Improving comparative effectiveness research through electronic health records continuity cohorts

标题：通过电子健康记录改善比较有效性研究 连续性队列 PI：约书亚林，医学博士，公共卫生硕士 摘要（约30行） 卫生保健数据的流行病学分析可以提供关于有效性和安全性的关键证据， 由于临床试验经常排除体弱和老年患者， 大多数药物的主要消费者。电子健康记录（EHR）数据库包含丰富的临床信息， 这些信息对许多比较有效性研究至关重要，并越来越多地用于药物研究。 目前，美国有50多个基于EHR的研究网络。因此，关键是要了解 我们可以用EHR数据进行有效的临床比较研究。然而，除了少数高度集成的 计划，大多数美国EHR系统没有全面捕获整个护理过程中的医疗事件 连续体，并且可能错过大量的信息。暴露、合并症和健康结局 在给定EHR系统之外的诊所或医院记录的信息对调查者是“不可见的”， 越来越多的错误分类或基本变量的完全遗漏。虽然这些问题普遍存在，但 先前的研究曾经量化了由此产生的偏差的大小，以及如果与 更多的信息是不可行的。为了解决这一知识差距，我们结合了纵向索赔数据， 将来自大型多中心医疗保健系统的EHR患者数据作为“黄金标准”设置 其中索赔数据全面捕获跨护理设置和提供者系统的医疗信息 EHR提供必要的临床数据。我们将（1）使用这些“黄金标准”数据来确定“电子健康记录的连续性 EHR系统捕获所有遭遇的高比例并评估是否 在比较有效性研究中错误分类/遗漏了一系列基本变量， 与EHR连续性队列外相比，EHR连续性队列内的风险大幅降低;（2）制定识别EHR的策略 连续性队列的基础上，一套代理指标，可在典型的电子健康档案数据库，并验证 候选预测规则内部地在给定EHR内的样本中并且外部地使用第二EHR 系统，也与医疗保险索赔数据;（3）评估EHR中的研究有效性和普遍性 在几项实证研究中的连续性队列;以及（4）就如何进行 在EHR系统中使用高有效性EHR连续性队列进行比较有效性研究， 链接的索赔数据，并使我们的计划公开，以促进未来的研究使用EHR为基础的 研究网络。

Title: Improving comparative effectiveness research through electronic health records continuity cohorts PI: Joshua Lin, MD, MPH Abstract (about 30 lines) Epidemiologic analyses of health care data can provide critical evidence on the effectiveness and safety of therapeutics in the routine care setting since clinical trials often exclude frail and older patients who are the primary consumers of most medications. Electronic health record (EHR) databases contain rich clinical information vital for many comparative effectiveness studies and have been increasingly used for drug research. There are currently more than 50 EHR-based research networks in the US. It is thus critical to understand how we can conduct valid comparative clinical studies with EHR data. However, other than few highly integrated plans, most US EHR systems do not have comprehensive capture of medical encounters across the care continuum and may miss substantial amounts of information. Exposures, co-morbidities, and health outcomes that are recorded at a clinic or hospital outside of a given EHR system are "invisible" to the investigator, increasing misclassification or complete omission of essential variables. While such issues are pervasive, no prior study has ever quantified the magnitude of resultant bias and how to remedy the situation if linkage of more information is not feasible. To address this knowledge gap, we have combined longitudinal claims data from Medicare with EHR patient data from a large multi-center health care system as a `gold standard' setup where the claims data comprehensively capture medical information across care settings and provider systems and EHR provides necessary clinical data. We will (1) use these `gold standard' data to identify `EHR continuity cohorts' for whom the EHR system captures a high proportion of all encounters and evaluate whether misclassification/omission of a list of essential variables in the comparative effectiveness research is substantially reduced within vs outside of the EHR continuity cohort; (2) develop strategies to identify the EHR continuity cohort based on a set of proxy indicators available in typical EHR databases and validate the candidate prediction rules internally in a sample within the given EHR and externally using a second EHR system that is also linked to Medicare claims data; (3) assess research validity and generalizability in the EHR continuity cohorts in several empirical studies; and (4) Develop structured recommendation on how to conduct comparative effectiveness research using high-validity EHR continuity cohorts in an EHR system without linked claims data and make our program public available to facilitate future research using EHR-based research networks.