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Psychosocial factors are known to have adverse health impacts, but are rarely measured; using natural language processing, we extracted factors that identified a higher risk segment of older adults with multimorbidity. We find these extracted features are highly predictive of future emergency department visits and hospitalizations, although only marginal prediction gains are seen compared to other models without these factors. Combining these extraction techniques with other measures of social determinants may help catalyze population health efforts to mitigate these health impacts. [ABSTRACT FROM AUTHOR]

Social and behavioral factors influence health but are infrequently recorded in electronic health records (EHRs). Here, we demonstrate that psychosocial vital signs can be extracted from EHR data. We processed structured and unstructured EHR data using expert-driven queries and Natural Language Processing (NLP), validating results through structured annotation. We found that although these vital signs are present in EHRs, with 681 structured entries identified for psychosocial concepts, NLP identified a nearly 90-fold increase in patients [ABSTRACT FROM AUTHOR]