Locking a Validated Performance Baseline
Placeholder: baseline accuracy is fixed at clearance, before any drift.
- 94.5%: Baseline Accuracy (validated at clearance)
- 92.1%: Baseline Sensitivity (locked reference value)
- 96.0%: Baseline Specificity (locked reference value)
- ±2.5 pp: Acceptable Margin (predefined drift tolerance)
Why a locked baseline matters
Placeholder line — baseline is the fixed yardstick every future measurement compares against.
Placeholder line — set once, during formal validation, not re-tuned after deployment.
Real-World Inputs Drift From Training Data
Placeholder: population, equipment, and workflow shifts change model inputs.
- Demographic mix: Population Shift (ages, comorbidities change)
- New scanner/sensor: Equipment Shift (different input signal)
- Changed clinical practice: Workflow Shift (new ordering patterns)
- 1–10 scale: Shift Rate Slider (slow-stable to fast-significant)
Sources of real-world drift
Placeholder line — covariate shift, label shift, and concept shift all degrade silently.
Placeholder line — none of these trigger an error; the model just gets quietly worse.
Tracking Live Accuracy Against Baseline
Placeholder: continuous surveillance compares live accuracy to baseline over time.
- Continuous: Monitoring Cadence (rolling performance window)
- Accuracy / Sens / Spec: Metric Tracked (vs locked baseline)
- Live-updating: Current Live Accuracy (see metrics panel)
- Statistical control chart: Detection Method (flags sustained deviation)
What ongoing monitoring looks for
Placeholder line — sustained deviation, not single noisy data points, triggers concern.
Placeholder line — dashboards plot live accuracy against the baseline reference line.
Performance Exits the Acceptable Margin
Placeholder: live accuracy falls below the predefined drift tolerance band.
- Live-updating: Drift Magnitude (baseline minus live, pp)
- ±2.5 pp: Warning Threshold (first alert boundary)
- ±6.0 pp: Critical Threshold (second alert boundary)
- Live-updating: Alert Status (normal / warning / critical)
Why thresholds are set in advance
Placeholder line — margins are predefined so alerts fire on evidence, not judgment calls.
Placeholder line — crossing the red zone is the trigger for mandatory action.
Placeholder highlight — silent decay past this line risks undetected patient harm.
Intervention Prevents Silent Decay
Placeholder: detection triggers retraining, recalibration, or takedown/resubmission.
- Model update: Retrain (on newer real-world data)
- Threshold adjustment: Recalibrate (without full retrain)
- Withdraw / resubmit: Takedown (if degradation is severe)
- Harm prevented: Outcome (via caught, addressed drift)
Closing the monitoring loop
Placeholder line — the chosen intervention restores performance toward baseline.
Placeholder line — monitoring then resumes against the same or an updated baseline.
Placeholder highlight — the point of monitoring is catching drift before harm, not after.