Determination of Marital Status of Patients from Structured and Unstructured Electronic Healthcare Data

Social Determinants of Health, including marital status, are becoming increasingly identified as key drivers of health care utilization. This paper describes a robust method to determine the marital status of patients using structured and unstructured electronic healthcare data from a single academic institution in the United States. We developed and validated a natural language processing pipeline (NLP) for the ascertainment of marital status from clinical notes and compared the performance against two baseline methods: a machine learning n-gram model, and structured data obtained from the electronic health record. Overall our NLP engine had excellent performance on both document-level (F1 0.97) and patient-level (F1 0.95) classification. The NLP Engine had superior performance compared with a baseline machine learning n-gram model. We also observed a good correlation between the marital status obtained from our NLP engine and the baseline structured electronic healthcare data (κ 0.6).