Reconfiguration of uncertainty: Introducing AI for prediction of mortality at the emergency department

The promise behind many advanced digital technologies in healthcare is to provide novel and accurate information, aiding medical experts to navigate and, ultimately, decrease uncertainty in their clinical work. However, sociological studies have started to show that these technologies are not producing straightforward objective knowledge, but instead often become associated with new uncertainties arising in unanticipated places and situations. This study contributes to the body of work by presenting a qualitative study of an Artificial Intelligence (AI) algorithm designed to predict the risk of mortality in patients discharged to home from the emergency department (ED). Through in-depth interviews with physicians working at the ED of a Swedish hospital, we demonstrate that while the AI algorithm can reduce targeted uncertainty, it simultaneously introduces three new forms of uncertainty into clinical practice: epistemic uncertainty, actionable uncertainty and ethical uncertainty. These new uncertainties require deliberate management and control, marking a shift from the physicians' accustomed comfort with uncertainty in mortality prediction. Our study advances the understanding of the recursive nature and temporal dynamics of uncertainty in medical work, showing how new uncertainties emerge from attempts to manage existing ones. It also reveals that physicians' attitudes towards, and management of, uncertainty vary depending on its form and underscores the intertwined role of digital technology in this process. By examining AI in emergency care, we provide valuable insights into how this epistemic technology reconfigures clinical uncertainty, offering significant theoretical and practical implications for the integration of AI in healthcare.

Adopting an Ecosystem Approach to Digitalization-driven Organizational Change ? Actionable Knowledge from a Collaborative Project

A vibrant debate has emerged on the opportunities of digitalization for ‘humanizing’ work versus substitution or deskilling effects. Recognizing the processes of designing digitalized organizations as crucial for predicting the outcome of digitalization on workers and labor, extant research has developed an evolutionary ecosystem socio-technical (STS) perspective aimed at designing more human organizations in light of digital transformation. In this study we build upon the STS ecosystem analytical framework by Winby and Mohrman (2018), aiming to explore how the STS design principles, as proposed by said authors, are applied in three big manufacturing companies, in light of digitalization. Data collected through qualitative techniques has been analyzed abductively. Findings provide a detailed overview of how the STS ecosystem design principles are operationalized, and shed novel light on applied design methods, such as ‘agile’ and ‘design thinking’, able to support interconnection among systems, in a never-ending and iterative process. This study extends the existing ecosystem STS organization design conceptual model by exploring ‘how’ we change, ‘who’ the change is for, while also investigating ‘what’ this means for the hitherto conceptualized STS work-system. Implication for management practice and ODC scholarship are also discussed.

Adopting AI in the Context of Knowledge Work: Empirical Insights from German Organizations

Artificial Intelligence (AI) is increasingly adopted by organizations. In general, scholars agree that the adoption of AI will be associated with substantial changes in the workplace. Empirical evidence on the phenomenon remains scarce, however. In this article, we explore the adoption of AI in the context of knowledge work. Drawing on case study research in eight German organizations that have either implemented AI or are in the process of developing AI systems, we identify three pervasive changes that knowledge workers perceive: a shift from manual labor and repetitive tasks to tasks that involve reasoning and empathy, an emergence of new tasks and roles, and an emergence of new skill requirements. In addition, we identify three factors that are conducive to the development of AI systems in the context of knowledge work: leadership support, participative change management, and effective integration of domain knowledge. Theoretical and managerial implications are discussed.

Brains exposed: How new imaging technology reconfigures expertise coordination in neurosurgery

This study explores the consequences of the uptake of novel medical technology for expertise coordination among specialists. Building on a 40-month field study, we show how the introduction of iMRI triggered a significant change in temporal, interactional and role dimensions of coordination. We trace the origin of those changes to the novel role the images started to play in surgery: iMRI generated new forms of visibility of the brain (offering real-time, more precise imaging) and new forms of ambiguity for clinical action (representing the brain undergoing manipulation). In response to this dual nature of images, specialists reconfigured how they engage with each other: aligning temporal rhythms of their corresponding work practices, shifting their interactions to focus more on dialog and synchronous mutual exploration of images, and delegating increased weight to radiological judgement for guiding immediate surgical action. We discuss the implications of our findings for research on expertise coordination and on the consequences of novel medical technologies for situated practice.

From Lock-In to Transformation: A Path-Centric Theory of Emerging Technology and Organizing

We offer a path-centric theory of emerging technology and organizing that addresses a basic question. When does emerging technology lead to transformative change? A path-centric perspective on technology focuses on the patterns of actions afforded by technology in use. We identify performing and patterning as self-reinforcing mechanisms that shape patterns of action in the domain of emerging technology and organizing. We use a dynamic simulation to show that performing and patterning can lead to a wide range of trajectories, from lock-in to transformation, depending on how emerging technology in use influences the pattern of action. When emerging technologies afford new actions that can be flexibly recombined to generate new paths, decisive transformative effects are more likely. By themselves, new affordances are not likely to generate transformation. We illustrate this theory with examples from the practice of pharmaceutical drug discovery. The path-centric perspective offers a new way to think about generativity and the role of affordances in organizing.