Objective: To explore how primary healthcare workers in Sweden experienced and perceived the long-term impact of the pandemic on their work. Design: This is a descriptive qualitative study with individual semistructured interviews conducted 2 years after the onset of COVID-19. Data were analysed using an inductive thematic approach. Setting: Swedish primary healthcare units in rural and urban locations. Participants 29 healthcare providers (6 registered nurses, 7 assistant nurses, 8 physicians and 8 managers) in Swedish primary healthcare. Results: Data analysis yielded three overarching themes: (1) primary healthcare still affected by the pandemic; (2) primary healthcare changes made permanent; and (3) lessons learnt for handling future crises affecting primary healthcare. The participants experienced a high workload, even after the pandemic, and concluded that it would take years to catch up both mentally and workwise. Four lessons were learnt for future handling of crises that might affect primary healthcare: the importance of creating a cohesive primary healthcare management system to provide clarity regarding recommendations for how primary healthcare personnel should work, the need for management support at all levels, restricting and adapting the flow of information for primary healthcare and ascertaining the necessary resources if primary healthcare is to take on additional tasks. Conclusion: Two years after the onset of the COVID-19 pandemic, primary healthcare workers in Sweden experienced that their work was still affected by the pandemic. Our findings highlight the importance of ensuring sufficient recovery time and providing opportunities for reflection on the experiences of primary healthcare personnel. This also includes preparedness for managing the heavy workload and strained energy levels of healthcare workers in the aftermath of a crisis. © Author(s) (or their employer(s)) 2024.

Background: Primary healthcare, the first line of care in many countries, treats patients with diverse health problems. High workload, time pressure, poor job control and negative interpersonal experiences with supervisors have been documented in primary healthcare. The work environment in primary healthcare is also affected by several types of changes. Aim: We aimed to explore the levels of job satisfaction, turnover intention, social support, leadership climate and change fatigue according to physicians in Swedish primary healthcare. We also aimed to identify and characterize physicians exhibiting both high turnover intention and low job satisfaction, i.e., “discontent with current job”. Methods: A cross-sectional survey based on a random sample of physicians working in Swedish primary healthcare. Results: Approximately one-quarter of the respondents were discontented with their current job. Discontent was negatively associated with poor general health and change fatigue among the respondents; social support from colleagues and a favorable leadership climate showed positive associations in terms of reducing the levels of discontent with current job. Conclusions: The findings of this study highlight the association between low levels of job satisfaction and high levels of turnover intention (i.e., discontent with current job) among physicians in primary healthcare. Moreover, these variables exhibited a strong association with physicians’ general health; poor health significantly increased the likelihood of discontent with current job. Our findings also show that experiencing change fatigue is associated with discontent with current job among physicians in primary healthcare. This knowledge can help identify and improve shortcomings within the psychosocial work environment in Swedish primary healthcare. © The Author(s) 2024.

Background: Implementation science in health is an interdisciplinary field with an emphasis on supporting behavior change required when clinicians and other actors implement evidence-based practices within organizational constraints. Behavioral economics has emerged in parallel and works towards developing realistic models of how humans behave and categorizes a wide range of features of choices that can influence behavior. We argue that implementation science can be enhanced by the incorporation of approaches from behavioral economics. Main body First, we provide a general overview of implementation science and ways in which implementation science has been limited to date. Second, we review principles of behavioral economics and describe how concepts from BE have been successfully applied to healthcare including nudges deployed in the electronic health record. For example, de-implementation of low-value prescribing has been supported by changing the default in the electronic health record. We then describe what a behavioral economics lens offers to existing implementation science theories, models and frameworks, including rich and realistic models of human behavior, additional research methods such as pre-mortems and behavioral design, and low-cost and scalable implementation strategies. We argue that insights from behavioral economics can guide the design of implementation strategies and the interpretation of implementation studies. Key objections to incorporating behavioral economics are addressed, including concerns about sustainment and at what level the strategies work. Conclusion: Scholars should consider augmenting implementation science theories, models, and frameworks with relevant insights from behavioral economics. By drawing on these additional insights, implementation scientists have the potential to boost efforts to expand the provision and availability of high quality care. © The Author(s) 2024.

