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在学术医疗中心建立数据科学部门:一个说明性模型。

Establishing a Data Science Unit in an Academic Medical Center: An Illustrative Model.

机构信息

M. Desai is professor of medicine and of biomedical data science, section chief of biostatistics, Division of Biomedical Informatics Research, and director, Quantitative Sciences Unit, Stanford University School of Medicine, Palo Alto, California.

M. Boulos is executive director, Quantitative Sciences Unit, Stanford University School of Medicine, Palo Alto, California.

出版信息

Acad Med. 2022 Jan 1;97(1):69-75. doi: 10.1097/ACM.0000000000004079.

Abstract

The field of data science has great potential to address critical questions relevant for academic medical centers. Data science initiatives are consequently being established within academic medicine. At the cornerstone of such initiatives are scientists who practice data science. These scientists include biostatisticians, clinical informaticians, database and software developers, computational scientists, and computational biologists. Too often, however, those involved in the practice of data science are viewed by academic leadership as providing a noncomplex service to facilitate research and further the careers of other academic faculty. The authors contend that the success of data science initiatives relies heavily on the understanding that the practice of data science is a critical intellectual contribution to the overall science conducted at an academic medical center. Further, careful thought by academic leadership is needed for allocation of resources devoted to the practice of data science. At the Stanford University School of Medicine, the authors have developed an innovative model for a data science collaboratory based on 4 fundamental elements: an emphasis on collaboration over consultation, a subscription-based funding mechanism that reflects commitment by key partners, an investment in the career development of faculty who practice data science, and a strong educational component for data science members in team science and for clinical and translational investigators in data science. As data science becomes increasingly essential to learning health systems, centers that specialize in the practice of data science are a critical component of the research infrastructure and intellectual environment of academic medical centers.

摘要

数据科学领域具有解决与学术医疗中心相关的关键问题的巨大潜力。因此,学术医学领域正在建立数据科学计划。此类计划的基石是从事数据科学实践的科学家。这些科学家包括生物统计学家、临床信息学家、数据库和软件开发人员、计算科学家和计算生物学家。然而,参与数据科学实践的人员往往被学术领导层视为提供非复杂服务,以促进研究并促进其他学术教师的职业发展。作者认为,数据科学计划的成功在很大程度上取决于这样一种理解,即数据科学实践是对学术医疗中心进行的整体科学的一项关键知识贡献。此外,学术领导层需要仔细考虑用于数据科学实践的资源分配。斯坦福大学医学院的作者们根据以下 4 个基本要素,开发了一种基于数据科学协作实验室的创新模式:强调合作而非咨询、基于订阅的资助机制,反映关键合作伙伴的承诺、投资于从事数据科学实践的教师的职业发展,以及为团队科学中的数据科学成员以及数据科学中的临床和转化研究人员提供强大的教育组成部分。随着数据科学对学习健康系统变得越来越重要,专门从事数据科学实践的中心是学术医疗中心的研究基础设施和知识环境的关键组成部分。

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