Regulation, Privacy & Deployment
Monitoring Clinical AI
Drift is a clinical event
Three clocks run against your model: data drift (new scanner, new EHR build, new patient mix), practice drift (guidelines change the treatment → the outcome your label encoded), and pipeline drift (a field stops populating after an EHR update — silent feature death).
Operationally: monitor input distributions, calibration-in-production, and outcome-linked metrics — and alert on pipeline completeness first, because a dead upstream feed looks like 'missing data', not an outage. In regulated land, every drift response is also a PCCP question: is this update inside your pre-approved change plan?
warning
The most common production failure in hospital ML is a quiet upstream schema change, not model decay. Alert on the plumbing before you alert on the predictions.