Three illustrative changes
| Change | Example |
|---|---|
| Input distribution | A service begins seeing a different age mix |
| Outcome frequency | The target condition becomes more common |
| Input-outcome relationship | A new treatment changes what follows a risk marker |
Real deployments can involve several changes at once. Technical terms such as covariate shift and label shift also have formal assumptions, so the everyday examples should not substitute for a precise statistical model.
A distribution alert is a clue
An input shift may occur without meaningful performance loss. Performance may also worsen even when simple input summaries appear stable. A drift detector therefore identifies something to investigate rather than proving clinical harm or continued safety.
Trace the change to the intended use
Ask whether the eligible population, measurement process, care pathway, or outcome definition changed. Check performance where outcomes are available and examine groups that may be affected differently.
Do not retrain reflexively
Updating the model on newly collected data can preserve a broken labeling process or incorporate downstream effects of the model itself. Understand the source of the shift first. The appropriate response may be data repair, recalibration, restricted use, workflow revision, or a new evaluation rather than immediate replacement of the model.
