HomeResearch LibraryAdvancing healthcare AI governance through a comprehens…
Chapter 6 · 2026

Advancing healthcare AI governance through a comprehensive maturity model based on systematic review

Rowan Hussein, Anna Zink, Bashar Ramadan

Abstract

Artificial Intelligence (AI) deployment in healthcare is accelerating, yet governance frameworks remain fragmented and often assume extensive resources. Through a systematic review of 35 frameworks for AI implementation in healthcare (published 2019–2024), we identified seven critical domains of healthcare AI governance. To address the gap where resource requirements create barriers for smaller healthcare organizations, we organized key findings from the review to create the Healthcare AI Governance Readiness Assessment (HAIRA), a five-level maturity model that provides actionable governance pathways based on organizational resources. This tiered approach enables healthcare organizations to assess their current AI governance capabilities and establish appropriate advancement targets.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper contributes useful building blocks for Zone III architecture through its work on healthcare AI governance, maturity model, systematic review. While not exclusively focused on enterprise deployment, the insights translate directly to the challenges of long-horizon agentic workflows. The key lesson for Zone III practitioners: the problems identified here do not disappear at scale — they compound. Understanding them at the research level is prerequisite to solving them in production.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

healthcare AI governancematurity modelsystematic reviewAI implementationrisk mitigation