  Objective: Social determinants of health (SDOH) impact health outcomes and
are documented in the electronic health record (EHR) through structured data
and unstructured clinical notes. However, clinical notes often contain more
comprehensive SDOH information, detailing aspects such as status, severity, and
temporality. This work has two primary objectives: i) develop a natural
language processing (NLP) information extraction model to capture detailed SDOH
information and ii) evaluate the information gain achieved by applying the SDOH
extractor to clinical narratives and combining the extracted representations
with existing structured data.
  Materials and Methods: We developed a novel SDOH extractor using a deep
learning entity and relation extraction architecture to characterize SDOH
across various dimensions. In an EHR case study, we applied the SDOH extractor
to a large clinical data set with 225,089 patients and 430,406 notes with
social history sections and compared the extracted SDOH information with
existing structured data.
  Results: The SDOH extractor achieved 0.86 F1 on a withheld test set. In the
EHR case study, we found extracted SDOH information complements existing
structured data with 32% of homeless patients, 19% of current tobacco users,
and 10% of drug users only having these health risk factors documented in the
clinical narrative.
  Conclusions: Utilizing EHR data to identify SDOH health risk factors and
social needs may improve patient care and outcomes. Semantic representations of
text-encoded SDOH information can augment existing structured data, and this
more comprehensive SDOH representation can assist health systems in identifying
and addressing these social needs.
