学习共享卫生保健数据:美国卫生保健研究中有影响力的公共数据模型和分布式卫生数据网络的简要时间表。

John Weeks, Roy Pardee
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引用次数: 51

摘要

在过去的二十年中,医疗保健研究已经看到了用于多组织研究的常见医疗保健数据模型的稳定流。每个模型都为来自不同组织的数据提供统一的接口,从而实现研究工具和数据的共享。虽然设计模型的小组有不同的需求和目标,并且在此期间可用的数据也发生了重大变化,但它们之间仍然存在惊人的相似之处。本文追溯了常用数据模型的演变,描述了它们的相似之处和出发点。我们认为,这项工作的历史应该得到理解和保护。这项工作为竞争组织之间的合作研究提供了动力,并将来自全球临床实践、大学和研究机构的研究人员聚集在一起。理解为合作研究而设计的数据模型的生态系统,可以让读者评估我们作为一个领域已经取得的成就,以及我们将走向的方向,并评估不同模型对他们自己工作的效用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Learning to Share Health Care Data: A Brief Timeline of Influential Common Data Models and Distributed Health Data Networks in U.S. Health Care Research.

Learning to Share Health Care Data: A Brief Timeline of Influential Common Data Models and Distributed Health Data Networks in U.S. Health Care Research.

Learning to Share Health Care Data: A Brief Timeline of Influential Common Data Models and Distributed Health Data Networks in U.S. Health Care Research.

Learning to Share Health Care Data: A Brief Timeline of Influential Common Data Models and Distributed Health Data Networks in U.S. Health Care Research.

The last twenty years of health care research has seen a steady stream of common health care data models implemented for multi-organization research. Each model offers a uniform interface on data from the diverse organizations that implement them, enabling the sharing of research tools and data. While the groups designing the models have had various needs and aims, and the data available has changed significantly in this time, there are nevertheless striking similarities between them. This paper traces the evolution of common data models, describing their similarities and points of departure. We believe the history of this work should be understood and preserved. The work has empowered collaborative research across competing organizations and brought together researchers from clinical practice, universities and research institutes around the planet. Understanding the eco-system of data models designed for collaborative research allows readers to evaluate where we have been, where we are going as a field, and to evaluate the utility of different models to their own work.

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