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

Abstract 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

Support for Data-driven Context Awareness in Smart Mobile and IoT Applications: Resource Efficient Probabilistic Models and a Quality-aware Middleware Architecture

The Internet of Things (IoT) is a new and emerging computing paradigm which strives to seamlessly link the physical and cyber worlds. Despite the technical foundations laid by context-aware computing and Ambient Intelligence (AmI), enabling smartness in IoT remains a multifaceted challenge. The high data volume, various heterogeneity and uncertainty aspects of IoT generated data along with the requirement to sense and interpret complex user related data often on resource constrained devices pose a major challenge for enabling context awareness in IoT. In this thesis, we address these challenges by providing necessary modeling abstractions, algorithms and architectural support. Our objective is to support data-driven techniques for robust and efficient recognition of user contexts in open, distributed and evolving environments. Also, we aim to hide the technical complexities in enabling context awareness from the application developers while keeping the entire process of context acquisition and dissemination transparent to the end users. Our first contribution includes a literature review on the state of the art machine learning techniques in the related fields and a practical analysis of the feasibility of such techniques for smart mobile and IoT applications. We identify various trade-offs in providing practical data driven intelligence for IoT. Furthermore, we investigate the usefulness and viability of transfer learning techniques to reuse models for context inference across devices. Our second contribution is a hybrid modeling framework for context inference called HARD-BN (Heterarchical, Autonomous, Recursive and Distributed Bayesian Network) that leverages correlated user contexts to not only improve the recognition accuracy but also robustness against missing data at runtime. It improves the capabilities of state-of-the-art models for context inference by systematically combining multiple learning techniques and sensor data. We use a frequent set mining inspired self-learning algorithm to discover the relations between user contexts by identifying one-to-many, many-to-one and many-to- many relations between the user contexts. This helps to semi-automate the modeling in HARD-BN as well as enable duty cycling of resource hungry sensors by discovering efficient alternative sensors. Together, these algorithms enable opportunistic recognition of diverse user contexts simultaneously. Third, we have realized an encompassing context middleware - Co4IoT, that enables provisioning of context on demand for smart applications. It brings together the users, applications and smart things, and addresses coordination concerns such as which sensors and configurations to use, and how to actively engage users to obtain ground truth annotations and effectively disseminate the context to the applications with complete control to the end users. We refine and extend the catalog of architectural tactics by codifying the design decisions for energy efficiency in IoT based on the best practices in the literature. Then we utilize them to provide a quality driven software architecture for Co4IoT. Also, we propose a feature model inspired knowledge representation technique for effectively matching the capabilities of the IoT platforms with the requirements of the smart applications. Finally, we corroborate the usefulness of the presented algorithms and middleware solutions with a novel smart locking mechanism for the mobile devices. We present a policy driven risk based authentication technique (PRISM) that leverages user contexts to provide low cost secondary authentication by identifying unsafe situations and automatically locking the devices. Our experiments have shown that it effectively protects mobile applications against unauthorized access in everyday scenarios without compromising on the usability.status: publishe

To cloud or not to cloud: A context-aware deployment perspective of augmented reality mobile applications

The resource limitations of mobile devices continue to impose constraints on the development of complex mobile applications. Performance and resource eefficiency remains a challenge. We have examined resource utilization and performance tradeoffs when extending an Augmented Reality (AR) application with context-awareness and cloud computing. The hypothesis is that the cost of these technologies is worth the benefits they result in. Our measurements show that filtered image datasets obtained through context-awareness result in lower latency and a reduced memory load when performing all AR computations on the mobile device. However, a cloud computing AR application does not benefit from in-depth context-awareness, as no part of the dataset is stored locally and the latency is approximately constant, relative to the Internet connectivity.status: publishe

Impact of COVID-19 on heart failure hospitalization and outcome in India – A cardiological society of India study (CSI–HF in COVID 19 times study – “The COVID C–HF study”)

Objectives: The presentation and outcomes of acute decompensated heart failure (ADHF) during COVID times (June 2020 to Dec 2020) were compared with the historical control during the same period in 2019. Methods: Data of 4806 consecutive patients of acute HF admitted in 22 centres in the country were collected during this period. The admission patterns, aetiology, outcomes, prescription of guideline-directed medical therapy (GDMT) and interventions were analysed in this retrospective study. Results: Admissions for acute heart failure during the pandemic period in 2020 decreased by 20% compared to the corresponding six-month period in 2019, with numbers dropping from 2675 to 2131. However, no difference in the epidemiology was seen. The mean age of presentation in 2019 was 61.75 (±13.7) years, and 59.97 (±14.6) years in 2020. There was a significant decrease in the mean age of presentation (p = 0.001). Also. the proportion of male patients decreased significantly from 68.67% to 65.84% (p = 0.037). The in-hospital mortality for acute heart failure did not differ significantly between 2019 and 2020 (4.19% and 4.,97%) respectively (p = 0.19). The proportion of patients with HFrEF did not change in 2020 compared to 2019 (76.82% vs 75.74%, respectively). The average duration of hospital stay was 6.5 days. Conclusion: The outcomes of ADHF patients admitted during the Covid pandemic did not differ significantly. The length of hospital stay remained the same. The study highlighted the sub-optimal use of GDMT, though slightly improving over the last few years

Students' participation in collaborative research should be recognised

Letter to the editor