Artificial intelligence (AI) is revolutionizing healthcare by introducing novel treatments and applications, thereby transforming the sector. However, the complexity, ambiguity, and inherent risks associated with AI can create tensions for healthcare workers that may result in stress, anxiety, and discomfort when they make decisions. These tensions are paradoxical in nature as they may present conflicting demands that can persist over time and develop into seemingly irrational situations. Understanding how these paradoxical tensions affect healthcare workers' responses to AI is crucial in addressing their concerns. This study investigates the role of paradoxical tensions and the paradoxical mindset in shaping healthcare workers' responses to AI. The study examines how these two factors influence individuals' intention to adopt AI systems and tools and evaluates the users' satisfaction with them. Using a quantitative survey design, data were collected from 357 healthcare workers. The results, based on regression analysis, indicate that paradoxical tensions positively influence both individuals' intention to adopt AI systems and tools and their satisfaction with the current use of AI systems and tools. The results also indicate that the paradoxical mindset positively mediates these relationships. © 2024 The Authors

The project Open and Self Organizing Mechanisms for Sustainable Mobility as a Service (OSMaaS) ran between 2020 and 2024, hosted by Halmstad University and funded by The Knowledge Foundation. The project was a collaboration between researchers from service design, design ethnography, business model innovation, and intelligent systems, and the companies Volvo Cars, WirelessCar, Polestar, and Devoteam. One of the project’s outputs is the OSMaaS Service Design Framework that integrates research from the different activities in the project into a toolkit for service designers. This booklet provides a guide for how to apply the framework. Each canvas can be used standalone or in any order, but our experience is that the framework is most powerful when following the design process presented here. The canvases can be downloaded from the OSMaaS webpage and are free to use. 

A growing and aging population provides challenges for the healthcare sector, generating higher healthcare costs, and ineffective work process that results in long patient queues and problems with recruiting and retaining healthcare professionals. Artificial intelligence (AI) is considered as one means to provide efficient processes for healthcare professionals, e.g. in diagnostics and treatment recommendations. However, research has shown that there are many obstacles to successfully introducing and using AI applications in healthcare, especially by focusing on the organizational level. However, individual healthcare professionals have an important role to play in the transition towards information driven healthcare. 

Therefore, we address the healthcare professionals' perception of the usefulness and value of AI applications, as well as challenges and considerations of this new technology. 

The study is based on an exploratory approach with more than 350 healthcare professionals in Sweden, carried out beginning of 2024. The questionnaire includes perceptions of the use of AI and identifies potential challenges that need to be addressed. The respondents include doctors (92%) and nurses (8%). The sample consists of answers from 221 (62%) male and 136 (38%) female respondents. Most of the respondents work in public hospitals (54%) and health centers (20% public and 14% private). Several AI applications are used by healthcare professionals, spanning from administrative work reduction to new insights in the analysis of complex cases.

Thematic analysis is conducted to create a model of perception of usefulness, values and problems (barriers). The analysis includes a stepwise analysis to identify patterns and themes.

The  results from the project provide insights into how the introduction of AI applications in healthcare changes the work of healthcare professionals and the perceived challenges that need to be addressed to improve their work by using AI. To some extent, implementation and use is based on healthcare professionals’ interest in using new advanced technology but for others the decision to adopt AI is primarily based on formal decisions within the organization. Respondents that have been using AI for at least six months, indicate AI supports decision making, with the main benefit consisting of a more effective and faster work process, while other respondents do not perceive any changes. A surprising result is that healthcare professionals have identified the possibility to test and evaluate new ideas and more complex cases. One interpretation is that AI has made the workload easier, which may allow for more innovative work. Another interpretation is that their experience-based knowledge is augmented by AI, and this makes it possible for them to handle more complex cases.   However, others experience a learning paradox – challenging to find time and learn how to use the technology, while at the same time adopting by testing AI applications.

Conclusions drawn from the ongoing study provide insights on the transformation phase towards implementing and using AI applications in healthcare.

