| 1. |
- Hagberg, Johan, 1973, et al.
(författare)
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Konsumtionsrapporten 2024
- 2024
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- I Konsumtionsrapporten 2024 sammanfattas och analyseras konsumtionen i Sverige under 2023. I den första delen, ”Hushållens konsumtion” ges en översikt över den privata konsumtionen i Sverige och hur den förändrats. Här beskrivs även hållbarhetsaspekter på konsumtionen samt hushållens framtidsförväntningar på den egna ekonomin. I andra delen, ”Detaljhandeln” beskrivs försäljning och utveckling inom detaljhandeln under 2023 med fokus på olika delbranscher, kanaler och platser, inom e-handeln respektive den butiksbaserade detaljhandeln. Den andra delen avslutas med handelns framtidsförväntningar. Årets Konsumtionsrapport innehåller två fördjupningsdelar som var en och analyserar aktuella teman inom konsumtion. I den första av fördjupningsdelarna diskuterar Magdalena Petersson McIntyre och Emma Björner lyxkonsumtionens utveckling, status, hållbarhet och moral. I den andra fördjupningsdelen belyser Karin M. Ekström kulturkonsumtion med fokus på konstutställningar.
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| 2. |
- Karnehed, Sara, 1979-, et al.
(författare)
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Electronic medication administration record (eMAR) in Swedish home healthcare—Implications for Nurses' and nurse Assistants' Work environment : A qualitative study
- 2024
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Ingår i: Scandinavian Journal of Caring Sciences. - Chichester : John Wiley & Sons. - 0283-9318 .- 1471-6712. ; 38:2, s. 347-357
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Tidskriftsartikel (refereegranskat)abstract
- Background: The electronic medication administration record (eMAR) is an eHealth system that has replaced the traditional paper-based medication administration used in many healthcare settings. Research has highlighted that eHealth technologies can change working methods and professional roles in both expected and unexpected ways. To date, there is sparse research that has explored how nurses and nurse assistants (NA) in home healthcare experience eMAR in relation to their work environment. Aim: The aim was to explore how nurses and nurse assistants experienced their work environment, in terms of job-demand, control, and support in a Swedish home healthcare setting where an electronic medication administration record had been implemented to facilitate delegation of medical administration. Method: We took a qualitative approach, where focus groups were used as data collection method. The focus groups included 16 nurses and nine NAs employed in a Swedish municipality where an eMAR had been implemented 6 months before the first focus groups were performed. The analysis adapted the job-demand-control-support model, by condensing the professionals' experiences into the three categories of demand, control, and support, in alignment with the model. Results: NAs experienced high levels of job demand and low levels of job control. The use of the eMAR limited NAs' ability to control their work, in terms of priorities, content, and timing. In contrast, the nurses described demands as high but manageable, and described having a high level of control. Both professions found the eMar supportive. Conclusion: Nurses and NAs in home healthcare experienced changes in their work environment regarding demand, control, and support when an eMAR was implemented to facilitate delegation of medical administration. In general, nurses were satisfied with the eMAR. However, NAs felt that the eMAR did not cover all aspects of their daily work. Healthcare organisations should be aware of the changes that digitalisation processes entail in the work environment of nurses and NAs in home healthcare. © 2024 The Authors. Scandinavian Journal of Caring Sciences published by John Wiley & Sons Ltd on behalf of Nordic College of Caring Science.
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| 3. |
- Barth, Henrik, 1971-, et al.
(författare)
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Towards a Mass Customised Healthcare - Healthcareprofessionals Experience of AI
- 2024
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 4. |
- Nilsen, Per, 1960-, et al.
(författare)
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Towards evidence-based practice 2.0 : leveraging artificial intelligence in healthcare
- 2024
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Ingår i: Frontiers in Health Services. - Lausanne : Frontiers Media S.A.. - 2813-0146. ; 4
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Forskningsöversikt (refereegranskat)abstract
- Background: Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this.Challenges with the three components of EBP: Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources.AI assistance for the three components of EBP: AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue.Conclusion: This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare. ©2024 The Authors
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| 5. |
- Petersson, Lena, 1968-, et al.
(författare)
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Paving the way for additional forms of boundary work – how the implementation of AI can change healthcare
- 2024
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Konferensbidrag (refereegranskat)abstract
- A digital transformation of Swedish healthcare is currently taking place, and artificial intelligence (AI) is meant to solve many of the healthcare sector's challenges. The objective of this paper is to describe and analyze how the boundaries around the physicians' work could change when AI is implemented in healthcare and what boundary work actors on different levels in a healthcare system conduct. We conducted 26 semi-structured interviews with healthcare leaders and 18 with healthcare managers and professionals. The result shows that the leaders, healthcare managers, and healthcare professionals describe different types of boundary work in regard to the implementation of AI. The implementation of AI in healthcare could change the boundaries around the healthcare professionals’ work and generate new kinds of boundary work that could affect the implementation. These findings can inform both practice and policy.
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| 6. |
- Petersson, Lena, 1968-, et al.
(författare)
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This far you may come, but no farther: How the implementation of AI triggered boundary work among healthcare professionals
- 2024
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Konferensbidrag (refereegranskat)abstract
- A digital transformation of Swedish healthcare is currently taking place, and artificial intelligence (AI) is meant to solve many of the healthcare sector's challenges. The objective of this paper is to describe and analyze how the boundaries around the physicians' work could change when AI is implemented in healthcare and what boundary work actors on different levels in a healthcare system conduct. We conducted 26 semi-structured interviews with healthcare leaders and 18 with healthcare managers and professionals. The result shows that the leaders, healthcare managers, and healthcare professionals describe different types of boundary work in regard to the implementation of AI. The implementation of AI in healthcare could change the boundaries around the healthcare professionals’ work and generate new kinds of boundary work that could affect the implementation. These findings can inform both practice and policy.
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| 7. |
- Petersson Troije, Charlotte, 1971-, et al.
(författare)
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Unlocking the Transformative Potential of Outdoor Office Work—A Constructivist Grounded Theory Study
- 2024
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Ingår i: Challenges. - 2078-1547. ; 15:2
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Tidskriftsartikel (refereegranskat)abstract
- White-collar workers around the world are reconfiguring their ways of working. Some have found their way out, performing office work outdoors, through walk-and-talks, outdoor meetings, or reading sessions. Working outdoors has proved both invigorating and challenging. This qualitative interview study aims to develop a conceptual framework concerning the implications of white-collar workers incorporating the outdoors into their everyday work life. Applying a constructivist grounded theory approach, 27 interviews with a total of 15 participants were systematically analyzed. Findings evolved around the following categories: practicing outdoor office work, challenging the taken-for-granted, enjoying freedom and disconnection, feeling connected and interdependent, promoting health and well-being, enhancing performance, and finally adding a dimension to work. These categories were worked into a conceptual model, building on the dynamic relationship between the practice of working outdoors on one hand, and how this challenges the system in which office work traditionally takes place on the other. Interviews reflected the profound learning process of the employees. Drawing on the concepts of free space and resonance, we demonstrate how performing office work outdoors may unlock a transformative potential by opening up connectedness and interdependence and contribute to a sustainable work life as well as overall sustainable development.
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