Improving comparative effectiveness research through electronic health records continuity cohorts

标题：通过电子健康记录改进比较有效性研究 连续性队列 少年派：Joshua Lin，医学博士，公共卫生硕士 摘要(约30行) 对卫生保健数据的流行病学分析可以为以下方面提供关键证据： 常规护理环境中的治疗方法，因为临床试验通常排除虚弱和年长的患者 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 信息对许多比较有效性研究至关重要，并已越来越多地被用于药物研究。 目前，美国有50多个基于EHR的研究网络。因此，关键是要了解如何 我们可以用EHR数据进行有效的临床对比研究。然而，除了少数几个高度集成之外 计划，大多数美国电子病历系统没有全面捕获整个护理过程中的医疗遭遇 并且可能会遗漏大量信息。暴露、合并症和健康后果 在给定的EHR系统之外的诊所或医院记录的数据对于调查者来说是“不可见的”， 不断增加的基本变量的错误分类或完全遗漏。虽然这样的问题无处不在，但没有 先前的研究曾经量化过由此产生的偏见的大小，以及如何补救这种情况，如果 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 来自大型多中心医疗保健系统的联邦医疗保险和电子病历患者数据，以此作为“黄金标准”设置 其中索赔数据全面地捕获跨护理设置和提供者系统的医疗信息 而EHR提供了必要的临床数据。我们将(1)使用这些“黄金标准”数据来确定“EHR连续性” EHR系统捕获所有接触的很高比例的人群，并评估 比较有效性研究中基本变量清单的错误分类/遗漏是 (2)制定确定电子健康记录连续性队列的策略 基于典型EHR数据库中提供的一组代理指标的连续性队列，并验证 在给定EHR内的样本中的内部候选预测规则和外部使用第二EHR 也与联邦医疗保险索赔数据相关联的系统；(3)评估研究的有效性和在电子病历中的普适性 在几个实证研究中的连续性队列；以及(4)就如何进行 高效度EHR连续性队列在无EHR系统中的有效性比较研究 链接索赔数据，并公开我们的计划，以促进未来使用基于电子病历的研究 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.