Ford Elizabeth, Boyd Andy, Bowles Juliana K F, Havard Alys, Aldridge Robert W, Curcin Vasa, Greiver Michelle, Harron Katie, Katikireddi Vittal, Rodgers Sarah E, Sperrin Matthew
Department of Primary Care and Public Health Brighton and Sussex Medical School Brighton UK.
ALSPAC, Population Health Sciences, Bristol Medical School University of Bristol Bristol UK.
Learn Health Syst. 2019 Mar 25;3(3):e10191. doi: 10.1002/lrh2.10191. eCollection 2019 Jul.
The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well-being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team-based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.
在过去六年里，英国及其他地区对健康数据科学进行了持续投资，这理应造就一个包容所有利益相关者的数据科学群体，各方共同努力，通过改善公众健康和福祉，利用数据造福社会。然而，通过创新性使用数据所带来的机遇仍未得到充分实现，导致研究效率低下以及本可避免的健康损害。在本文中，我们确定了健康数据科学实现更高生产率的最重要障碍。然后，我们借鉴以往的研究、领域专业知识和理论，概述如何克服这些障碍，同时应用我们包容性和透明度的核心价值观。我们相信，通过在研究规划、设计和执行的每个阶段让利益相关者有意义地参与，以及基于团队的数据科学，再加上利用新颖且安全的数据技术，能够实现巨大转变。将这些价值观应用于健康数据科学将保障健康数据研究的社会许可，并确保为了公共利益而透明、安全地使用数据。
