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

Bridging Data Science and Organization Science: Leveraging Algorithmic Induction to Research Online Communities

Major developments in digital technology in recent decades have led to new ways of collaboration and knowledge creation, particularly the proliferation of open source software development communities. The unique characteristics of open source software development communities—especially the absence of employment contracts, fluidity of organizational boundaries and openness of knowledge collaboration—provide fruitful ground for advancing organizational theories. Anecdotal evidence has shown that new solutions to the problems of collaboration and coordination in the absence of a formal hierarchy and centralized organizational design have emerged in these communities. The last few years have also witnessed rapid progress in the development of predictive technologies based on artificial intelligence and machine learning algorithms that are efficient at providing fast, accurate and low-cost predictions. Such predictive algorithms show great promise as a methodological tool for organizational research. Moreover, as these algorithms increasingly facilitate decision-making tasks in organizations, they open up new opportunities for organizational design researchers to understand how organizational decision making can be better aided by applying machine learning. Accordingly, the goal of this thesis is two-fold. First, the thesis aims to extend the theoretical understanding of the following important but poorly understood questions: (1) How can organizations scale up without a formal hierarchy? and (2) How should decentralized organizations be designed? Second, condensing insights from the application of machine learning and artificial intelligence algorithms in organization science, I propose a framework that is useful for introducing machine learning algorithms into 1) organizational research methods and 2) organizational decision making. The first two studies of this thesis explore two important issues regarding organizing at scale in the absence of a formal hierarchy and centralized explicit mechanisms: (i) the efficient resolution of disputes and (ii) the delegation of tasks. These studies employ open source software development communities as a research context—specifically, the GitHub and OpenStack communities. In Study I, I find that dispute resolution in online communities features problem solving rather than bargaining and occurs in the absence of explicit global mechanisms. In these communities, larger and unconstrained discussions facilitate the switch from alternative-focused discussions to attribute-focused discussions, which increases the likelihood of dispute resolution. In Study II, I find that in the absence of perfect information on potential delegates and a formal hierarchy, delegators rely on experiential and vicarious learning from past delegation decisions when making current decisions. I also show that the decision to delegate authority significantly mediates the relationship between a delegators’ previous experience with delegation and the likelihood of future implementation. The last two studies in this thesis provide a framework for applying artificial intelligence and machine learning algorithms to organizational research and organizational decision making. In Study III, I present machine learning techniques as a useful new tool for organizational researchers pursuing inductive research. I propose that adding machine learning techniques to existing inductive research methods—for example, using robust and replicable “stylized” pattern detection—allows researchers interested in using qualitative data to both develop and test theories in a transparent and reproducible manner. Finally, Study IV provides a framework outlining prototypical decision-making structures for organizations in which human decision making can be blended with algorithmic decision-making. Overall, this thesis examines and advances our understanding of two underexplored aspects of online communities—i.e., dispute resolution and delegation (Study I and Study II, respectively)—and provides useful frameworks for integrating machine learning and artificial intelligence-based predictive technologies into organizational research and organizational decision making (Study III and Study IV, respectively)

On the strategic value of equifinal choice

Managers are often faced with the need to choose among multiple satisficing options. We call this situation equifinal choice and argue how it opens an opportunity for managers to choose a new trajectory for their firm-an opportunity for strategic action. Although equifinal choice can exist in any environment, it becomes most consequential when uncertainty is high. Uncertainty weakens the adherence of organizational members to a superordinate goal and the plurality of goals leads political processes to guide the firm's strategy. Extant view has identified random choice as an unbiased, fair, simple, and swift solution to the problem of equifinal choice. Random choice is also commonly used in machine learning and artificial intelligence systems. As organizations augment their decision making with these systems, there is a threat that they forego these strategic opportunities and randomly choose actions that fail to harness commitment and trust. In this Point of View article, we highlight the problem of equifinal choice, explain different ways it can be approached, and motivate why strategic choice can be valuable for organizations over and above defaulting to random choice.ISSN:2245-408

Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges

The current expansion of theory and research on artificial intelligence in management and organization studies has revitalized the theory and research on decision-making in organizations. In particular, recent advances in deep learning (DL) algorithms promise benefits for decision-making within organizations, such as assisting employees with information processing, thereby augment their analytical capabilities and perhaps help their transition to more creative work. We conceptualize the decision-making process in organizations augmented with DL algorithm outcomes (such as predictions or robust patterns from unstructured data) as deep learning–augmented decision-making (DLADM). We contribute to the understanding and application of DL for decision-making in organizations by (a) providing an accessible tutorial on DL algorithms and (b) illustrating DLADM with two case studies drawing on image recognition and sentiment analysis tasks performed on datasets from Zalando, a European e-commerce firm, and Rotten Tomatoes, a review aggregation website for movies, respectively. Finally, promises and challenges of DLADM as well as recommendations for managers in attending to these challenges are also discussed. © 2020 The Author(s)ISSN:0148-2963ISSN:1873-797

Resolving governance disputes in communities: A study of software license decisions

Research summary Resolving governance disputes is of vital importance for communities. Gathering data from GitHub communities, we employ hybrid inductive methods to study discussions around initiation and change of software licenses—a fundamental and potentially contentious governance issue. First, we apply machine learning algorithms to identify robust patterns in data: resolution is more likely in larger discussion groups and in projects without a license compared to those with a license. Second, we analyze textual data to explain the causal mechanisms underpinning these patterns. The resulting theory highlights the group process (reflective agency switches disputes from bargaining to problem solving) and group property (preference alignment over attributes) that are both necessary for the resolution of governance disputes, contributing to the literature on community governance. Managerial summary Online communities play an increasingly important role in how companies innovate across organizational boundaries and attract talent across geographic locations. However, online communities are no Utopia; disputes abound even (more) when we collaborate virtually. In particular, governance disputes can threaten the functioning and existence of online communities. Our study suggests that governance disputes in online communities either unfold as bargaining over which solution is better or searching for a satisfactory solution. The latter is more likely to reach a resolution, when there is common ground. Companies interested in leveraging the power of online communities should (a) identify or train certain participants to transform endless bargaining into collective problem solving and (b) foster shared knowledge and value basis among participants through recruitment and strong organizational culture

Abstracts of National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental Biotechnology

This book contains the abstracts of the papers presented at the National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental Biotechnology (NCB4EBT-2021) Organized by the Department of Biotechnology, National Institute of Technology Warangal, India held on 29–30 January 2021. This conference is the first of its kind organized by NIT-W which covered an array of interesting topics in biotechnology. This makes it a bit special as it brings together researchers from different disciplines of biotechnology, which in turn will also open new research and cooperation fields for them. Conference Title: National Conference on Biological, Biochemical, Biomedical, Bioenergy, and Environmental BiotechnologyConference Acronym: NCB4EBT-2021Conference Date: 29–30 January 2021Conference Location: Online (Virtual Mode)Conference Organizer: Department of Biotechnology, National Institute of Technology Warangal, Indi

Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83-0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders