Improving comparative effectiveness research through electronic health records continuity cohorts

通过电子健康记录连续性队列改进比较有效性研究

• 批准号: 9983157
• 负责人: JOSHUA K LIN
• 金额: $ 31.95万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9983157
• 关键词: Address; Algorithms; Caring; Classification; Clinical; Clinical Data; Clinical Research; Clinical Trials; Clinics and Hospitals; Comparative Effectiveness Research; Continuity of Patient Care; Data; Databases; Drug usage; Effectiveness; Electronic Health Record; Epidemiology; Extravasation; Gold; Growth; Health; Healthcare; Healthcare Systems; Information Systems; Knowledge; Link; Medical; Medicare; Medicare claim; Methods; Outcome; Pharmaceutical Preparations; Population Study; Provider; Proxy; Recommendation; Records; Research; Research Personnel; Safety; Sampling; Source; Structure; System; Therapeutic; Work; base; care providers; care systems; cohort; comorbidity; comparative; comparative effectiveness study; data standards; improved; medication safety; older patient; patient health information; programs; randomized trial; routine care; study population; treatment effect

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.

标题：通过电子健康记录提高比较有效性研究 连续性队列 PI：Joshua Lin，医学博士，MPH 摘要（大约30行） 医疗保健数据的流行病学分析可以提供有关有效性和安全性的关键证据 在常规护理环境中的治疗剂，因为临床试验通常排除脆弱的患者，而老年患者则是 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 信息对于许多比较有效性研究至关重要，并且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，了解如何 我们可以使用EHR数据进行有效的比较临床研究。但是，除了少数高度集成 计划，大多数美国EHR系统没有全面捕获整个护理中的医疗遭遇 连续体，可能会错过大量信息。暴露，合并症和健康成果 在给定EHR系统以外的诊所或医院记录的研究人员“看不见” 增加错误分类或完全省略基本变量。虽然这些问题无处不在，但没有 先前的研究曾经量化了由此产生的偏差的幅度以及如何解决情况 更多信息是不可行的。为了解决这个知识差距，我们结合了纵向索赔数据 从大型多中心医疗保健系统的Medicare和EHR患者数据作为“黄金标准”设置 索赔数据在跨护理设置和提供商系统中全面捕获医疗信息的位置 EHR提供必要的临床数据。我们将（1）使用这些“黄金标准”数据来识别EHR连续性 EHR系统捕获了所有相遇的很大比例，并评估是否评估是否 在比较有效性研究中，错误分类/省略基本变量列表是 在EHR连续性队列外的VS内大大降低了； （2）制定识别EHR的策略 基于典型EHR数据库中可用的一组代理指标的连续性队列并验证 在给定的EHR内的样本中内部的候选预测规则，并在外部使用第二个EHR 也链接到Medicare索赔数据的系统； （3）评估EHR的研究有效性和概括性 在几项实证研究中的连续性队列； （4）开发有关如何进行的结构化建议 使用高效率EHR连续性在没有EHR系统中的比较有效性研究 链接索赔数据，并使我们的计划公开以促进未来的研究使用基于EHR的研究 研究网络。

项目成果

Longitudinal Data Discontinuity in Electronic Health Records and Consequences for Medication Effectiveness Studies.

• DOI: 10.1002/cpt.2400
• 发表时间: 2022-01
• 期刊: Clinical pharmacology and therapeutics
• 影响因子: 6.7
• 作者: Joshua Lin K;Jin Y;Gagne J;Glynn RJ;Murphy SN;Tong A;Schneeweiss S
• 通讯作者: Schneeweiss S

An algorithm to predict data completeness in oncology electronic medical records for comparative effectiveness research.

一种预测肿瘤电子病历数据完整性的算法，用于比较有效性研究。

• DOI: 10.1016/j.annepidem.2022.07.007
• 发表时间: 2022
• 期刊: Annals of epidemiology
• 影响因子: 5.6
• 作者: Merola,David;Schneeweiss,Sebastian;Schrag,Deborah;Lii,Joyce;Lin,KueiyuJoshua
• 通讯作者: Lin,KueiyuJoshua