Integrating structured and unstructured EHR data to characterize social and behavioral factors associated with frequent emergency department use among patients with cardiovascular disease

Objective: The goal was to integrate structured and unstructured data from the electronic health record (EHR) to identify social and behavioral determinants of health (SBDH) factors and evaluate their association with high emergency department (ED) utilization among cardiovascular disease (CVD) patients. Methods: A custom natural language processing (NLP) algorithm was developed to extract 17 SBDH domains from clinical notes, combined with ICD-10 Z codes and screening questionnaires from the EHR. The study included patients aged 18-64 with a history of CVD. Multivariable logistic regression was used to identify associations between SBDH and high ED utilization ([≥] 5 visits) after adjusting for age, sex, race, insurance class, and chronic conditions. Results: Use of only ICD-10 codes and screening questionnaires identified patients in only a subset of the 17 SBDH domains, whereas extraction from clinical notes identified patients across all domains and increased prevalence estimates for every domain. Among 4,844 patients with CVD, 526 (10.9%) were frequent ED users. After adjustment, patients with inadequate support system (OR= 2.8), opioid abuse (OR=2.47), inadequate insurance (OR=2.42), alcohol abuse (OR=2.28), medication affordability concerns (OR=2.28), unreliable transportation (OR=1.82), financial strain (OR=1.69), and depression (OR=1.42) were significantly more likely to have frequent ED utilization. Conclusion: NLP can efficiently and more comprehensively identify socioeconomic risk factors from the EHR. Substance use, financial strain, inadequate social support, and other socioeconomic challenges may be linked to increased ED visits in patients with CVD.