Post-Deployment Monitoring & Re-training Controls
GMLP Principle 10. Monitor deployed performance and govern re-training with controls that catch drift, bias, and degradation before they hurt patients.
Overview
Post-deployment monitoring is where many AI medical devices quietly accumulate failure modes. Drift creeps in, subgroup performance erodes, and re-training cycles introduce subtle behavioral changes that surprise clinicians.
We design post-market monitoring and re-training controls that catch issues early and govern updates without breaking the device's regulatory standing.
Our Process
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1
Monitoring instrumentation
What signals do we capture from deployments?
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2
Drift detection
Input distribution shift, output distribution shift, performance drift.
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3
Subgroup tracking
Continuous subgroup-level performance monitoring.
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4
Re-training controls
PCCP-aligned governance for model updates.
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5
Real-world performance reporting
Periodic reporting to QMS and regulators.
Frequently Asked Questions
How does this fit with PCCP?
Directly, monitoring outputs inform PCCP-permitted modifications.
What about post-market surveillance under EU MDR?
Coordinated with PMS plan and PSUR cadence.
Can we automate retraining safely?
With proper controls, yes. Without, emphatically no.
FDA expectations?
Real-world performance monitoring is increasingly expected in submission and post-market.
Govern the device after launch.
Send us your deployed footprint and current monitoring. We will return a post-deployment plan within four weeks.
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