Description
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).
Learning Objective:
After participating in this session, the learner should be better able to:
1. Understand the current methodology for collection of a patient's marital status in the electronic health record
2. Learn the value of natural language processing in the acertainment of marital status from clinical notes.
Authors:
Brian Bucher (Presenter)
University of Utah School of Medicine
Jianlin Shi, University of Utah School of Medicine
Robert Pettit, University of Utah School of Medicine
Jeffrey Ferraro, Intermountain Healthcare
Wendy Chapman, University of Utah School of Medicine
Adi Gundlapalli, University of Utah School of Medicine
Presentation Materials: