麻醉中的大数据:一个叙述性的、非系统的回顾

P. Dony, Rémi Florquin, P. Forget
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引用次数: 0

摘要

随着“麻醉信息管理系统”(AIMS)的使用,数据的生成正在增长,但是为科学目的而适当使用数据往往由于缺乏整合而被浪费。这篇叙述性综述的目的是描述常规收集数据的使用及其对提高护理质量的潜在有用性,首先通过定义国家卫生服务(NHS)提出的电子健康记录整合的六个级别,并以麻醉实践中的例子为例进行说明。其次,解释可以采取哪些措施从微观系统(对患者而言)、中观系统(对部门和医院机构而言)和宏观系统(对医疗保健和公共卫生而言)的数据中获利。接下来,我们将描述一个自制的AIMS解决方案,以及他在不同层面上的整合所带来的机会和隐含的研究前景。高收入国家以外的机会也将出现。所有这些都得出结论,围手术期全球研究的核心数据集可能有助于建立一个框架,用于整合来自电子健康记录的大量数据。它将允许一个不断的重新评估我们的实践作为麻醉师提供最好的护理病人。在这方面,对一些麻醉师进行数据科学和人工智能方面的培训至关重要。我们还必须考虑数据中心的生态足迹,因为这些数据中心消耗能源。必须为这些变化做好准备,并将麻醉专业与数据科学家合作,转变为围手术期医学中更突出的角色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Big data in anaesthesia: a narrative, nonsystematic review
Data generation is growing with the use of ‘anaesthesia information management systems’ (AIMS), but the appropriate use of data for scientific purposes is often wasted by a lack of integration. This narrative review aims to describe the use of routinely collected data and its potential usefulness to improve the quality of care, first by defining the six levels of integration of electronic health records as proposed by the National Health Service (NHS) illustrated by examples in anaesthesia practice. Secondly, by explaining what measures can be taken to profit from those data on the micro-system level (for the patient), the meso-system (for the department and the hospital institution) and the macro-system (for healthcare and public health). We will next describe a homemade AIMS solution and the opportunities which result from his integration on the different levels and the research prospects implied. Opportunities outside of high-income countries will also be presented. All lead to the conclusion that a core dataset for peri-operative global research may facilitate a framework for the integration of large volumes of data from electronic health records. It will allow a constant re-evaluation of our practice as anaesthesiologists to offer the best care for patients. In this regard, the training of some anaesthesiologists in data science and artificial intelligence is of paramount importance. We must also take into account the ecological footprint of data centres as these are energy-consuming. It is essential to prepare for these changes and turn the speciality of anaesthesia, collaborating with data scientists, into a more prominent role of peri-operative medicine.
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