Fiske Amelia, Prainsack Barbara, Buyx Alena
Institute for History and Ethics of Medicine, Technical University of Munich School of Medicine, Technical University of Munich, Munich, Germany.
Department of Anthropology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
J Med Internet Res. 2019 Jul 9;21(7):e11672. doi: 10.2196/11672.
In the era of data-rich medicine, an increasing number of domains of people's lives are datafied and rendered usable for health care purposes. Yet, deriving insights for clinical practice and individual life choices and deciding what data or information should be used for this purpose pose difficult challenges that require tremendous time, resources, and skill. Thus, big data not only promises new clinical insights but also generates new-and heretofore largely unarticulated-forms of work for patients, families, and health care providers alike. Building on science studies, medical informatics, Anselm Strauss and colleagues' concept of patient work, and subsequent elaborations of articulation work, in this article, we analyze the forms of work engendered by the need to make data and information actionable for the treatment decisions and lives of individual patients. We outline three areas of data work, which we characterize as the work of supporting digital data practices, the work of interpretation and contextualization, and the work of inclusion and interaction. This is a first step toward naming and making visible these forms of work in order that they can be adequately seen, rewarded, and assessed in the future. We argue that making data work visible is also necessary to ensure that the insights of big and diverse datasets can be applied in meaningful and equitable ways for better health care.
在数据丰富的医学时代，人们生活中越来越多的领域被数据化，并被用于医疗保健目的。然而，从临床实践和个人生活选择中获取见解，以及决定为此目的应使用哪些数据或信息，都带来了艰巨的挑战，需要大量的时间、资源和技能。因此，大数据不仅有望带来新的临床见解，还为患者、家庭和医疗保健提供者带来了新的、迄今为止很大程度上未被阐明的工作形式。基于科学研究、医学信息学、安塞尔姆·施特劳斯及其同事的患者工作概念，以及随后对表达工作的阐述，在本文中，我们分析了为使数据和信息对个体患者的治疗决策和生活具有可操作性而产生的工作形式。我们概述了数据工作的三个领域，我们将其描述为支持数字数据实践的工作、解释和情境化的工作以及纳入和互动的工作。这是朝着命名并使这些工作形式可见迈出的第一步，以便它们在未来能够得到充分的关注、认可和评估。我们认为，使数据工作可见对于确保庞大而多样的数据集的见解能够以有意义且公平的方式应用于改善医疗保健也是必要的。
