通过自然语言处理管道评估全髋关节置换术结果并生成骨科研究结果数据库:开发和验证研究。

IF 3.1 3区 医学 Q2 MEDICAL INFORMATICS
Nicholas H Mast, Clara L Oeste, Dries Hens
{"title":"通过自然语言处理管道评估全髋关节置换术结果并生成骨科研究结果数据库:开发和验证研究。","authors":"Nicholas H Mast, Clara L Oeste, Dries Hens","doi":"10.2196/64705","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Processing data from electronic health records (EHRs) to build research-grade databases is a lengthy and expensive process. Modern arthroplasty practice commonly uses multiple sites of care, including clinics and ambulatory care centers. However, most private data systems prevent obtaining usable insights for clinical practice.</p><p><strong>Objective: </strong>This study aims to create an automated natural language processing (NLP) pipeline for extracting clinical concepts from EHRs related to orthopedic outpatient visits, hospitalizations, and surgeries in a multicenter, single-surgeon practice. The pipeline was also used to assess therapies and complications after total hip arthroplasty (THA).</p><p><strong>Methods: </strong>EHRs of 1290 patients undergoing primary THA from January 1, 2012 to December 31, 2019 (operated and followed by the same surgeon) were processed using artificial intelligence (AI)-based models (NLP and machine learning). In addition, 3 independent medical reviewers generated a gold standard using 100 randomly selected EHRs. The algorithm processed the entire database from different EHR systems, generating an aggregated clinical data warehouse. An additional manual control arm was used for data quality control.</p><p><strong>Results: </strong>The algorithm was as accurate as human reviewers (0.95 vs 0.94; P=.01), achieving a database-wide average F1-score of 0.92 (SD 0.09; range 0.67-0.99), validating its use as an automated data extraction tool. During the first year after direct anterior THA, 92.1% (1188/1290) of our population had a complication-free recovery. In 7.9% (102/1290) of cases where surgery or recovery was not uneventful, lateral femoral cutaneous nerve sensitivity (47/1290, 3.6%), intraoperative fractures (13/1290, 1%), and hematoma (9/1290, 0.7%) were the most common complications.</p><p><strong>Conclusions: </strong>Algorithm evaluation of this dataset accurately represented key clinical information swiftly, compared with human reviewers. This technology may provide substantial value for future surgeon practice and patient counseling. Furthermore, the low early complication rate of direct anterior THA in this surgeon's hands was supported by the dataset, which included data from all treated patients in a multicenter practice.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e64705"},"PeriodicalIF":3.1000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11922490/pdf/","citationCount":"0","resultStr":"{\"title\":\"Assessing Total Hip Arthroplasty Outcomes and Generating an Orthopedic Research Outcome Database via a Natural Language Processing Pipeline: Development and Validation Study.\",\"authors\":\"Nicholas H Mast, Clara L Oeste, Dries Hens\",\"doi\":\"10.2196/64705\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Processing data from electronic health records (EHRs) to build research-grade databases is a lengthy and expensive process. Modern arthroplasty practice commonly uses multiple sites of care, including clinics and ambulatory care centers. However, most private data systems prevent obtaining usable insights for clinical practice.</p><p><strong>Objective: </strong>This study aims to create an automated natural language processing (NLP) pipeline for extracting clinical concepts from EHRs related to orthopedic outpatient visits, hospitalizations, and surgeries in a multicenter, single-surgeon practice. The pipeline was also used to assess therapies and complications after total hip arthroplasty (THA).</p><p><strong>Methods: </strong>EHRs of 1290 patients undergoing primary THA from January 1, 2012 to December 31, 2019 (operated and followed by the same surgeon) were processed using artificial intelligence (AI)-based models (NLP and machine learning). In addition, 3 independent medical reviewers generated a gold standard using 100 randomly selected EHRs. The algorithm processed the entire database from different EHR systems, generating an aggregated clinical data warehouse. An additional manual control arm was used for data quality control.</p><p><strong>Results: </strong>The algorithm was as accurate as human reviewers (0.95 vs 0.94; P=.01), achieving a database-wide average F1-score of 0.92 (SD 0.09; range 0.67-0.99), validating its use as an automated data extraction tool. During the first year after direct anterior THA, 92.1% (1188/1290) of our population had a complication-free recovery. In 7.9% (102/1290) of cases where surgery or recovery was not uneventful, lateral femoral cutaneous nerve sensitivity (47/1290, 3.6%), intraoperative fractures (13/1290, 1%), and hematoma (9/1290, 0.7%) were the most common complications.</p><p><strong>Conclusions: </strong>Algorithm evaluation of this dataset accurately represented key clinical information swiftly, compared with human reviewers. This technology may provide substantial value for future surgeon practice and patient counseling. Furthermore, the low early complication rate of direct anterior THA in this surgeon's hands was supported by the dataset, which included data from all treated patients in a multicenter practice.</p>\",\"PeriodicalId\":56334,\"journal\":{\"name\":\"JMIR Medical Informatics\",\"volume\":\"13 \",\"pages\":\"e64705\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11922490/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR Medical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2196/64705\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/64705","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0

