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This paper explores the potential of Henri Lefebvre's rhythmanalysis to understand data as an appearance assumed by the quantitative abstraction of everyday life, which negates a qualitative disjuncture between different natural and social rhythms – specifically those between embodied circadian and biological rhythms and the rhythms of working life. It takes as a case study a prototype performance research method investigating the methodological and practical potential of quantified self technologies to reconnect the body to its forms of abstraction in a digital age by means of the collection, interpretation and sonification of data using wearable tech, mobile apps, synthesised music and modes of visual communication. Quantitative data were selectively ‘sonified’ with synthesisers and drum machines to produce a 40-minute electronic symphony performed to a public audience. The paper theorises the project as an intervention reconnecting quantitative data with the qualitative experience it abstracts from, exploring the potential for these technologies to be used as tools of remediation that recover the embodied social subject from its abstraction in data for critical self-knowledge and understanding.

Cognitive work analysis (CWA) is a method of understanding and documenting the constraints inherent in a work domain, irrespective of the actions undertaken within the work domain and the actors who undertake them. The keystone of CWA is the abstraction–decomposition space (ADS), which provides a constraint-based overview of the system. CWA has been successfully applied in a variety of settings to create tools that make the underlying goals and constraints of the system more apparent, and allow a worker the flexibility to perform his or her job in a manner appropriate to the current conditions, without being restricted to a particular task flow. In the current study, semistructured protocol analysis was conducted with six research librarians in order to create an ADS representing the information research work domain. The resulting ADS was reviewed with the participants, who confirmed its accuracy. Insight provided by the ADS regarding the work domain of research librarians is discussed, as are implications for tools to support information research.

The development of qualitative research methods addresses the need to explore, understand and interpret complex and subjective phenomena across various fields of study. These methods are guided by methodological frameworks, and data collection involves taking several precautions for observation or interviews. While these guidelines facilitate an emphasis on the objective aspects of discourse, accounting for subjectivity and emotions proves more challenging. However, these subjectivity and emotions are deemed as significant sources of information. In this chapter, we propose an innovative data collection method centred around creating collages and engaging in group discussions to decipher their meaning. Collage serves as a visual medium, and we recommend utilising semiotic analysis tools to comprehend its significance. To gain a more precise understanding of the value of collage as a data collection method, we studied a collage workshop organised by CREE. Through image analysis and exchanges, our findings reveal that collage acts as a physical medium that fosters exchanges, deepens ideas and restricts digressions. Additionally, collage allows for the expression and discussion of emotions linked to the image rather than the individual. The space of intersubjective reflexivity facilitated by collage enables a profound comprehension, critical assessment and augmentation of ideas and the interpretation of emotions without compromising the sensitivity of the author. This chapter’s main contribution is evidently manifested here.

Achieving reliable instrument reprocessing requires finding the right balance among cost, productivity, and safety. However, there have been few attempts to comprehensively examine sterile processing department (SPD) work systems. We considered an SPD as an example of a socio-technical system – where people, tools, technologies, the work environment, and the organization mutually interact – and applied work systems analysis (WSA) to provide a framework for future intervention and improvement.

The study was conducted at two SPD facilities at a 700-bed academic medical center servicing 56 onsite clinics, 31 operating rooms (ORs), and nine ambulatory centers. Process maps, task analyses, abstraction hierarchies, and variance matrices were developed through direct observations of reprocessing work and staff interviews and iteratively refined based on feedback from an expert group composed of eight staff from SPD, infection control, performance improvement, quality and safety, and perioperative services. Performance sampling conducted focused on specific challenges observed, interruptions during case cart preparation, and analysis of tray defect data from administrative databases.

Across five main sterilization tasks (prepare load, perform double-checks, run sterilizers, place trays in cooling, and test the biological indicator), variance analysis identified 16 failures created by 21 performance shaping factors (PSFs), leading to nine different outcome variations. Case cart preparation involved three main tasks: storing trays, picking cases, and prioritizing trays. Variance analysis for case cart preparation identified 11 different failures, 16 different PSFs, and seven different outcomes. Approximately 1% of cases had a tray with a sterilization or case cart preparation defect and 13.5 interruptions per hour were noted during case cart preparation.

While highly dependent upon the individual skills of the sterile processing technicians, making the sterilization process less complex and more visible, managing interruptions during case cart preparation, improving communication with the OR, and improving workspace and technology design could enhance performance in instrument reprocessing.

Machine learning and artificial intelligence (AI) have arisen as the availability of larger data sources, statistical methods, and computing power have rapidly and simultaneously evolved. The transformation is leading to a revolution that will affect virtually every industry. Businesses that are slow to adopt modern data practices are likely to be left behind with little chance to catch up.

The purpose of this chapter is to provide a brief overview of machine learning and AI in the business setting. In addition to providing historical context, the chapter also provides justification for AI investment, even in industries in which data is not the core business function. The means by which computers learn is de-mystified and various algorithms and evaluation methods are presented. Lastly, the chapter considers various ethical and practical consequences of machine learning algorithms after implementation.

Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.

The objective of the research, and paper, is to determine first whether or not the accounting abstraction appears to dominate the manager’s perceptions of the physical reality of the firm’s utilization of its physical assets, and second, whether changes in the accounting abstraction (e.g. the addition of Capacity cost management reports and measurements) lead to changes in how managers perceive, and use, their physical assets. Using a cognitive decision-making structure developed by Wagenaar et al. (1995), this study explores the interplay between the structure and nature of capacity reporting (the surface structure of the decision) and the subsequent analysis and choice of managers within the firm (the deep structure of the decision). A five-site field research methodology was used to gather data from companies across a multitude of industry contexts and situations. Results suggest that the nature of capacity measurement and reporting does shape manager’s perceptions of current and potential future performance (the cognitive surface structure), with major implications for the nature and type of decisions and trade-offs made (the deep structure). Specifically, managers appear to make decisions that are illogical when considered in light of the physical reality of their operations based on the representations of this reality (e.g. the capacity measures and reports). Analysis and interpretation of these results suggest that what accounting makes visible appears to drive decision-making and performance in organization.