Natural language processing-driven state machines to extract social factors from unstructured clinical documentation

Abstract

Lay Summary Social factors, such as an individual’s housing, food, employment, and income situations, affect their overall health and well-being. As a result, data on patients’ social factors aid in clinical decision making, planning by hospital administrators and policy-makers, and enrich research studies with data representative of more factors influencing the life of an individual. Data on social factors can be collected at the time of a healthcare visit through screening questionnaires or are often documented in the clinical text as part of the social narrative. This study examines the use of natural language processing—a machine method to identify certain text within a larger document—to identify housing instability, financial insecurity, and unemployment from within the clinical notes. Using a relatively unsophisticated methodology, this study demonstrates strong performance in identifying these social factors, which will enable stakeholders to utilize these details in support of improved clinical care.