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

Leveraging Natural Language Processing to Augment Structured Social Determinants of Health Data in the Electronic Health Record