Adaptation of an NLP system to a new healthcare environment to identify social determinants of health

• Published in: Journal of biomedical informatics 2021-08, Vol.120, p.103851-103851, Article 103851
• Main Authors: Reeves, Ruth M., Christensen, Lee, Brown, Jeremiah R., Conway, Michael, Levis, Maxwell, Gobbel, Glenn T., Shah, Rashmee U., Goodrich, Christine, Ricket, Iben, Minter, Freneka, Bohm, Andrew, Bray, Bruce E., Matheny, Michael E., Chapman, Wendy
• Format: Article
• Language: English
• Subjects: Academic Medical Centers; Cohort Studies; Delivery of Health Care; Electronic Health Records; Humans; Social Determinants of Health
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2021.103851

[Display omitted] •SoDH can affect access to care, quality of life, health outcomes & life expectancy.•Currently, SDoH are not collected in a systematic way that would allow analyses.•The Moonstone NLP tool has been successfully adapted from VA EHRs to Vanderbilt EHR.•Moonstone extracts 8 SoDH from clinical notes: prec. 0.83 recall 0.74, F-measure 0.78.•Moonstone’s graphical interface aids testing the effects of semantic interactions. Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort. We developed a transformation of the SDoH outputs of the tool into the OMOP common data model (CDM) for re-use across many potential use cases, yielding performance measures across 8 SDoH classes of precision 0.83 recall 0.74 and F-measure of 0.78.