Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A survey of parametric fingerprint-positioning methods

The term fingerprint-based (FP) positioning includes a wide variety of methods for determining a receiver’s position using a database of radio signal strength measurements that were collected earlier at known locations. Nonparametric methods such as the weighted k-nearest neighbor (WKNN) method are infeasible for large-scale mobile device services because of the large data storage and transmission requirements. In this work we present an overview of parametric FP methods that use model-based representations of the survey data. We look at three different groups of parametric methods: methods that use coverage areas, methods that use path loss models, and methods that use Gaussian mixtures. Within each group we study different approaches and discuss their pros and cons. Furthermore, we test the positioning performance of several of the analyzed approaches in different scenarios using real-world WLAN indoor data and compare the results to those of the WKNN method.publishedVersionPeer reviewe