Department of Professional Health Solutions & Services, Philips Research, 5656AE Eindhoven, The Netherlands.
Int J Environ Res Public Health. 2020 Apr 27;17(9):3046. doi: 10.3390/ijerph17093046.
In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these 'big data' put together can be utilized to optimize treatments for each unique patient ('precision medicine'). For this to be possible, it is necessary that hospitals, academia and industry work together to bridge the 'valley of death' of translational medicine. However, hospitals and academia often are reluctant to share their data with other parties, even though the patient is actually the owner of his/her own health data. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. There are some publicly available datasets, but these are usually only shared after study (and publication) completion, which means a severe delay of months or even years before others can analyse the data. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here, we show an analysis of the current literature around data sharing, and we discuss five aspects of data sharing in the medical domain: publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing, such as medical crowdsourcing and data generalists.
近年来，越来越多的健康数据正在产生。这些数据不仅来自专业的健康系统，还来自可穿戴设备。所有这些“大数据”结合起来，可以用于优化每个独特患者的治疗方法（精准医疗）。为了实现这一点，医院、学术界和工业界必须共同努力，弥合转化医学的“死亡之谷”。然而，医院和学术界往往不愿意与其他方共享其数据，尽管患者实际上是其自身健康数据的所有者。学术医院通常在临床试验和数据收集方面投入大量时间，并希望成为首批在该数据上发表论文的人。有一些公开可用的数据集，但这些数据集通常仅在研究（和发布）完成后才共享，这意味着其他人需要数月甚至数年的时间才能分析这些数据。一种解决方案是激励医院将数据与（其他）学术机构和行业共享。在这里，我们分析了当前围绕数据共享的文献，并讨论了医疗领域数据共享的五个方面：出版商要求、数据所有权、对数据共享的支持不断增加、数据共享计划以及联合使用联邦数据可能是解决方案。我们还讨论了一些围绕数据共享的潜在未来发展，例如医疗众包和数据通才。
