Natural Language Processing for Identification of Hospitalized People Who Use Drugs: Cohort Study - Taisuke Sato; Emily D Grussing; Ruchi Patel; Jessica Ridgway; Joji Suzuki; Benjamin Sweigart; Robert Miller; Alysse G Wurcel (JMIR AI)
Abstract BackgroundPeople who use drugs (PWUD) are at heightened risk of severe injection–related infections. Current research relies on billing codes to identify PWUD—a methodology with suboptimal accuracy that may underestimate the economic, racial, and ethnic diversity of hospitalized PWUD. ObjectiveThe goal of this study is to examine the impact of natural language processing (NLP) on enhancing identification of PWUD in electronic medical records, with a specific focus on determining improved systems of identifying populations who may previously been missed, including people who have low income or those from racially and ethnically minoritized populations. MethodsHealth informatics specialists assisted in querying a cohort of likely PWUD hospital admissions at Tufts Medical Center between 2020‐2022 using the following criteria: (1) ICD-10 ResultsThe cohort included 4548 hospitalization admissions, with broad heterogeneity in how people entered the cohort and subcohorts; a total of 288 hospital admissions entered the cohort through NLP token presence alone. NLP demonstrated a 54% positive predictive value, outperforming biomarkers, prescription for medications for opioid use disorder, and ICD ConclusionsNLP proved effective in identifying hospitalizations of PWUD, surpassing traditional methods. While further refinement is needed, NLP shows promising potential in minimizing health care disparities.