Aim: This systematic literature review aimed to identify, appraise and synthesize available research studies that apply intersectionality in nursing research. Design: Systematic review. Data Sources: Empirical and theoretical nursing studies published before February 2022 were identified from the PubMed and CINAHL databases. Studies were eligible for inclusion if they substantially covered the topics of intersectionality and nursing, had undergone peer-review, and were written in English. Review Methods: The PRISMA 2020 statement for reporting systematic reviews was used to report findings. The Joanna Briggs Institute Critical Appraisal tools were used to assess the quality of the included research studies. Results: Out of 331 identified studies, 60 studies were substantially about nursing and intersectionality, and were included in the review. There are a myriad of ways that the concept of intersectionality has been adopted in nursing research. Furthermore, there was great heterogeneity in the definition and application of the concept of intersectionality, and only a few studies were empirical. Conclusion: There is a need for robust and clear framing of how the concept of intersectionality is defined and understood in nursing research. There is also a need for more empirical research effectively adopting the concept of intersectionality to enhance our understanding of how health inequities operate within the field of nursing. No Patient or Public Contribution: No patients, service users, caregivers or members of the public were involved in this work. © 2023 The Authors. Nursing Open published by John Wiley & Sons Ltd.

In Sweden, the term information-driven care has recently been put forward by healthcare organizations and researchers as a means for taking a comprehensive approach to the introduction of Artificial Intelligence (AI) in healthcare. The aim of this study is to systematically generate a consensus definition of the term information-driven care. To this end, we are conducting a Delphi study utilizing literature and experts' opinions. The definition is needed to enable knowledge exchange on information-driven care and operationalize its introduction into healthcare practice. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.

Background: Artificial intelligence (AI) holds significant promise for enhancing the efficiency and safety of medical history-taking and triage within primary care. However, there remains a dearth of knowledge concerning the practical implementation of AI systems for these purposes, particularly in the context of healthcare leadership. This study explores the experiences of healthcare leaders regarding the barriers to implementing an AI application for automating medical history-taking and triage in Swedish primary care, as well as the actions they took to overcome these barriers. Furthermore, the study seeks to provide insights that can inform the development of AI implementation strategies for healthcare.

Methods: We adopted an inductive qualitative approach, conducting semi-structured interviews with 13 healthcare leaders representing seven primary care units across three regions in Sweden. The collected data were subsequently analysed utilizing thematic analysis. Our study adhered to the Consolidated Criteria for Reporting Qualitative Research to ensure transparent and comprehensive reporting.

Results: The study identified implementation barriers encountered by healthcare leaders across three domains: (1) healthcare professionals, (2) organization, and (3) technology. The first domain involved professional scepticism and resistance, the second involved adapting traditional units for digital care, and the third inadequacies in AI application functionality and system integration. To navigate around these barriers, the leaders took steps to (1) address inexperience and fear and reduce professional scepticism, (2) align implementation with digital maturity and guide patients towards digital care, and (3) refine and improve the AI application and adapt to the current state of AI application development.

Conclusion: The study provides valuable empirical insights into the implementation of AI for automating medical history-taking and triage in primary care as experienced by healthcare leaders. It identifies the barriers to this implementation and how healthcare leaders aligned their actions to overcome them. While progress was evident in overcoming professional-related and organizational-related barriers, unresolved technical complexities highlight the importance of AI implementation strategies that consider how leaders handle AI implementation in situ based on practical wisdom and tacit understanding. This underscores the necessity of a holistic approach for the successful implementation of AI in healthcare. © The Author(s) 2024.

Background: The process of translation of AI and its potential benefits into practice in healthcare services has been slow in spite of its rapid development. Trust in AI in relation to implementation processes is an important aspect. Without a clear understanding, the development of effective implementation strategies will not be possible, nor will AI advance despite the significant investments and possibilities.

Objective: This study aimed to explore the scientific literature regarding how trust in AI in relation to implementation in healthcare is conceptualized and what influences trust in AI in relation to implementation in healthcare.

Methods: This scoping review included five scientific databases. These were searched to identify publications related to the study aims. Articles were included if they were published in English, after 2012, and peer-reviewed. Two independent reviewers conducted an abstract and full-text review, as well as carrying out a thematic analysis with an inductive approach to address the study aims. The review was reported in accordance with the PRISMA-ScR guidelines.

Results: A total of eight studies were included in the final review. We found that trust was conceptualized in different ways. Most empirical studies had an individual perspective where trust was directed toward the technology's capability. Two studies focused on trust as relational between people in the context of the AI application rather than as having trust in the technology itself. Trust was also understood by its determinants and as having a mediating role, positioned between characteristics and AI use. The thematic analysis yielded three themes: individual characteristics, AI characteristics and contextual characteristics, which influence trust in AI in relation to implementation in healthcare.

Conclusions: Findings showed that the conceptualization of trust in AI differed between the studies, as well as which determinants they accounted for as influencing trust. Few studies looked beyond individual characteristics and AI characteristics. Future empirical research addressing trust in AI in relation to implementation in healthcare should have a more holistic view of the concept to be able to manage the many challenges, uncertainties, and perceived risks.