Leonard Santen , Florian Windler , Mark Coburn , Birgit Bette , Se-Chan Kim , Christian Bode , Philippe Kruse
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引用次数: 0
Abstract
Introduction
The medical sector is responsible for a significant share of greenhouse gas emissions. It is imperative that a transition towards ecological sustainability takes place. Nevertheless, initiatives for healthcare providers to reduce emissions remain limited in clinical practice, and climate-damaging behaviour persists. A salient example is the regular use of desflurane in anaesthesia. In order to promote sustainable clinical practice, the implementation of behavioural decision support mechanisms, so called nudges, could be a promising approach.
Methods
A retrospective study was conducted to analyse the effectiveness of nudges to reduce the consumption of desflurane. The nudges comprised structural modifications to the workplace, with desflurane vaporisers being replaced with those for sevoflurane and isoflurane. The effectiveness of the nudges was evaluated by analysis of the desflurane's order volume. In addition, the emissions of greenhouse gases were normalised to the number of surgical procedures performed. Finally, the economic benefit of reduced desflurane consumption was investigated based on the purchase price.
Results
Following the implementation, desflurane was no longer utilised in the long term. Overall, these nudges resulted in a 91 % reduction in emissions of volatile anaesthetics per quarter, equivalent to 219 t CO2e. Consequently, the average greenhouse gas emissions per surgical procedure were reduced by 42.2 kg CO2e. Furthermore, the costs for volatile anaesthetics decreased by 63.9 %, amounting to a reduction of €18,237.28.
Discussion
The implementation of nudges has been demonstrated to lead to a cessation of desflurane consumption, thereby supporting the green transformation of the healthcare sector. In the future, green nudges favouring sustainable clinical practice are poised to assume greater significance as a cost-effective and readily implementable sustainability measure.