Governance is the continuous practice of monitoring, evaluating, iterating, and re-evaluating clinical AI systems throughout their operational lifecycle. It is distinct from point-in-time evaluation. It requires architectural choices made at design time, not compliance activities added after deployment.
We integrate four evaluation dimensions within a single continuous system. While governance frameworks have been proposed in the literature, no published work has demonstrated operational governance of a deployed clinical AI agent with empirical evidence.
Case-specific rubrics authored by expert clinicians encode what correct documentation should contain for each clinical encounter.
Real-use failure detection during deployment. Clinicians report issues through in-workflow mechanisms during live patient encounters.
Latency, failure rates, and reliability monitored through tiered logging architecture with per-stage attribution.
Economic sustainability of governance. Token attribution, compute costs, and clinician time tracked across every governance activity.
These dimensions are connected by controlled experimentation that gates every engineering change. Candidate system versions are tested against the full benchmark before deployment. No change ships without quantitative evidence.
Governability is an architectural property: the degree to which a system's design enables governance. These design choices, made during system development, determine whether governance is tractable.
Typed, schema-defined objects at every pipeline stage enable automated validation and cross-version comparison.
Inspectable reasoning layers enable failure attribution to specific pipeline stages.
Constrained to predefined clinical actions validated against the patient chart.
Case-specific rubrics produce quantitative scores enabling controlled version comparison.