployments remain isolated point solutions locked inside departmental silos, resulting in duplicated effort, hidden risks, and unrealised enterprise value. Despite explosive growth of AI in healthcare market and accelerating investment, an estimated 70–80% of healthcare AI pilots fail to scale, largely due to governance gaps, fragmented data, and missing integration blueprints. This research proposes a hospital-specific, compliance-first, Agentic AI architecture with multiple interoperable layers, extending existing hospital AI platform models with: (i) an Agent Orchestration Layer for multi-agent workflows across clinical, operational, and financial domains, (ii) a Compliance and Policy Layer that centralises policy-as-code for HIPAA, GDPR, the EU AI Act, DISHA, India’s DPDP Act, and ISO/IEC security and safety standards, and (iii) a Privacy-Preserving Data Fabric that plugs federated learning, differential privacy, and secure enclaves into real-world Hospital Information Management System (HIMS) flows. Using a synthetic but structurally realistic hospital dataset and an open, ready-to-deploy prototype implementation, this study demonstrates the end-to-end orchestration of triage risk prediction, workflow optimisation, and compliance logging, achieving substantial simulated reductions in task turnaround times and manual documentation effort while maintaining policy-guarded data access. The resulting architecture offers hospital leaders a pragmatic blueprint to move from ad hoc tools to a governed, globally compliant, ROI-focused AI platform that can be tailored to on-premise, hybrid, and cloud-native deployments.

Key Innovations and Highlights

World's First 7-Layered Agentic AI Architecture for Hospital Information Management System
This research introduces a novel hospital-specific, compliance-first, 7-layered digital platform designed to transition healthcare facilities from isolated "shadow data" apps to unified enterprise-grade systems.
Privacy-Preserving Data Fabric:
Eliminates data silos by creating a standardized, policy-filtered gateway connecting directly to real-world HIMS components like the EHR, Laboratory Information Systems (LIS), Picture Archiving and Communication Systems (PACS), and billing software.
Unified Global Adaptability Blueprint:
The platform changes its legal and deployment structures via dynamic policy configurations rather than requiring entirely separate software codebases for different countries making it readily adaptable globally.
India Compliance Fitment
Features localized, on-premise deployment topographies that adhere to strict data localization laws, patient consent rules under the India’s DPDP Act 2023, and India’s first hospital AI architecture developed to be compliant with the upcoming DISHA framework.
Exponential Cost Savings
Transitioning away from fragmented departmental point solutions results in a massive 67% cost reduction (approx.) compared to siloed deployments, achieving over $8.2 million in cumulative savings over 5 years (projected).
Crushing Technical Debt
Reusable interfaces in the data fabric and centralized compliance controls reduce overall data integration, access control, and audit trail engineering efforts by around 71% to 78%.
Compliance-by-Design as Standard
Treats regulatory compliance as an immutable, technical system layer built directly into the data runtime rather than handling it as a post-hoc or peripheral validation checklist.