Enhance Arthroplasty Research through Electronic Health Records and Nlp-Enabled Informatics

通过电子健康记录和支持 NLP 的信息学加强关节置换术研究

• 批准号: 10358647
• 负责人: Hilal Maradit Kremers
• 金额: $ 56.35万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10358647
• 关键词: Adoption; Algorithms; American; Bioinformatics; Clinic; Clinical; Clinical Data; Clinical Trials; Complex; Complication; Data; Data Collection; Data Element; Data Set; Data Sources; Decision Making; Development; Devices; Documentation; Electronic Health Record; Epidemic; Evidence based practice; Future; Goals; Gold; Guide prevention; Health Benefit; Hospitals; Individual; Informatics; Institution; Intervention; Joint Prosthesis; Knowledge; Logistics; Manuals; Marketing; Medicare; Methods; Modeling; Monitor; Natural Language Processing; Observational Study; Operative Surgical Procedures; Patient-Focused Outcomes; Patients; Performance; Policies; Postoperative Period; Prevention; Prevention strategy; Procedures; Provider; Publishing; Registries; Replacement Arthroplasty; Research; Risk; Risk Factors; Safety; Scientific Advances and Accomplishments; Source; Structure; Techniques; Technology; Testing; Text; Time; United States; age group; base; computerized data processing; cost; data access; data resource; electronic data; electronic structure; epidemiology study; evidence base; health care quality; health information technology; high risk; improved; individual patient; infection risk; informatics infrastructure; informatics tool; innovation; joint infection; modifiable risk; novel; open source; outcome prediction; patient population; portability; pragmatic trial; predictive modeling; prototype; public health relevance; risk prediction; risk prediction model; structured data; surgery outcome; tool; willingness

ABSTRACT Total joint arthroplasty (TJA) is the most common and fastest growing surgical procedure in the nation. Despite the high procedure volume, the evidence base for TJA procedures and associated interventions are limited. This is mainly due to lack of high quality data sources and the logistical difficulties associated with manually extracting TJA information from the unstructured text of the Electronic Health Records (EHR). Meanwhile, the rapid adoption of EHR and the advances in health information technology offer the potential to transform unstructured EHR notes into structured, codified format that can then be analyzed and shared with local and national arthroplasty registries and other agencies. We therefore propose to leverage unique data resources and natural language processing (NLP) technologies to build an informatics infrastructure for automated EHR data extraction and analysis. We will (1) develop a high performance, externally validated and user centric NLP- enabled algorithm for extraction of complex TJA-specific data elements from the structured and unstructured text of the EHR, (2) validate the algorithm externally in multiple EHR platforms and hospital settings, and (3) conduct a demonstration project focused on prediction of prosthetic joint infections using data elements collected by the NLP-enabled algorithm. Our overarching goal is to develop valid, open source and portable NLP-enabled data collection and risk prediction tools and disseminate them widely to hospitals participating in regional and national TJA registries. This research is significant as it leverages strong data resources and expertise to tackle the pressing need for high quality data and accurate prediction models in TJA. Automated data collection and processing capabilities will lead to an upsurge in secondary use of EHR to advance scientific knowledge on TJA risk factors, healthcare quality and patient outcomes. Accurate prediction of high risk patients for prosthetic joint infections will guide prevention and treatment decisions resulting in significant health benefits to TJA patients. The research is innovative because TJA-specific bioinformatics technology will shift TJA research from current under-powered, single-center studies to large, multi-center registry-based observational studies and clinical trials. Our deliverables have the potential to exert a sustained downstream effect on future TJA research, practice and policy.

