Real-World Data: The Benefits, Limitations, and Future

真实世界数据(RWD)被广泛定义为作为医疗保健交付的一部分而收集的数据，包括电子健康记录(EHRs)。, insurance claims databases, and disease-specific registries. Although these data sources were not designed with research in mind, 数据基础设施的进步使人们越来越有兴趣利用它们来回答新兴的研究问题. We asked 凯文·威尔逊, 博士学位, 领导和协调趣赢平台数据科学小组的趣赢平台副总监, for insight into the latest in RWD.

Q. 什么是RWD?

A. RWD are data derived from a multiple sources, 例如电子病历, insurance claims, disease registries, wearable devices, administrative records, product consumption, and social media. When these existing sources, which are collected for a specific purpose, are used for research, they are termed “real-world data.”

Q. What are the benefits of applying RWD to solve clients’ issues?

A. 使用RWD的一个重要好处是，它使我们能够更快、更便宜地访问大型数据集. 在调查回复率下降的时候，这一点尤其重要，因为我们不需要招募和采访参与者. As an example, RWD在应对新型冠状病毒肺炎前所未有的挑战方面非常有用，因为它们使公共卫生机构能够迅速了解病毒的发病率和严重程度，并迅速开发疫苗和药物来对抗它. In other areas, the U.S. Food and Drug Administration uses RWD to identify adverse reactions to drugs, which is critical to ensuring drug safety.

Q. 什么是RWD’s limitations?

A. There are a few challenges associated with the use of RWD. When we link the multiple sources of digital data together, 例如电子病历 and insurance claims, it can increase the risk of identifying someone, 特别是如果涉及到一个患有罕见疾病和独特人口统计学特征的人.

也有可能出现测量误差，因为数据源是否一致地测量概念并不总是很清楚. 例如, in health records, 医生可能会根据保险公司可能承保的范围对病人的就诊进行不同的编码, which can introduce subtle shifts in meaning.

另一个限制是数据可能不能代表一般人群，因为在使用电子病历时, 数据是从比一般人口稍微患病的人口和能够获得医疗保健的人口中收集的, excluding those who do not. 根据要回答的研究问题，这些限制可能导致偏差.

It’s also important to carefully assess the quality and completeness of the data. Data collected in health care facilities may not necessarily be comparable, and because the data are constantly evolving, they may lack reproducibility. 然而, we have procedures to assess the quality and potential for bias, and in general, the benefits outweigh the risks.

Q. How is 趣赢平台 using RWD to support clients?

A. We have a number of projects in which we are using RWD. These include REDS-IV-P, DAWN, and VISION. We harmonize the data so it can be transformed it into one cohesive dataset. 在REDS-IV-P, a study that links blood donor, component characteristics, and recipient outcomes, with a focus on pediatric populations, we conduct analyses related to transfusion medicine practices and outcomes. DAWN是一个全国性的公共卫生监测系统，旨在对新出现的药物趋势和与药物和/或酒精有关的急诊就诊特点提供早期预警和持续监测. RWD工作允许在全国范围内识别急诊科就诊的药物和药物组合. And for VISION, which leverages existing virtual networks, including the VISION flu network, we integrate massive amounts of data from 9 medical systems across the U.S. 来自这些系统的RWD通过安全的数据管道发送，趣赢平台管理质量检查并执行分析, enabling swift reporting to the CDC.

Q. How does 趣赢平台 stand apart from competitors in harnessing RWD?

A. 我们拥有集成来自多个数据源的数据的技术，以及将数据映射到公共数据模型的工具. We have exceptional statisticians, data scientists, 还有流行病学家，他们了解偏见的来源，可以使用加权和imputation等技术对数据进行修正. 此外，我们有对广泛的健康结果有深入了解的主题专家. So, 正是对如何利用RWD解决客户挑战的全面理解，使我们有别于竞争对手.

Q. What do you see as the future uses of RWD?

A. With the increased integration of data sources and availability of data, 我可以预见，RWD将被用于增进我们对人口和个人健康的了解, 这将使我们能够更精确地根据个别病人的需要定制医疗干预措施. The options for using RWD are manifold, 趣赢平台将继续利用我们多样化的资源来应对这些挑战. By bringing together skills in epidemiology, 统计数据, 数据科学, and informatics, and with a broad knowledge of health outcomes, 趣赢平台能够最大限度地提高RWD的效用和质量，以及它生成的真实世界证据.