A Natural Language Processing Approach to Identify Negative Patient Descriptors in Electronic Health Records for Maternal Care.

ABSTRACT: Maternal harm, especially for Black women, is a significant health care issue. Unstructured clinical notes in electronic health records (EHRs) may reveal unsafe maternal care. Prior studies using natural language processing (NLP) have shown that tone and sentiment in notes contribute to preventable safety events.This study aimed to examine whether negative patient descriptors in EHR clinical notes are associated with adverse maternal outcomes and how their use varies by patient demographics.We conducted a retrospective cohort study of women who delivered at two large birthing hospitals in Washington, DC between January 1, 2016 and March 31, 2020. Using a predefined list of negative keywords (e.g., combative) and NLP, we identified sentences from clinical notes for manual review. Two subjec

SUBMITTER: Tabaie A 

PROVIDER: S-EPMC12566921 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature