Zhihong Zhang PhD, RN , Jihye Kim Scroggins PhD, RN , Sarah Harkins BSN, RN , Ismael Ibrahim Hulchafo MD, MS , Hans Moen PhD , Michele Tadiello MS , Veronica Barcelona PhD, RN , Maxim Topaz PhD, RN
{"title":"迈向公平的文件:评估ChatGPT在识别和改写电子健康记录中的污名化语言方面的作用","authors":"Zhihong Zhang PhD, RN , Jihye Kim Scroggins PhD, RN , Sarah Harkins BSN, RN , Ismael Ibrahim Hulchafo MD, MS , Hans Moen PhD , Michele Tadiello MS , Veronica Barcelona PhD, RN , Maxim Topaz PhD, RN","doi":"10.1016/j.outlook.2025.102472","DOIUrl":null,"url":null,"abstract":"<div><div>Stigmatizing language in electronic health records (EHRs) harms clinician and patient relationships, reinforcing health disparities. To assess ChatGPT’s ability to reduce stigmatizing language in clinical notes. We analyzed 140 clinical notes and 150 stigmatizing examples from 2 urban hospitals. ChatGPT-4 identified and rephrased stigmatizing language. Identification performance was evaluated using precision, recall, and F1 score, with human expert annotations as the gold standard. Rephrasing quality was rated by experts on a three-point Likert scale for de-stigmatization, faithfulness, conciseness, and clarity. ChatGPT showed poor overall identification (micro-F1 = 0.51) but moderate-to-high performance across individual stigmatizing language categories (micro-F1 = 0.69–0.91). Rephrasing scored 2.7 for de-stigmatization, 2.8 for faithfulness, and 3.0 for conciseness and clarity. Prompt design significantly affected ChatGPT’s performance. While ChatGPT has limitations in automatic identification, it can be used to support real-time identification and rephrasing stigmatizing language in EHRs with appropriate prompt design and human oversight.</div></div>","PeriodicalId":54705,"journal":{"name":"Nursing Outlook","volume":"73 4","pages":"Article 102472"},"PeriodicalIF":3.7000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Toward equitable documentation: Evaluating ChatGPT’s role in identifying and rephrasing stigmatizing language in electronic health records\",\"authors\":\"Zhihong Zhang PhD, RN , Jihye Kim Scroggins PhD, RN , Sarah Harkins BSN, RN , Ismael Ibrahim Hulchafo MD, MS , Hans Moen PhD , Michele Tadiello MS , Veronica Barcelona PhD, RN , Maxim Topaz PhD, RN\",\"doi\":\"10.1016/j.outlook.2025.102472\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Stigmatizing language in electronic health records (EHRs) harms clinician and patient relationships, reinforcing health disparities. To assess ChatGPT’s ability to reduce stigmatizing language in clinical notes. We analyzed 140 clinical notes and 150 stigmatizing examples from 2 urban hospitals. ChatGPT-4 identified and rephrased stigmatizing language. Identification performance was evaluated using precision, recall, and F1 score, with human expert annotations as the gold standard. Rephrasing quality was rated by experts on a three-point Likert scale for de-stigmatization, faithfulness, conciseness, and clarity. ChatGPT showed poor overall identification (micro-F1 = 0.51) but moderate-to-high performance across individual stigmatizing language categories (micro-F1 = 0.69–0.91). Rephrasing scored 2.7 for de-stigmatization, 2.8 for faithfulness, and 3.0 for conciseness and clarity. Prompt design significantly affected ChatGPT’s performance. While ChatGPT has limitations in automatic identification, it can be used to support real-time identification and rephrasing stigmatizing language in EHRs with appropriate prompt design and human oversight.</div></div>\",\"PeriodicalId\":54705,\"journal\":{\"name\":\"Nursing Outlook\",\"volume\":\"73 4\",\"pages\":\"Article 102472\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nursing Outlook\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0029655425001253\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nursing Outlook","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0029655425001253","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
Toward equitable documentation: Evaluating ChatGPT’s role in identifying and rephrasing stigmatizing language in electronic health records
Stigmatizing language in electronic health records (EHRs) harms clinician and patient relationships, reinforcing health disparities. To assess ChatGPT’s ability to reduce stigmatizing language in clinical notes. We analyzed 140 clinical notes and 150 stigmatizing examples from 2 urban hospitals. ChatGPT-4 identified and rephrased stigmatizing language. Identification performance was evaluated using precision, recall, and F1 score, with human expert annotations as the gold standard. Rephrasing quality was rated by experts on a three-point Likert scale for de-stigmatization, faithfulness, conciseness, and clarity. ChatGPT showed poor overall identification (micro-F1 = 0.51) but moderate-to-high performance across individual stigmatizing language categories (micro-F1 = 0.69–0.91). Rephrasing scored 2.7 for de-stigmatization, 2.8 for faithfulness, and 3.0 for conciseness and clarity. Prompt design significantly affected ChatGPT’s performance. While ChatGPT has limitations in automatic identification, it can be used to support real-time identification and rephrasing stigmatizing language in EHRs with appropriate prompt design and human oversight.
期刊介绍:
Nursing Outlook, a bimonthly journal, provides innovative ideas for nursing leaders through peer-reviewed articles and timely reports. Each issue examines current issues and trends in nursing practice, education, and research, offering progressive solutions to the challenges facing the profession. Nursing Outlook is the official journal of the American Academy of Nursing and the Council for the Advancement of Nursing Science and supports their mission to serve the public and the nursing profession by advancing health policy and practice through the generation, synthesis, and dissemination of nursing knowledge. The journal is included in MEDLINE, CINAHL and the Journal Citation Reports published by Clarivate Analytics.