Human-Centered Experience Engineering for Healthcare AI Governance: A SER-M Model Approach
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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Details
| Section | Articles |
| Issue | Vol. 2 No. 2 (2026): Volume 2 Issue 2 (Jun 2026) |
| Published | 2026-06-18 |
| Pages | 48-61 |
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