Abstract

As artificial intelligence becomes increasingly embedded in healthcare operations, governance can no longer be treated solely as a static set of principles or regulatory requirements. This study proposes a Human-Centered Experience Engineering (HCEE)-based healthcare AI governance architecture designed as a closed-loop operational control system for stabilizing and adaptively managing AI-enabled healthcare systems. Rather than treating patient and employee experience as downstream outcomes, the framework repositions them as governance input signals, operationalized as the Patient Experience Index (PXI) and Employee Experience Index (EXI). The architecture integrates Policy, Governance, Control, and Experience through recurrent feedback, trigger-control rules, and adaptive learning mechanisms that translate experiential signals into ongoing operational adjustment. To examine how this architecture affects system behavior, agent-based simulation compares four scenarios: S1 (no governance), S2 (sense-only), S3 (full HCEE closed loop), and S4 (full HCEE under exogenous stress). Results show a consistent pattern of S1 < S2 < S3 in both PXI and EXI, indicating that sensing alone yields limited improvement, whereas feedback-coupled sensing, control, and learning produce stronger stabilization and performance gains. In S4, the same architecture demonstrates recovery, re-stabilization, and adaptive reinforcement under shock. These findings suggest that effective healthcare AI governance depends on whether feedback loops are architected to function operationally. These results are based on synthetic experience indices and are interpreted as conceptual, mechanism-level evidence.

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