A Natural Language Processing Pipeline to Study Disparities in Cannabis Use and Documentation Among Children and Young Adults A Survey of 21 Years of Electronic Health Records

Abstract

AbstractThe legalizations of medical and recreational cannabis have generated a great deal of interest in studying the health impacts of cannabis products. Despite increases in cannabis use, its documentation during clinical visits is not yet mainstream. This lack of information hampers efforts to study cannabis effects on health outcomes. A clear and in-depth understanding of current trends in cannabis use documentation is necessary to develop proper guidelines to screen and document cannabis use. Here we have developed and used a hierarchical natural language processing pipeline (AUROC=0.94) to evaluate the trends and disparities in cannabis documentation on more than 23 million notes from a large cohort of 370,087 patients seen in a high-volume multi-site pediatric and young adult clinic over a period of 21 years. Our findings show a very low but growing rate of cannabis use documentation (<2%) in electronic health records with significant demographic and socioeconomic disparities in both documentation and use, which requires further attention.