Big data in anaesthesia: a narrative, nonsystematic review

Abstract

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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