Equity and Bias in CTCAE Documentation and Automation

Introduction: Bias Does Not Start With Algorithms

Automation often gets blamed for bias, but the roots usually lie in documentation practices. CTCAE grading reflects what is documented, how it is documented, and whose experiences are captured accurately.

Ignoring this reality risks amplifying existing inequities.

Where Bias Enters the System

Language differences, cultural norms, and access barriers influence how symptoms are reported and recorded. Some patients underreport, some clinicians probe less deeply, and some symptoms are normalized rather than escalated.

These patterns become embedded in data.

Automation as a Mirror

When automation surfaces fewer adverse events in certain populations, the correct response is not denial. It is investigation. Is documentation less detailed? Are PROs underutilized? Are thresholds misaligned?

Automation can make inequities visible, which is a prerequisite for addressing them.

Designing With Equity in Mind

Equity-aware systems monitor performance across demographic groups, compare clinician and patient-reported data, and flag systematic discrepancies. Governance structures must explicitly include equity review.

Fairness is not automatic. It is designed, measured, and maintained.

Moving From Awareness to Action

CTCAE automation should be held to a higher standard than manual workflows, not a lower one. Transparency creates the opportunity to improve safety equity rather than silently perpetuate bias.