  Medical systems in general, and patient treatment decisions and outcomes in
particular, are affected by bias based on gender and other demographic
elements. As language models are increasingly applied to medicine, there is a
growing interest in building algorithmic fairness into processes impacting
patient care. Much of the work addressing this question has focused on biases
encoded in language models -- statistical estimates of the relationships
between concepts derived from distant reading of corpora. Building on this
work, we investigate how word choices made by healthcare practitioners and
language models interact with regards to bias. We identify and remove gendered
language from two clinical-note datasets and describe a new debiasing procedure
using BERT-based gender classifiers. We show minimal degradation in health
condition classification tasks for low- to medium-levels of bias removal via
data augmentation. Finally, we compare the bias semantically encoded in the
language models with the bias empirically observed in health records. This work
outlines an interpretable approach for using data augmentation to identify and
reduce the potential for bias in natural language processing pipelines.