Conducting implementation science research involves critical decision making on the methods for data collection and analysis as well as the study design that would best answer the research question of interest. This chapter reviews several research methods and study designs used in implementation science research. The chapter begins with an overview of qualitative, quantitative and mixed methods for identifying and/or assessing barriers and facilitators to implementing an evidence-based practice. The second part of the chapter looks at different study designs for carrying out studies to evaluate the effectiveness of different strategies to overcome barriers and harness facilitators. © 2024 selection and editorial matter, Per Nilsen; individual chapters, the contributors.

Background: Mental health problems represent a large and growing public health concern. Primary care handles most of the patients with mental health problems, but there are many barriers to detection and treatment in this setting, causing under-recognition and under-treatment of patients. The service delivery model Primary Care Behavioral Health (PCBH) shows promise to manage mental health problems in primary care, but more research is needed regarding its effects on multiple levels. Methods: This project investigates the effectiveness and implementation of a large-scale implementation of PCBH in Region Östergötland, Sweden. The aim is to generate new knowledge concerning the impact of a real-world implementation and use of PCBH in routine primary care. A Pragmatic Stepped-Wedge Cluster Trial will be used: 24 PCBH primary care centres in one region will be compared with 48 standard care centres in three other regions. The model will be implemented sequentially at the PCBH centres according to a staggered timetable. Results will be investigated at patient, staff and organization levels and various forms of data will be collected: (1) local and national registry data; (2) questionnaire data; (3) interview data; and (4) document data. Discussion: This project investigates the effectiveness and implementation of PCBH in routine primary care. The project could result in improved mental health care for the included patients and contribute to the general good for a wider population who have mental health problems. The project’s study design will make it possible to assess many important effects of the PCBH service delivery model at different levels, providing evidence of the effectiveness (or not) of the PCBH model under routine conditions in primary care. The project has the potential to generate clinically meaningful results that can provide a basis for decisions concerning further implementation and use of the model and thus for future development of mental health care provision in primary care. Trial registration: NCT05633940, date of registration: 2021–04-21. © The Author(s) 2024.

Nursing staff turnover is an increasing problem for healthcare globally. In Sweden, the shortage of nurses in primary healthcare has increased significantly in recent years. This development is alarming because primary healthcare, both in Sweden and internationally, is responsible for a large part of healthcare. The aim of this study was to explore working conditions (change fatigue, leadership climate, and social support from colleagues) and characteristics of primary care nurses who are discontent with their current job, i.e., those with high turnover intentions and poor job satisfaction in Sweden. This was a cross-sectional survey of 466 registered nurses working in Swedish primary healthcare. Data were analyzed using descriptive statistics and logistic regression. The results demonstrate that 21.1% of the responding nurses are discontent with their current job and have considered quitting. Being discontent had significant associations with poor leadership climate (p<0.001), lack of social support from colleagues (p<0.001), change fatigue (p<0.001), poor health (p<0.001), and working more than 40 h per week (p=0.02). The results have implications for how healthcare organizations structure the work of nurses in primary healthcare and how they can attract and retain future staff to these workplaces. © 2024 Elin Karlsson et al.

Background: The Danish Health Authority recommended the implementation of new types of emergency departments. Organizational changes in the hospital sector challenged the role, identity, and autonomy of medical specialists. They tend to identify with their specialty, which can challenge successful implementation of change. However, investigations on specialty identity are rare in implementation science, and how the co-existence of different specialty identities influences the implementation of new emergency departments needs to be explored for the development of tailored implementation strategies. The aim of this study was to examine how medical specialty identity influences collaboration between physicians when implementing a new emergency department in Denmark. Methods: Qualitative methods in the form of participants’ observations at 13 oilcloth sessions (a micro-simulation method) were conducted followed up by 53 individual semi-structured interviews with participants from the oilcloth sessions. Out of the 53 interviews, 26 were conducted with specialists. Data from their interviews are included in this study. Data were analysed deductively inspired by Social Identity Theory. Results: The analysis yielded three overarching themes: [1] ongoing creation and re-creation of specialty identity through boundary drawing; [2] social categorization and power relations; and [3] the patient as a boundary object. Conclusions: Specialty identity is an important determinant of collaboration among physicians when implementing a new emergency department. Specialty identity involves social categorization, which entails ongoing creation and re-creation of boundary drawing and exercising of power among the physicians. In some situations, the patient became a positive boundary object, increasing the possibility for a successful collaboration and supporting successful implementation, but direct expressions of boundaries and mistrust were evident. Both were manifested through a dominating power expressed through social categorization in the form of in- and out-groups and in an “us and them” discourse, which created distance and separation among physicians from different specialties. This distancing and separation became a barrier to the implementation of the new emergency department. © 2024, The Author(s).