摘要 全关节置换术(TJA)是最常见、发展最快的外科手术。 国家。尽管程序量很大，但TJA程序和 相关干预措施是有限的。这主要是由于缺乏高质量的数据源和 与手动提取TJA信息相关的后勤困难 电子健康记录(EHR)的非结构化文本。与此同时，电子病历的快速采用 医疗信息技术的进步提供了转变非结构化的潜力 将电子病历记录转换为结构化、编码化的格式，然后可进行分析并与本地和 国家关节置换术登记处和其他机构。 因此，我们建议利用独特的数据资源和自然语言处理 (NLP)技术，以构建用于自动电子病历数据提取和 分析。我们将(1)开发高性能、外部验证和以用户为中心的NLP- 使能的算法，用于从结构化的和 EHR的非结构化文本，(2)在多个EHR平台上外部验证算法，并 医院环境，以及(3)开展以假体预测为重点的示范项目 使用启用NLP的算法收集的数据元素进行联合感染。我们最重要的是 目标是开发有效、开源和可移植的、支持NLP的数据收集和风险 预测工具，并将其广泛传播给区域和国家参与的医院 TJA登记处。 这项研究具有重要意义，因为它利用强大的数据资源和专业知识来解决 TJA迫切需要高质量的数据和准确的预测模型。自动化数据 收集和处理能力将导致EHR的二次使用热潮 增进有关TJA风险因素、医疗质量和患者预后的科学知识。 准确预测人工关节感染的高危患者将指导预防和治疗 治疗决策给TJA患者带来了显著的健康益处。这项研究是 创新是因为TJA特定的生物信息学技术将改变TJA的研究 动力不足的单中心研究到基于多中心注册的大型观察性研究 和临床试验。我们的交付成果有可能对以下方面产生持续的下游影响 未来TJA的研究、实践和政策。

项目成果

Use of Natural Language Processing Algorithms to Identify Common Data Elements in Operative Notes for Knee Arthroplasty.

• DOI: 10.1016/j.arth.2020.09.029
• 发表时间: 2021-03
• 期刊: The Journal of arthroplasty
• 作者: Sagheb E;Ramazanian T;Tafti AP;Fu S;Kremers WK;Berry DJ;Lewallen DG;Sohn S;Maradit Kremers H
• 通讯作者: Maradit Kremers H

A Deep Learning Tool for Automated Radiographic Measurement of Acetabular Component Inclination and Version After Total Hip Arthroplasty.

全髋关节置换术后髋臼组件倾斜度和版本的自动放射学测量的深度学习工具。

• DOI: 10.1016/j.arth.2021.02.026
• 发表时间: 2021-07
• 期刊: The Journal of arthroplasty
• 作者: Rouzrokh P;Wyles CC;Philbrick KA;Ramazanian T;Weston AD;Cai JC;Taunton MJ;Lewallen DG;Berry DJ;Erickson BJ;Maradit Kremers H
• 通讯作者: Maradit Kremers H

Deep learning model for measurement of shoulder critical angle and acromion index on shoulder radiographs.

• DOI: 10.1016/j.xrrt.2022.03.002
• 发表时间: 2022-08
• 期刊: JSES reviews, reports, and techniques
• 作者: Shariatnia, M Moein;Ramazanian, Taghi;Sanchez-Sotelo, Joaquin;Maradit Kremers, Hilal
• 通讯作者: Maradit Kremers, Hilal

Neighborhood-Level Socioeconomic Deprivation, Rurality, and Long-Term Outcomes of Patients Undergoing Total Joint Arthroplasty: Analysis from a Large, Tertiary Care Hospital.

• DOI: 10.1016/j.mayocpiqo.2022.06.001
• 发表时间: 2022-08
• 期刊: Mayo Clinic proceedings. Innovations, quality & outcomes
• 作者: Kamath, Celia C;O'Byrne, Thomas J;Lewallen, David G;Berry, Daniel J;Maradit Kremers, Hilal
• 通讯作者: Maradit Kremers, Hilal

Frank Stinchfield Award: Creation of a Patient-Specific Total Hip Arthroplasty Periprosthetic Fracture Risk Calculator.

Frank Stinchfield 奖：创建针对患者的全髋关节置换术假体周围骨折风险计算器。

• DOI: 10.1016/j.arth.2023.03.031
• 发表时间: 2023
• 期刊: The Journal of arthroplasty
• 作者: Wyles,CodyC;Maradit-Kremers,Hilal;Fruth,KristinM;Larson,DirkR;Khosravi,Bardia;Rouzrokh,Pouria;Johnson,QuinnJ;Berry,DanielJ;Sierra,RafaelJ;Taunton,MichaelJ;Abdel,MatthewP
• 通讯作者: Abdel,MatthewP