Natural Language Processing of Nursing Notes

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.

Advancing Health Equity Through Artificial Intelligence: An Educational Framework for Preparing Nurses in Clinical Practice and Research

“…NLP uses computational techniques that understand and interact with human language to power technologies like chatbots and digital assistants (Brown, 2021). In nursing, NLP can extract insights from nursing notes that aid in de-identifying personal health information in clinical notes (Abu-El-Rub et al, 2022;Mitha et al, 2023). The integration of AI-powered chatbots into patient care routines is revolutionizing health care by leveraging NLP to mimic human interaction and conversation (Surve et al, 2023).…”

“…DL networks analyze large datasets and identify and weigh various connections within the data (Brown, 2021). DL's capabilities extend across medical fields and surpass human experts in disease detection and classification in some areas, marking a significant advancement in medical diagnostics and patient care (Poalelungi et al, 2023).…”