Background: There is overwhelming evidence for the preventive effects of regular physical activity and healthy eating habits on the risk for developing a non-communicable disease (NCD). Increasing attention has been paid to community-wide approaches in the battle against NCDs. Communities can create supportive policies, modify physical environments, and foster local stakeholder engagement through intersectoral collaboration to encourage communities to support healthy lifestyles. The Pep initiative is based on intersectoral community-wide collaboration among Sweden’s municipalities. Primary targets are municipality professionals who work with children and young people as well as parents of children <18 years. The goal is to spread knowledge and create commitment to children’s and young people’s health with a special focus on physical activity and healthy eating habits to facilitate and support a healthy lifestyle. The overarching aim of the research project described in this study protocol is to investigate factors that influence the implementation of the Pep initiative in Sweden, to inform tailored implementation strategies addressing the needs and local prerequisites of the different municipalities. Methods: The project includes a qualitative and a quantitative study and is framed by a theoretical model involving four complementary forms of knowledge, explicitly recognized in the Pep initiative: knowledge about the issue; knowledge about interventions; knowledge about the context; and knowledge about implementation. Study 1 is a focus group study exploring barriers and facilitators for implementing the Pep initiative. The study will be carried out in six municipalities, selected purposively to provide wide variation in municipality characteristics, including population size and geographical location. Data will be analyzed using thematic analysis. Study 2 is a cross-sectional web-based survey investigating the implementability of the Pep initiative in Sweden’s 290 municipalities. Conditions for implementing different areas of the Pep initiative will be examined in terms of the acceptability, appropriateness, and feasibility, three predictors of implementation success. Data will be analyzed using non-parametric statistics. Discussion: The findings of the two studies will increase understanding of the prerequisites for implementing the Pep initiative in Swedish municipalities, which will provide valuable input into how implementation of the Pep initiative can best be facilitated in the different municipality settings. Copyright © 2024 Leijon, Algotson, Bernhardsson, Ekholm, Ersberg, Höök, Klüft, Müssener, Garås and Nilsen.

Background: Despite the extensive hopes and expectations for value creation resulting from the implementation of artificial intelligence (AI) applications in healthcare, research has predominantly been technology-centric rather than focused on the many changes that are required in clinical practice for the technology to be successfully implemented. The importance of leaders in the successful implementation of innovations in healthcare is well recognised, yet their perspectives on the specific innovation characteristics of AI are still unknown. The aim of this study was therefore to explore the perceptions of leaders in healthcare concerning the innovation characteristics of AI intended to be implemented into their organisation.

Methods: The study had a deductive qualitative design, using constructs from the innovation domain in the Consolidated Framework for Implementation Research (CFIR). Interviews were conducted with 26 leaders in healthcare.

Results: Participants perceived that AI could provide relative advantages when it came to care management, supporting clinical decisions, and the early detection of disease and risk of disease. The development of AI in the organisation itself was perceived as the main current innovation source. The evidence base behind AI technology was questioned, in relation to its transparency, potential quality improvement, and safety risks. Although the participants acknowledged AI to be superior to human action in terms of effectiveness and precision in some situations, they also expressed uncertainty about the adaptability and trialability of AI. Complexities such as the characteristics of the technology, the lack of conceptual consensus about AI, and the need for a variety of implementation strategies to accomplish transformative change in practice were identified, as were uncertainties about the costs involved in AI implementation.

