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首页> 外文期刊>BMC Medical Informatics and Decision Making >Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction
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Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction

机译：通过静音脑梗塞的案例研究评估EHR异质性对临床研究的影响

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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. 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. 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. 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.
机译：通过基于实践的知识发现，电子健康记录（EHRS）的快速采用对推进医学具有很大的承诺。然而，基于EHR的临床研究的有效性是由于由医疗机构和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流程和最佳实践而导致的研究重现差，以及进行EHR的临床研究。我们开发了一种数据抽象框架，以规范基于多站点的EHR的临床研究进程，旨在提高研究重现性。该框架是为一个基于多网站EHR的研究项目，浓缩咖啡项目，目标是识别塔夫茨医疗中心（TMC）和Mayo诊所的静音脑梗塞（SBI）的个体。在定量和定性地评估医疗保健机构，EHR系统，文档和过程变化的异质性。我们发现了两个站点患者患者群体，神经影像报告，EHR系统和抽象过程的显着变化。 TMC和MAYO超过50岁以上的SBI的患病率分别为7.4％和12.5％。有关神经影像报告的变化，TMC是冗长的，标准化和描述性的，而Mayo的报告是短而明确的，具有更短的变化。此外，确定了EHR系统，技术基础设施和数据收集过程的差异。该框架的实施确定了参与案例研究的地点ehrs的制度和过程变化和异质性。实验表明，在进行基于EHR的临床研究时，必须在进行数据抽象的规范化过程中的必要性。

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