FEATURESNatural Language Processing of Nursing Notes An Integrative ReviewMitha, Shazia MPhil, MSN, AGACNP-BC, RN; Schwartz, Jessica PhD, RN; Hobensack, Mollie BSN, RN; Cato, Kenrick PhD, RN, CPHIMS, FAAN; Woo, Kyungmi PhD, RN, CCM; Smaldone, Arlene PhD, CPNP-PC, CDE, FAAN; Topaz, Maxim PhD, RN Author Information Author Affiliations: Columbia University School of Nursing, New York. The authors have disclosed that they have no significant relationship with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Maxim Topaz, PhD, RN, Columbia University School of Nursing, 560 W 168th St, New York, NY 10032 ([email protected]). Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.cinjournal.com). CIN: Computers, Informatics, Nursing 41(6):p 377-384, June 2023. | DOI: 10.1097/CIN.0000000000000967 Buy SDC CME Test Metrics Abstract Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in nursing. We searched PubMed, EMBASE, and CINAHL and found 689 studies, narrowed to 43 eligible studies using natural language processing in nursing notes. Data related to the study purpose, patient population, methodology, performance evaluation metrics, and quality indicators were extracted for each study. The majority (86%) of the studies were conducted from 2015 to 2021. Most of the studies (58%) used inpatient data. One of four studies used data from open-source databases. The most common standard terminologies used were the Unified Medical Language System and Systematized Nomenclature of Medicine, whereas nursing-specific standard terminologies were used only in eight studies. Full system performance metrics (eg, F score) were reported for 61% of applicable studies. The overall number of nursing natural language processing publications remains relatively small compared with the other medical literature. Future studies should evaluate and report appropriate performance metrics and use existing standard nursing terminologies to enable future scalability of the methods and findings. Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.