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Monitoring Deployed Clinical AI Models for Drift and Bias
A health system has deployed several clinical decision-support models into live care pathways with no ongoing oversight after go-live.
Runs onRakshaHow the work runs
The pressure that made this worth automating, the steps the system runs, and what came out the other side.
Pressure & Trigger Points
- Model performance is validated once before deployment and never re-checked against live population data.
- Performance differences across patient demographics are not monitored, creating clinical and regulatory exposure.
- No governance body has visibility into which models are live and what they are used for.
The run · 5 operational steps
Click any step to inspect telemetry signals, model reasoning, and governance gates.
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Model Inventory
Every clinical model in production is registered with its purpose, owner, and care pathway.
Input Signal:
Real-time operational telemetry & queue
Reasoning Pattern:
MCP grounded vector inference
Governance Gate:
Policy constrained with audit write-back
Verified Business Outcomes
- Every production clinical model registered with an accountable owner.
- Performance monitored on live population data rather than only at validation.
- Demographic performance disparity visible as a monitored metric.
Capabilities this relies on
- policy compliance
- document intelligence
- risk scoring
- evidence audit trail
- alert routing
- in boundary deployment
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