Conclusion: Healthcare leaders not only saw potential in the technology and its use in practice, but also felt that AI’s opacity limits its evidence strength and that complexities in relation to AI itself and its implementation influence its current use in healthcare practice. More research is needed based on actual experiences using AI applications in real-world situations and their impact on clinical practice. New theories, models, and frameworks may need to be developed to meet challenges related to the implementation of AI in healthcare. © 2023, The Author(s).

Implementation science has seen the development of literally hundreds of theoretical approaches in the form of theories, models and frameworks for use by researchers in the field. This chapter provides an overview of the theories, models and frameworks that are available in implementation science, with the aim of describing how these approaches are applied. The chapter presents a taxonomy that distinguishes between different approaches to advance clarity and achieve a common terminology in the field. Knowledge about the theories, models and frameworks facilitates appropriate selection and application of relevant approaches in implementation science studies. © 2024 selection and editorial matter, Per Nilsen; individual chapters, the contributors.

[No abstract available]

Determinant frameworks are used in implementation science to describe and categorize influences (i.e. determinants) on the implementation of evidence-based practices. Determinants are typically divided into barriers (or hindrances) and facilitators (or enablers or drivers) of implementation. Knowledge about implementation determinants is important to develop and select appropriate strategies to overcome barriers and harness facilitators to support the implementation of evidence-based interventions, programmes, services and other practices. This chapter looks at the origins, content and use of six determinant frameworks. © 2024 selection and editorial matter, Per Nilsen; individual chapters, the contributors.

This core textbook introduces the key concepts, theories, models and frameworks used in implementation science, and supports readers applying them in research projects. The first part of the book focuses on the theory of implementation science, providing a discussion of its emergence from the evidence-based practice movement and its connections to related topics such as innovation research. It includes chapters looking at a wide range of theories, methods and frameworks currently used in implementation science, and a chapter focusing on suitable theories that could be imported from other fields. The first part also addresses strategies and outcomes of implementation and discusses how researchers can build causal pathways adapted to their study. The second part of the book focuses squarely on putting the theory of implementation science to work in practice, with chapters discussing research methods used in the field and how to select the most appropriate approach. This section also features several chapters presenting in-depth case studies of specific applications. This multidisciplinary text is an essential resource for graduate students from a range of healthcare backgrounds taking courses on implementation science, as well as researchers from medicine, nursing, public health, allied health, economics, political science, sociology and engineering. © 2024 selection and editorial matter, Per Nilsen; individual chapters, the contributors.

Working in accordance with the evidence model is challenging, which prompted research interest in identifying barriers to implementing evidence-based interventions, programmes, services and other practices to achieve an evidence-based practice. Studies of the determinants of implementation led to research on implementation strategies for overcoming barriers and harnessing facilitators. This chapter provides a historical background to implementation science, focusing on the evidence-based movement but also addressing other research that has influenced implementation science, including research on the spread of innovations, the utilization of research-based knowledge in healthcare practice and policy implementation. © 2024 selection and editorial matter, Per Nilsen; individual chapters, the contributors.

Theories and concepts from other fields than implementation science can contribute to improved understanding of the challenges of implementing evidence-based interventions, programmes, services and various practices. Implementation science could benefit from a broader dialogue with a variety of theoretical and conceptual orientations. This chapter provides a select overview of potentially relevant theories and concepts that relate to the individual level (e.g. social-cognitive theories and dual process theories), social group level (e.g. leadership and culture) and organization level (e.g. organizational learning and complexity theory). Key features and principles of theories and concepts from miscellaneous fields are described and their relevance for potential use in implementation science is discussed. © 2024 selection and editorial matter, Per Nilsen; individual chapters, the contributors.

Implementation strategies have been defined as methods or techniques that are used to enhance the adoption, implementation and sustainability of evidence-based practices in healthcare and other settings. An overview of some taxonomies that categorize different types of implementation strategies is provided. The chapter also deals with the matching of determinants with the appropriate strategies, which represents a considerable challenge in implementation science. Some tools and methods to facilitate this matching process are described. Implementation outcomes, defined as the effects of deliberate and purposive actions to implement evidence-based practices, are also addressed. They are distinct from patient or population outcomes. Two commonly used implementation outcome taxonomies are described. © 2024 selection and editorial matter, Per Nilsen; individual chapters, the contributors.

