ArXiv

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

2026-08-06 · 14:44 UTC ·Manideep Dhar, Ritwik Singh, Sharat Chandra Kumar Manikonda ·1 min read
Authors
Manideep Dhar, Ritwik Singh, Sharat Chandra Kumar Manikonda
Categories
cs.AI, cs.CL, cs.LG, cs.MA
arXiv
https://arxiv.org/abs/2608.06112v1
PDF
https://arxiv.org/pdf/2608.06112v1

Brief

A compliance-first, agentic AI architecture for hospitals addresses why 70–80% of healthcare AI pilots fail to scale by combining an Agent Orchestration Layer, a centralized Compliance & Policy Layer, and a Privacy-Preserving Data Fabric. Using a synthetic hospital dataset and an open prototype, the paper demonstrates end-to-end triage risk prediction, workflow optimization, and auditable compliance logging with simulated gains in turnaround and documentation efficiency.

Why it matters

Dhar, Singh, and Manikonda (arXiv:2608.06112v1, published 2026-08-06) propose a compliance-first, multi-layer hospital AI architecture adding an Agent Orchestration Layer, a Compliance & Policy Layer (policy-as-code for HIPAA, GDPR, EU AI Act, DISHA Act, India’s DPDP Act, ISO/IEC), and a Privacy-Preserving Data Fabric (federated learning, differential privacy, secure enclaves).

Key details

  • With a synthetic but structurally realistic hospital dataset and an open prototype, the authors demonstrate end-to-end orchestration of triage risk prediction, workflow optimization, and compliance logging, reporting substantial simulated reductions in task turnaround times and manual documentation effort while preserving policy-guarded data access.
Source evidence

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 unrealized 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 centralizes policy-as-code for HIPAA, GDPR, the EU AI Act, DISHA Act, 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 optimization, 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.

Comment: Peer-reviewed published article
Journal: IJISRT, 11-2026(5), IJISRT26MAY1651