Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction
Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction
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DOI：
10.1186/s12911-020-1072-9
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发表时间：
2020-03-30
影响因子：
3.5
通讯作者：
Liu, Hongfang
中科院分区：
文献类型：
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作者：
Fu, Sunyang;Leung, Lester Y.;Liu, Hongfang
电子健康记录（EHR）的快速采用为通过基于实践的知识发现推进医学带来了巨大的希望。然而，基于EHR的临床研究的有效性是值得怀疑的，因为医疗机构和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来进行基于EHR的临床研究。方法我们开发了一个数据抽象框架，以标准化的过程中，多地点EHR为基础的临床研究，旨在提高研究的可重复性。该框架是为一个多站点的EHR为基础的研究项目，ESPARIO项目，目标是确定个人与无症状脑梗死（SBI）在塔夫茨医学中心（TMC）和马约诊所。对医疗机构、EHR系统、文档和病例识别过程差异的异质性进行了定量和定性评估。结果我们发现两个站点的患者人群、神经影像报告、EHR系统和抽象过程存在显著差异。TMC和马约的50岁以上患者的SBI患病率分别为7.4%和12.5%。关于神经影像学报告存在差异，其中TMC是冗长的、标准化的和描述性的，而马约的报告是简短的和明确的，具有更多的文本差异。此外，EHR系统，技术基础设施和数据收集过程中的差异被确定。结论该框架的实施确定了参与案例研究的各地点的机构和流程差异以及EHR的异质性。实验表明，进行基于EHR的临床研究时，有必要有一个标准化的数据提取过程。
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.