Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data

• Published in: Applied clinical informatics 2020-01, Vol.11 (1), p.172-181
• Main Authors: Feller, Daniel J., Bear Don't Walk IV, Oliver J., Zucker, Jason, Yin, Michael T., Gordon, Peter, Elhadad, Noémie
• Format: Article
• Language: English
• Subjects: Documentation; Electronic Health Records; Humans; Research Article; Risk Assessment - methods; Risk Factors; Social Behavior; Supervised Machine Learning; Text Messaging
• ISSN: 1869-0327; 1869-0327
• DOI: 10.1055/s-0040-1702214

Abstract Background  Social and behavioral determinants of health (SBDH) are environmental and behavioral factors that often impede disease management and result in sexually transmitted infections. Despite their importance, SBDH are inconsistently documented in electronic health records (EHRs) and typically collected only in an unstructured format. Evidence suggests that structured data elements present in EHRs can contribute further to identify SBDH in the patient record. Objective  Explore the automated inference of both the presence of SBDH documentation and individual SBDH risk factors in patient records. Compare the relative ability of clinical notes and structured EHR data, such as laboratory measurements and diagnoses, to support inference. Methods  We attempt to infer the presence of SBDH documentation in patient records, as well as patient status of 11 SBDH, including alcohol abuse, homelessness, and sexual orientation. We compare classification performance when considering clinical notes only, structured data only, and notes and structured data together. We perform an error analysis across several SBDH risk factors. Results  Classification models inferring the presence of SBDH documentation achieved good performance (F1 score: 92.7–78.7; F1 considered as the primary evaluation metric). Performance was variable for models inferring patient SBDH risk status; results ranged from F1 = 82.7 for LGBT (lesbian, gay, bisexual, and transgender) status to F1 = 28.5 for intravenous drug use. Error analysis demonstrated that lexical diversity and documentation of historical SBDH status challenge inference of patient SBDH status. Three of five classifiers inferring topic-specific SBDH documentation and 10 of 11 patient SBDH status classifiers achieved highest performance when trained using both clinical notes and structured data. Conclusion  Our findings suggest that combining clinical free-text notes and structured data provide the best approach in classifying patient SBDH status. Inferring patient SBDH status is most challenging among SBDH with low prevalence and high lexical diversity.