Using Human Override Data to Improve Healthcare AI

Human overrides are an important source of operational evidence in clinical AI systems. However, an override count alone provides limited insight into whether the model, interface or workflow needs improvement.

Capture the right context

For each relevant override, record the AI recommendation, the action taken, the timestamp, the model or agent version and the reason for disagreement when available. Link subsequent outcomes where appropriate and lawful.

Avoid collecting unnecessary sensitive information. Access to override records should follow appropriate privacy and governance controls.

Distinguish different failure modes

A clinician may override an AI system because the model missed important information, its threshold was inappropriate, the data was outdated or the recommendation did not fit the clinical context. Other causes include alert fatigue, interface problems and workflow constraints.

These categories should not be collapsed into a single measure of model quality.

Analyse patterns, not isolated events

Review overrides across clinical settings, decision types and relevant patient groups. Combine quantitative analysis with clinical review to identify recurring problems. A high override rate can signal a problem, but a low rate does not automatically demonstrate safety.

Avoid learning from overrides blindly

An override should not automatically become a new training label. Clinicians can disagree for different reasons, and some overrides may themselves be inappropriate. Any use of override data for model retraining requires suitable validation, governance and evaluation.

Preserve effective human control

For agentic systems, authorised users should be able to interrupt actions, redirect workflows and revoke permissions. Log the intervention and the resulting system state so that safety teams can reconstruct what happened.

The goal is not to minimise every disagreement. It is to understand which disagreements reveal limitations and which represent appropriate clinical judgement.

Engineering principle: Treat human overrides as signals for investigation, not automatic ground truth.

If you are building clinical AI, include override analysis in your monitoring and post-deployment evaluation plan.