End-to-End Evaluation and Governance of an EHR-Embedded AI Agent for Clinicians
Clinical AI systems require not just point-in-time evaluation but continuous governance: the ongoing practice of monitoring, evaluating, iterating, and re-evaluating performance throughout deployment. We present an end-to-end framework of governance that integrates rubric validation, live deployment feedback, technical performance monitoring, and cost tracking, with controlled experimentation gating system changes before deployment. Applied to Hyperscribe, an EHR-embedded agent that converts ambient audio into structured chart updates, twenty clinicians authored 1,646 validated rubrics across 823 cases. Seven Hyperscribe versions were evaluated through controlled experiments, with median ...
| Authors | Aaryan Shah, Andrew Hines, Alexia Downs et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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