In the AX era, the deployment of AI in healthcare organizations requires not only efficiency but also the simultaneous management of trust, workload, and operational stability. This study designs an experience-based closed-loop AI governance model that uses the Patient Experience Index (PXI) and the Employee Experience Index (EXI) as input signals, and validates—through simulation-based comparisons across governance levels—the resulting performance differences and the potential for post-shock recovery and re-stabilization. The analysis shows that the proposed model yields a consistent pattern of improvement in both operational performance and resilience.
목차
Abstract Introduction Background Research Design Scenario Design and Governance Logic Variables, Measures, and Trigger–Control Rules Simulation Execution and Reproducibility HCEE-Based Healthcare AI Governance Architecture Results Implications Conclusion References