  Algorithmic bias in healthcare mirrors existing data biases. However, the
factors driving unfairness are not always known. Medical devices capture
significant amounts of data but are prone to errors; for instance, pulse
oximeters overestimate the arterial oxygen saturation of darker-skinned
individuals, leading to worse outcomes. The impact of this bias in machine
learning (ML) models remains unclear. This study addresses the technical
challenges of quantifying the impact of medical device bias in downstream ML.
Our experiments compare a "perfect world", without pulse oximetry bias, using
SaO2 (blood-gas), to the "actual world", with biased measurements, using SpO2
(pulse oximetry). Under this counterfactual design, two models are trained with
identical data, features, and settings, except for the method of measuring
oxygen saturation: models using SaO2 are a "control" and models using SpO2 a
"treatment". The blood-gas oximetry linked dataset was a suitable test-bed,
containing 163,396 nearly-simultaneous SpO2 - SaO2 paired measurements, aligned
with a wide array of clinical features and outcomes. We studied three
classification tasks: in-hospital mortality, respiratory SOFA score in the next
24 hours, and SOFA score increase by two points. Models using SaO2 instead of
SpO2 generally showed better performance. Patients with overestimation of O2 by
pulse oximetry of > 3% had significant decreases in mortality prediction
recall, from 0.63 to 0.59, P < 0.001. This mirrors clinical processes where
biased pulse oximetry readings provide clinicians with false reassurance of
patients' oxygen levels. A similar degradation happened in ML models, with
pulse oximetry biases leading to more false negatives in predicting adverse
outcomes.
