Orphanidou Christina
Oxygen Research Ltd, 8 Vassileos Constantinou Street, 3075, Limassol, Cyprus.
Biophys Rev. 2019 Feb;11(1):83-87. doi: 10.1007/s12551-018-0495-3. Epub 2019 Jan 9.
The proliferation of smart physiological signal monitoring sensors, combined with the advancement of telemetry and intelligent communication systems, has led to an explosion in healthcare data in the past few years. Additionally, access to cheaper and more effective power and storage mechanisms has significantly increased the availability of healthcare data for the development of big data applications. Big data applications in healthcare are concerned with the analysis of datasets which are too big, too fast, and too complex for healthcare providers to process and interpret with existing tools. The driver for the development of such systems is the continuing effort in making healthcare services more efficient and sustainable. In this paper, we provide a review of current big data applications which utilize physiological waveforms or derived measurements in order to provide medical decision support, often in real time, in the clinical and home environment. We focus mainly on systems developed for continuous patient monitoring in critical care and discuss the challenges that need to be overcome such that these systems can be incorporated into clinical practice. Once these challenges are overcome, big data systems have the potential to transform healthcare management in the hospital of the future.
在过去几年中,智能生理信号监测传感器的激增,再加上遥测技术和智能通信系统的进步,导致医疗保健数据呈爆炸式增长。此外,更廉价且更有效的电力和存储机制的出现,显著提高了用于大数据应用开发的医疗保健数据的可用性。医疗保健领域的大数据应用涉及对数据集的分析,这些数据集规模太大、变化太快且过于复杂,以至于医疗服务提供者无法使用现有工具进行处理和解读。开发此类系统的驱动力在于持续努力提高医疗服务的效率和可持续性。在本文中,我们对当前利用生理波形或派生测量值以提供医疗决策支持(通常是实时的)的大数据应用进行综述,这些应用主要用于临床和家庭环境。我们主要关注为重症监护中的连续患者监测而开发的系统,并讨论需要克服的挑战,以便将这些系统纳入临床实践。一旦克服这些挑战,大数据系统就有可能改变未来医院的医疗管理。