摘要

背景:从电子健康记录(EHRs)中处理数据以建立研究级数据库是一个漫长而昂贵的过程。现代关节成形术通常使用多个护理地点,包括诊所和门诊护理中心。然而,大多数私人数据系统阻止获得临床实践可用的见解。目的:本研究旨在创建一个自动自然语言处理(NLP)管道,用于从多中心、单外科医生实践中与骨科门诊、住院和手术相关的电子病历中提取临床概念。该管道也用于评估全髋关节置换术(THA)后的治疗和并发症。方法:采用基于人工智能(AI)的模型(NLP和机器学习)对2012年1月1日至2019年12月31日1290例由同一外科医生手术和随访的原发性THA患者的电子病历进行处理。此外,3名独立医学审稿人使用随机选择的100份电子病历生成了一个金标准。该算法处理来自不同EHR系统的整个数据库,生成一个聚合的临床数据仓库。另外一个手动控制臂用于数据质量控制。结果:该算法与人工审稿人准确率相当(0.95 vs 0.94;P= 0.01),整个数据库的平均f1评分为0.92 (SD 0.09;范围0.67-0.99),验证其作为自动数据提取工具的使用。在直接前路THA术后的第一年,92.1%(1188/1290)的患者无并发症恢复。在7.9%(102/1290)手术或恢复不顺利的病例中,股骨外侧皮神经敏感(47/1290,3.6%)、术中骨折(13/1290,1%)和血肿(9/1290,0.7%)是最常见的并发症。结论:与人工审稿人相比,该数据集的算法评估能够快速准确地反映关键临床信息。这项技术可能为未来的外科医生实践和患者咨询提供实质性的价值。此外,该数据集包括来自多中心实践中所有接受治疗的患者的数据,该数据集支持该外科医生手部直接前路THA的早期并发症发生率低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing Total Hip Arthroplasty Outcomes and Generating an Orthopedic Research Outcome Database via a Natural Language Processing Pipeline: Development and Validation Study.

Background: Processing data from electronic health records (EHRs) to build research-grade databases is a lengthy and expensive process. Modern arthroplasty practice commonly uses multiple sites of care, including clinics and ambulatory care centers. However, most private data systems prevent obtaining usable insights for clinical practice.

Objective: This study aims to create an automated natural language processing (NLP) pipeline for extracting clinical concepts from EHRs related to orthopedic outpatient visits, hospitalizations, and surgeries in a multicenter, single-surgeon practice. The pipeline was also used to assess therapies and complications after total hip arthroplasty (THA).

Methods: EHRs of 1290 patients undergoing primary THA from January 1, 2012 to December 31, 2019 (operated and followed by the same surgeon) were processed using artificial intelligence (AI)-based models (NLP and machine learning). In addition, 3 independent medical reviewers generated a gold standard using 100 randomly selected EHRs. The algorithm processed the entire database from different EHR systems, generating an aggregated clinical data warehouse. An additional manual control arm was used for data quality control.

Results: The algorithm was as accurate as human reviewers (0.95 vs 0.94; P=.01), achieving a database-wide average F1-score of 0.92 (SD 0.09; range 0.67-0.99), validating its use as an automated data extraction tool. During the first year after direct anterior THA, 92.1% (1188/1290) of our population had a complication-free recovery. In 7.9% (102/1290) of cases where surgery or recovery was not uneventful, lateral femoral cutaneous nerve sensitivity (47/1290, 3.6%), intraoperative fractures (13/1290, 1%), and hematoma (9/1290, 0.7%) were the most common complications.

Conclusions: Algorithm evaluation of this dataset accurately represented key clinical information swiftly, compared with human reviewers. This technology may provide substantial value for future surgeon practice and patient counseling. Furthermore, the low early complication rate of direct anterior THA in this surgeon's hands was supported by the dataset, which included data from all treated patients in a multicenter practice.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
JMIR Medical Informatics
JMIR Medical Informatics Medicine-Health Informatics
CiteScore
7.90
自引率
3.10%
发文量
173
审稿时长
12 weeks
期刊介绍: JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信