当前位置: X-MOL 学术BMC Med. Inform. Decis. Mak. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction.
BMC Medical Informatics and Decision Making ( IF 3.3 ) Pub Date : 2020-03-30 , DOI: 10.1186/s12911-020-1072-9
Sunyang Fu 1 , Lester Y Leung 2 , Anne-Olivia Raulli 2 , David F Kallmes 3 , Kristin A Kinsman 3 , Kristoff B Nelson 2 , Michael S Clark 3 , Patrick H Luetmer 3 , Paul R Kingsbury 1 , David M Kent 4 , Hongfang Liu 1
Affiliation  

BACKGROUND The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery. However, the validity of EHR-based clinical research is questionable due to poor research reproducibility caused by the heterogeneity and complexity of healthcare institutions and EHR systems, the cross-disciplinary nature of the research team, and the lack of standard processes and best practices for conducting EHR-based clinical research. METHOD We developed a data abstraction framework to standardize the process for multi-site EHR-based clinical studies aiming to enhance research reproducibility. The framework was implemented for a multi-site EHR-based research project, the ESPRESSO project, with the goal to identify individuals with silent brain infarctions (SBI) at Tufts Medical Center (TMC) and Mayo Clinic. The heterogeneity of healthcare institutions, EHR systems, documentation, and process variation in case identification was assessed quantitatively and qualitatively. RESULT We discovered a significant variation in the patient populations, neuroimaging reporting, EHR systems, and abstraction processes across the two sites. The prevalence of SBI for patients over age 50 for TMC and Mayo is 7.4 and 12.5% respectively. There is a variation regarding neuroimaging reporting where TMC are lengthy, standardized and descriptive while Mayo's reports are short and definitive with more textual variations. Furthermore, differences in the EHR system, technology infrastructure, and data collection process were identified. CONCLUSION The implementation of the framework identified the institutional and process variations and the heterogeneity of EHRs across the sites participating in the case study. The experiment demonstrates the necessity to have a standardized process for data abstraction when conducting EHR-based clinical studies.

中文翻译:

通过无声脑梗死的案例研究评估EHR异质性对临床研究的影响。

背景技术电子健康记录(EHR)的快速采用对于通过基于实践的知识发现来推进医学具有广阔的前景。但是,由于医疗机构和EHR系统的异质性和复杂性,研究团队的跨学科性质以及缺乏标准的流程和最佳实践,导致基于EHR的临床研究的有效性令人怀疑。进行基于EHR的临床研究。方法我们开发了一个数据抽象框架,以标准化基于EHR的多站点临床研究的过程,旨在提高研究的可重复性。该框架是为基于EHR的多站点研究项目ESPRESSO项目,目的是在塔夫茨医疗中心(TMC)和梅奥诊所(Mayo Clinic)中识别出无脑梗死(SBI)的人。定量和定性评估了医疗机构,EHR系统,文档和案例识别过程差异的异质性。结果我们发现两个站点的患者群体,神经影像报告,EHR系统和提取过程存在显着差异。对于50岁以上的TMC和Mayo患者,SBI的患病率分别为7.4和12.5%。关于神经影像报告,存在多种变化,其中TMC冗长,标准化且具有描述性,而Mayo的报告则简短且确定性强,具有更多的文字变化。此外,还确定了EHR系统,技术基础结构和数据收集过程之间的差异。结论该框架的实施确定了参与案例研究的各站点之间EHR的制度和过程的差异以及异质性。该实验表明,在进行基于EHR的临床研究时,必须具有标准化的数据抽象过程。
更新日期:2020-04-22
down
wechat
bug