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Human-Centered Experience Engineering for Healthcare AI Governance: A SER-M Model Approach

Minseong Kim Primary Contact
Abstract

The rapid adoption of artificial intelligence (AI) in healthcare has intensified the "principle-practice gap," where existing governance frameworks provide ethical principles but lack actionable implementation guidance. This study establishes the concept of Human-Centered Experience Engineering (HCEE), systematizes it through the SER-M (Subject-Environment-Resource-Mechanism) model, and proposes an HCEE-based Healthcare AI Governance Architecture. Applying Design Science Research (DSR) methodology, we developed a four-layer governance architecture and validated it through agent-based simulation. The architecture integrates Patient Experience Index (PXI) and Employee Experience Index (EXI) as core governance variables, with trigger-control rules that adjust AI automation levels based on experience thresholds. Simulation results show that full HCEE implementation improves PXI by 34.5% and EXI by 43.6%. This study makes three contributions to the MIS field. First, it establishes HCEE, reconceptualizing human experience as a governance input variable and extending Human-Centered AI discourse to the operational level. Second, it applies the SER-M model to Healthcare AI Governance, extending the Mechanism-Based View to IS. Third, it demonstrates that AI governance can be implemented as a designable IS artifact through trigger-control rules.

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Keywords
Healthcare AI Governance Human-Centered Experience Engineering SER-M Model Design Science Research Agent-Based Simulation
Details

Authors
Minseong Kim Primary Contact

  • Ph.D. Student, Graduate School of AI, Seoul School of Integrated Sciences & Technologies (aSSIST), Administrative Director, Pogny Hospital
  • Research Interests: Healthcare AI Governance, Human-Centered Experience Engineering, Hospital Digital Transformation, Generative AI, Data-Driven Hospital Management
How to Cite
Human-Centered Experience Engineering for Healthcare AI Governance: A SER-M Model Approach. (2026). AI Journal of BUsiness, 2(2), 48-61. https://jnl.ampla.page/aijb/article/view/130