When Data Lies: A Nurse’s Guide to Recognizing and Reducing Bias in Clinical Data

Healthcare rests on data. Every diagnosis, every treatment plan, every predictive algorithm relies on what has been recorded before. But data is not neutral. It carries the fingerprints of history, culture, and human error. When bias enters, patients pay the price.

Nurses are uniquely positioned to catch it. We sit at the intersection of people and systems, translating human experience into structured data and interpreting structured data back into care. That means we also hold the power to challenge when “the numbers” do not tell the full truth.

What Bias Looks Like in Healthcare Data

Bias takes many forms:

• Sampling bias: when certain groups are under-represented. For example, a clinical trial that enrolls mostly white men will not generate results that apply well to Black women.
• Measurement bias: when tools or definitions skew results. Pulse oximeters, for instance, have historically read higher oxygen levels in darker skin tones, masking hypoxemia.
• Historical bias: when inequities of the past are baked into the dataset. If decades of unequal treatment produced worse outcomes for a community, an algorithm trained on that data will assume worse outcomes…