Payne Philip R O, Bernstam Elmer V, Starren Justin B
Institute for Informatics, Washington University in St. Louis, School of Medicine, Institute for Informatics, St. Louis, Missouri, USA.
The University of Texas Health Science Center at Houston, School of Biomedical Informatics, Houston, Texas, USA.
JAMIA Open. 2018 Aug 9;1(2):136-141. doi: 10.1093/jamiaopen/ooy032. eCollection 2018 Oct.
There are an ever-increasing number of reports and commentaries that describe the challenges and opportunities associated with the use of big data and data science (DS) in the context of biomedical education, research, and practice. These publications argue that there are substantial benefits resulting from the use of data-centric approaches to solve complex biomedical problems, including an acceleration in the rate of scientific discovery, improved clinical decision making, and the ability to promote healthy behaviors at a population level. In addition, there is an aligned and emerging body of literature that describes the ethical, legal, and social issues that must be addressed to responsibly use big data in such contexts. At the same time, there has been growing recognition that the challenges and opportunities being attributed to the expansion in DS often parallel those experienced by the biomedical informatics community. Indeed, many informaticians would consider some of these issues relevant to the core theories and methods incumbent to the field of biomedical informatics science and practice. In response to this topic area, during the 2016 American College of Medical Informatics Winter Symposium, a series of presentations and focus group discussions intended to define the current state and identify future directions for interaction and collaboration between people who identify themselves as working on big data, DS, and biomedical informatics were conducted. We provide a perspective concerning these discussions and the outcomes of that meeting, and also present a set of recommendations that we have generated in response to a thematic analysis of those same outcomes. Ultimately, this report is intended to: (1) summarize the key issues currently being discussed by the biomedical informatics community as it seeks to better understand how to constructively interact with the emerging biomedical big data and DS fields; and (2) propose a framework and agenda that can serve to advance this type of constructive interaction, with mutual benefit accruing to both fields.
越来越多的报告和评论描述了在生物医学教育、研究和实践中使用大数据和数据科学（DS）所带来的挑战和机遇。这些出版物认为，采用以数据为中心的方法来解决复杂的生物医学问题具有诸多显著益处，包括加快科学发现的速度、改善临床决策，以及在人群层面促进健康行为的能力。此外，还有一批相关且不断涌现的文献，描述了在这种背景下负责任地使用大数据必须解决的伦理、法律和社会问题。与此同时，人们越来越认识到，归因于数据科学扩展的挑战和机遇往往与生物医学信息学领域所经历的那些挑战和机遇相似。事实上，许多信息学家会认为其中一些问题与生物医学信息科学与实践领域的核心理论和方法相关。针对这一主题领域，在2016年美国医学信息学会冬季研讨会上，进行了一系列旨在界定当前状况并确定自认为从事大数据、数据科学和生物医学信息学工作的人员之间互动与合作未来方向的演讲和焦点小组讨论。我们提供了关于这些讨论及会议成果的观点，并根据对这些成果的主题分析提出了一系列建议。最终，本报告旨在：（1）总结生物医学信息学领域目前在寻求更好地理解如何与新兴的生物医学大数据和数据科学领域进行建设性互动时所讨论的关键问题；（2）提出一个框架和议程，以推动这种建设性互动，使两个领域都能互利。
