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.
Run on Drishti · Regulatory & Compliance
The challenge
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.
How Drishti solves it
Model Inventory
Every clinical model in production is registered with its purpose, owner, and care pathway.
Live Performance Monitoring
Model output is monitored against outcomes on the live population, not the validation set.
Subgroup Analysis
Performance is broken down across demographic subgroups so disparity surfaces as a monitored metric.
Policy Enforcement
Use of each model is constrained to its approved indication, with deviations flagged.
Governance Reporting
Findings are reported into the clinical governance body on a standing cadence.
The outcome
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.
Representative scenario: a modelled industry situation showing how this accelerator applies. Figures are projections, not a named client result.
Capabilities used
Every accelerator is assembled from the same capability layer, so a workflow you adopt here shares its foundation with the rest of the portfolio.
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