This article details the development of a Natural Language Processing (NLP) pipeline aimed at extracting information related to social support (SS) and social isolation (SI) from clinical notes leveraging electronic health record (EHR) data from two sites—MSHS and WCM. The study utilized a large dataset of psychiatric encounters, focusing on consistent documentation styles and comprehensive patient evaluations. The results demonstrated the efficacy of the NLP system in identifying key social metrics, underscoring the potential for these tools to improve patient care through deeper insights into social factors affecting health.
We developed a Natural Language Processing (NLP) pipeline to extract social support and social isolation information from clinical notes using electronic health record (EHR) data.
Our research utilized EHR data from two institutions, allowing us to analyze a comprehensive range of psychiatric encounters over multiple years.
The final system performance indicates effective extraction of social support and isolation metrics, emphasizing the potential of NLP in enhancing clinical documentation.
This study highlights the importance of systematically addressing social factors in health care using advanced computational methods.
#natural-language-processing #social-support #social-isolation #electronic-health-records #psychiatric-care
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