[No abstract available]

This chapter addresses process models that are used to describe the process of translating research into routine practice and/or provide guidance to achieve a successful implementation process. Two types of process models are distinguished. Precursor-type process models focus primarily on understanding problems associated with the current way of working, identifying evidence-based practices to address these problems, understanding barriers and facilitators to implement these practices and selecting implementation strategies to address these barriers and facilitators. Planning-type process models assume that all this work has already been completed and focus on how to implement the practice in different settings. © 2024 selection and editorial matter, Per Nilsen; individual chapters, the contributors.

Introduction: Artificial intelligence (AI) is widely seen as critical for tackling fundamental challenges faced by health systems. However, research is scant on the factors that influence the implementation and routine use of AI in healthcare, how AI may interact with the context in which it is implemented, and how it can contribute to wider health system goals. We propose that AI development can benefit from knowledge generated in four scientific fields: intervention, innovation, implementation and improvement sciences.

Aim: The aim of this paper is to briefly describe the four fields and to identify potentially relevant knowledge from these fields that can be utilized for understanding and/or facilitating the use of AI in healthcare. The paper is based on the authors' experience and expertise in intervention, innovation, implementation, and improvement sciences, and a selective literature review.

Utilizing knowledge from the four fields: The four fields have generated a wealth of often-overlapping knowledge, some of which we propose has considerable relevance for understanding and/or facilitating the use of AI in healthcare.

Conclusion: Knowledge derived from intervention, innovation, implementation, and improvement sciences provides a head start for research on the use of AI in healthcare, yet the extent to which this knowledge can be repurposed in AI studies cannot be taken for granted. Thus, when taking advantage of insights in the four fields, it is important to also be explorative and use inductive research approaches to generate knowledge that can contribute toward realizing the potential of AI in healthcare. © 2022 Nilsen, Reed, Nair, Savage, Macrae, Barlow, Svedberg, Larsson, Lundgren and Nygren. 

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

Background: Artificial intelligence (AI) has the potential in health care to transform patient care and administrative processes, yet health care has been slow to adopt AI due to many types of barriers. Implementation science has shown the importance of structured implementation processes to overcome implementation barriers. However, there is a lack of knowledge and tools to guide such processes when implementing AI-based applications in health care.

Objective: The aim of this protocol is to describe the development, testing, and evaluation of a framework, “Artificial Intelligence-Quality Implementation Framework” (AI-QIF), intended to guide decisions and activities related to the implementation of various AI-based applications in health care.

Methods: The paper outlines the development of an AI implementation framework for broad use in health care based on the Quality Implementation Framework (QIF). QIF is a process model developed in implementation science. The model guides the user to consider implementation-related issues in a step-by-step design and plan and perform activities that support implementation. This framework was chosen for its adaptability, usability, broad scope, and detailed guidance concerning important activities and considerations for successful implementation. The development will proceed in 5 phases with primarily qualitative methods being used. The process starts with phase I, in which an AI-adapted version of QIF is created (AI-QIF). Phase II will produce a digital mockup of the AI-QIF. Phase III will involve the development of a prototype of the AI-QIF with an intuitive user interface. Phase IV is dedicated to usability testing of the prototype in health care environments. Phase V will focus on evaluating the usability and effectiveness of the AI-QIF. Cocreation is a guiding principle for the project and is an important aspect in 4 of the 5 development phases. The cocreation process will enable the use of both on research-based and practice-based knowledge.

Results: The project is being conducted within the frame of a larger research program, with the overall objective of developing theoretically and empirically informed frameworks to support AI implementation in routine health care. The program was launched in 2021 and has carried out numerous research activities. The development of AI-QIF as a tool to guide the implementation of AI-based applications in health care will draw on knowledge and experience acquired from these activities. The framework is being developed over 2 years, from January 2023 to December 2024. It is under continuous development and refinement.

Conclusions: The development of the AI implementation framework, AI-QIF, described in this study protocol aims to facilitate the implementation of AI-based applications in health care based on the premise that implementation processes benefit from being well-prepared and structured. The framework will be coproduced to enhance its relevance, validity, usefulness, and potential value for application in practice. © 2023 The Author(s).

