Rebecca Friedman, Rebecca Lisk, Katherine Cordero-Bermudez, Soniya Singh, Sofia Ghani, Brian M Gillette, Scott A Gorenstein, Ernest S Chiu
{"title":"Optimizing Chart Review Efficiency in Pressure Injury Evaluation Using ChatGPT.","authors":"Rebecca Friedman, Rebecca Lisk, Katherine Cordero-Bermudez, Soniya Singh, Sofia Ghani, Brian M Gillette, Scott A Gorenstein, Ernest S Chiu","doi":"10.1097/SAP.0000000000004275","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Wound care is an essential discipline in plastic surgery, especially as the prevalence of chronic wounds, such as pressure injuries, is increasing. The escalating volume of patient data and the numerous variables influencing wound outcomes are making traditional manual chart reviews in wound care and research increasingly complex and burdensome. The emergence of Natural Language Processing (NLP) software based on large language models (LLMs) such as ChatGPT presents an opportunity to automate the data extraction process. This study harnesses the capabilities of ChatGPT, hosted by our medical center's secure, private Azure OpenAI service, to automatically extract and process variables from patient charts following sacral wound visits. We assess ChatGPT's potential to revolutionize chart review through improved data retrieval accuracy and efficiency.</p><p><strong>Methods: </strong>We evaluated the use of the medical center's internal ChatGPT in chart review. ChatGPT and a Python script were integrated into the existing chart review process for patients with sacral wounds from 2 hospital cohorts to extract and format variables related to wound care. Metrics include time taken for review, accuracy of extracted information, and assessment of ChatGPT-generated insights.</p><p><strong>Results: </strong>ChatGPT reduced the average time per chart review from 7.56 minutes with the manual method to 1.03 minutes using ChatGPT. Furthermore, it achieved a 0.957 overall accuracy rate compared to manual chart review, ranging from 0.747 to 0.986 across extracted data elements. ChatGPT was also able to synthesize accurate narrative descriptions of patient wounds.</p><p><strong>Conclusions: </strong>We highlight ChatGPT's potential to enhance speed and precision of chart review in the context of both clinical care and wound care research, offering valuable implications for integration of artificial intelligence in healthcare workflows.</p>","PeriodicalId":8060,"journal":{"name":"Annals of Plastic Surgery","volume":"94 4S Suppl 2","pages":"S327-S333"},"PeriodicalIF":1.4000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Plastic Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/SAP.0000000000004275","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SURGERY","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Wound care is an essential discipline in plastic surgery, especially as the prevalence of chronic wounds, such as pressure injuries, is increasing. The escalating volume of patient data and the numerous variables influencing wound outcomes are making traditional manual chart reviews in wound care and research increasingly complex and burdensome. The emergence of Natural Language Processing (NLP) software based on large language models (LLMs) such as ChatGPT presents an opportunity to automate the data extraction process. This study harnesses the capabilities of ChatGPT, hosted by our medical center's secure, private Azure OpenAI service, to automatically extract and process variables from patient charts following sacral wound visits. We assess ChatGPT's potential to revolutionize chart review through improved data retrieval accuracy and efficiency.
Methods: We evaluated the use of the medical center's internal ChatGPT in chart review. ChatGPT and a Python script were integrated into the existing chart review process for patients with sacral wounds from 2 hospital cohorts to extract and format variables related to wound care. Metrics include time taken for review, accuracy of extracted information, and assessment of ChatGPT-generated insights.
Results: ChatGPT reduced the average time per chart review from 7.56 minutes with the manual method to 1.03 minutes using ChatGPT. Furthermore, it achieved a 0.957 overall accuracy rate compared to manual chart review, ranging from 0.747 to 0.986 across extracted data elements. ChatGPT was also able to synthesize accurate narrative descriptions of patient wounds.
Conclusions: We highlight ChatGPT's potential to enhance speed and precision of chart review in the context of both clinical care and wound care research, offering valuable implications for integration of artificial intelligence in healthcare workflows.
期刊介绍：
The only independent journal devoted to general plastic and reconstructive surgery, Annals of Plastic Surgery serves as a forum for current scientific and clinical advances in the field and a sounding board for ideas and perspectives on its future. The journal publishes peer-reviewed original articles, brief communications, case reports, and notes in all areas of interest to the practicing plastic surgeon. There are also historical and current reviews, descriptions of surgical technique, and lively editorials and letters to the editor.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
