  We aimed to investigate the impact of social circumstances on cancer therapy
selection using natural language processing to derive insights from social
worker documentation. We developed and employed a Bidirectional Encoder
Representations from Transformers (BERT) based approach, using a hierarchical
multi-step BERT model (BERT-MS) to predict the prescription of targeted cancer
therapy to patients based solely on documentation by clinical social workers.
Our corpus included free-text clinical social work notes, combined with
medication prescription information, for all patients treated for breast
cancer. We conducted a feature importance analysis to pinpoint the specific
social circumstances that impact cancer therapy selection. Using only social
work notes, we consistently predicted the administration of targeted therapies,
suggesting systematic differences in treatment selection exist due to
non-clinical factors. The UCSF-BERT model, pretrained on clinical text at UCSF,
outperformed other publicly available language models with an AUROC of 0.675
and a Macro F1 score of 0.599. The UCSF BERT-MS model, capable of leveraging
multiple pieces of notes, surpassed the UCSF-BERT model in both AUROC and
Macro-F1. Our feature importance analysis identified several clinically
intuitive social determinants of health (SDOH) that potentially contribute to
disparities in treatment. Our findings indicate that significant disparities
exist among breast cancer patients receiving different types of therapies based
on social determinants of health. Social work reports play a crucial role in
understanding these disparities in clinical decision-making.