Despite the advances in modern medicine, the use of data-driven technologies (DDTs) to prevent surgical site infections (SSIs) remains a major challenge. Scholars recognise that data management is the next frontier in infection prevention, but many aspects related to the benefits and advantages of using DDTs to mitigate SSI risk factors remain unclear and underexplored in the literature. This study explores how DDTs enable value creation in the prevention of SSIs. This study follows a systematic literature review approach and the PRISMA statement to analyse peer-reviewed articles from seven databases. Fifty-nine articles were included in the review and were analysed through a descriptive and a thematic analysis. The findings suggest a growing interest in DDTs in SSI prevention in the last 5 years, and that machine learning and smartphone applications are widely used in SSI prevention. DDTs are mainly applied to prevent SSIs in clean and clean-contaminated surgeries and often used to manage patient-related data in the postoperative stage. DDTs enable the creation of nine categories of value that are classified in four dimensions: cost/sacrifice, functional/instrumental, experiential/hedonic, and symbolic/expressive. This study offers a unique and systematic overview of the value creation aspects enabled by DDT applications in SSI prevention and suggests that additional research is needed in four areas: value co-creation and product-service systems, DDTs in contaminated and dirty surgeries, data legitimation and explainability, and data-driven interventions. © 2023, The Author(s).

This chapter researches the reconfiguration of business models and ecosystems in relation to decoupling and resilience in the context of data-driven technologies via conducting a systematic literature review (SLR). New data-driven technologies have been largely introduced to different sectors. Digitalisation may lead to disruptive changes in any industry, including creating or entering new business models, lowering or changing entry barriers into markets and enabling the breakup of sectorial silos. Although the COVID-19 pandemic accelerated significantly the digitalisation of the healthcare sector, innovation adoption in the sector proceeds slower than in most other industries. This chapter reviews systematically the existing literature on this area and develops a research agenda aiming at answering the pre-set research question: To address the research question, an SLR methodology has been applied to provide insights, critical reflections, managerial implications and research road maps for future research. The chapter identifies the potential benefits of the use of data-driven technology in healthcare at organisational, institutional, ethical and macro-level dimensions. It discusses the adoption of digitalisation and healthcare management practices to enhance data-driven outcomes. Based on the conducted literature review and the bibliometric analysis of articles included in the chapter, an integrative conceptual framework for digital healthcare is suggested. © 2023 selection and editorial matter, Svetla T. Marinova and Marin A. Marinov; individual chapters, the contributors

In recent years, the concept of lifelong learning has been emphasized in relation to higher education, with a bearing idea of the possibility for the individual for a continuous, self-motivated pursuit of gaining knowledge for both personal and professional reasons, provided by higher education institutions (HEI:s). But how can this actually be done in practice? In this paper we present an ongoing project called MAISTR, which is a collaboration between Swedish HEI:s and industry with the aim of providing a number of flexible courses within the subjects of Artificial intelligence (AI), Design, and Innovation management, for professionals. Our aim is to describe how the project is setup to create new learning opportunities, including the development process and co-creation with industry, the core structure and the pedagogical design. Furthermore, we would like to discuss both challenges and opportunities that come with this kind of project, as well as reflecting on early stage outcomes. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

The use of artificial intelligence (AI) is changing how manufacturing firms create new value, shifting their business models from product suppliers toward product- service systems (PSS) providers. To create value for customers and users, firms need to internally develop or externally access new or modified capabilities to be integrated into the development of PSS value propositions. Currently, the PSS literature does not systematically show which capabilities are complementary to the focal firm’s capabilities and from which actors they are acquired. Therefore, this study characterizes and explains the necessary complementary capabilities for MedTech firms to diversify into PSS, identify external actors, and describe which business activities MedTech firms use to attract and integrate external complementary capabilities. The research design was an embedded case study of a multinational MedTech firm with four PSS innovation cases. The findings show that the complementary capabilities in PSS development by MedTech firms consist of 1) health-related capabilities; 2) data-driven capabilities; and 3) social capabilities. To attract and integrate these capabilities, MedTech firms rely on negotiations and formal contracts, additional benefits to current clients, after- sales contact and remote support, and exploration of partners’ networks and reputations. By drawing on the PSS, servitization, and capability-based views of the firm, the paper explains the limits to the internalization of complementary capabilities.