Background: Artificial intelligence (AI) for healthcare presents potential solutions to some of the challenges faced by health systems around the world. However, it is well established in implementation and innovation research that novel technologies are often resisted by healthcare leaders, which contributes to their slow and variable uptake. Although research on various stakeholders’ perspectives on AI implementation has been undertaken, very few studies have investigated leaders’ perspectives on the issue of AI implementation in healthcare. It is essential to understand the perspectives of healthcare leaders, because they have a key role in the implementation process of new technologies in healthcare. The aim of this study was to explore challenges perceived by leaders in a regional Swedish healthcare setting concerning the implementation of AI in healthcare.

Methods: The study takes an explorative qualitative approach. Individual, semi-structured interviews were conducted from October 2020 to May 2021 with 26 healthcare leaders. The analysis was performed using qualitative content analysis, with an inductive approach.

Results: The analysis yielded three categories, representing three types of challenge perceived to be linked with the implementation of AI in healthcare: 1) Conditions external to the healthcare system; 2) Capacity for strategic change management; 3) Transformation of healthcare professions and healthcare practice.

Conclusions: In conclusion, healthcare leaders highlighted several implementation challenges in relation to AI within and beyond the healthcare system in general and their organisations in particular. The challenges comprised conditions external to the healthcare system, internal capacity for strategic change management, along with transformation of healthcare professions and healthcare practice. The results point to the need to develop implementation strategies across healthcare organisations to address challenges to AI-specific capacity building. Laws and policies are needed to regulate the design and execution of effective AI implementation strategies. There is a need to invest time and resources in implementation processes, with collaboration across healthcare, county councils, and industry partnerships. © The Author(s) 2022.

Vi kommer att presentera ett nytt forskningsprojekt vid Högskolan i Halmstad med finansiering från Vetenskapsrådet, som förväntas bidra med kunskap om hur arbetets gränser i hälso- och sjukvården förändras vid implementering av artificiell intelligens (AI). Hälso- och sjukvården i Sverige brottas idag med utmaningar kring att klara av att fördela resurser där de gör mest nytta, säkerställa kvalitet i den vård som ges och att ställa om till en mer digitaliserad vård som sker i mer samproduktion mellan vårdpersonal och patienter. Ett teknikområde som förväntas kunna bidra till att lösa dessa utmaningar är AI, men forskning har visat att det finns många hinder för att lyckas med att införa och använda AI-applikationer inom hälso- och sjukvården. Hälso- och sjukvårdspersonal har en viktig roll att spela i förändringsarbete inom vården och AI-applikationer kan komma att konkurrera med det monopol på kunskap i förhållande till hälsa och behandling av sjukdomar som vårdpersonalen erhållit genom lång akademisk utbildning, träning och praktisk erfarenhet. Det övergripande syftet med forskningsprojektet ImpAI är att generera ny kunskap om implementering och användning av AI-applikationer i rutinsjukvård och hur professionella roller kan fungera som barriärer under implementeringsprocessen. Det teoretiska ramverket består av professionsteori med fokus på tillit och arbetets gränser samt implementeringsteori. Projektet bygger på olika case i form av AI-applikationer som implementeras under 2023–2024 i Region Halland, Sverige och mixad metod används vid processutvärderingen av dessa case. Resultatet kommer både att främja förståelsen för hur processer kan etableras vid införande av AI applikationer i hälso- och sjukvården och bidra med information om hur sådana processer kan bygga på hälso- och sjukvårdspersonalens kompetens och roller.

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.

Theories have been developed by researchers in implementation science to achieve enhanced understanding and explanation of how or why implementation succeeds or fails. These theories specify the causal mechanisms of change. This chapter looks at the concept of theory and examines how implementation science-specific theories are defined. The chapter also clarifies how theories used in implementation science can be distinguished from models and frameworks in the field. Three implementation theories that are used in implementation science are described: Normalization Process Theory (NPT), Organizational Readiness for Change (ORC) and Capability Opportunity Motivation - Behaviour (COM-B). © 2024 selection and editorial matter, Per Nilsen; individual chapters, the contributors.